# Handling Open Research Data within the Max Planck Society – Looking Closer
at the Year 2020

Martin Boosen https://orcid.org/0009-0009-3989-5087,
Michael Franke https://orcid.org/0000-0002-2661-8242,
Yves Vincent Grossmann https://orcid.org/0000-0002-2880-8947,
Sy Dat Ho https://orcid.org/0000-0002-6218-4146,
Larissa Leiminger https://orcid.org/0000-0002-6491-3197, and
Jan Matthiesen https://orcid.org/0000-0001-6548-3654
Max Planck Digital Library https://ror.org/0061msm67
<EMAIL_ADDRESS>
(February 2024)

This paper analyses the practice of publishing research data within the Max
Planck Society in the year 2020. The central finding of the study is that up
to 40% of the empirical text publications had research data available. The
aggregation of the available data is predominantly analysed. There are
differences between the sections of the Max Planck Society but they are not as
great as one might expect. In the case of the journals, it is also apparent
that a data policy can increase the availability of data related to textual
publications. Finally, we found that the statement on data availability ”upon
(reasonable) request” does not work.

###### Contents

  1. 1 Introduction
  2. 2 Observations on Sharing Research Data
    1. 2.1 Discussion on Sharing Research Data before 2016
    2. 2.2 Understanding Data Sharing Practices after Announcing the FAIR Data Principles in 2016
    3. 2.3 Data Availability Statements: Make Data ”Accessible” on Reasonable Request
    4. 2.4 Open Research Data Policies in 2020
    5. 2.5 Perspective of Third-Party Funders on Open Research Data
  3. 3 Open Research Data in the Max Planck Society
    1. 3.1 Good Scientific Practices within the Max Planck Society
    2. 3.2 Max Planck Services for Open Research Data
    3. 3.3 Max Planck Lighthouse Projects for Open Research Data
  4. 4 Publications by the Max Planck Society in the Year 2020
    1. 4.1 Method
    2. 4.2 Approach
    3. 4.3 Descriptive Analysis
    4. 4.4 Selected Publications
    5. 4.5 Results
    6. 4.6 Data Licenses
    7. 4.7 Data Repositories
    8. 4.8 Data “Available” Upon Request
    9. 4.9 Research Software in Publications
  5. 5 Discussion
  6. 6 Perspectives
  7. 7 Abbreviations
  8. 8 Statements and Comments
    1. 8.1 Data and Code Availability Statement
    2. 8.2 Author contributions

##  1 Introduction

Data sharing is becoming a new standard in research. Data accessibility is
increasingly being discussed as an important aspect of scientific
reproducibility. Funding agencies and public authorities are increasingly
demanding that their money be used to make research data as openly accessible
as possible. Many efforts are being made to ensure that the conscious handling
of research data becomes an established practice in everyday scientific life.
However, there is sometimes a gap between aspiration and reality. The reasons
for this are to be found on the side of the scientists, the publishers, and on
the side of the infrastructure providers. Although there is often some kind of
agreement – voluntary or involuntary – to share data. But it is still
remarkably rare. To change this, the aim would be to take the pain out of data
sharing.111See for example the recently published overview [43]. This and
other debates around data sharing are discussed with reference to the Max
Planck Society in the following chapters.

As already mentioned data sharing is perceived as an important element of
intra-scientific exchange. At the same time, the understanding of data sharing
is hampered by different interpretations of the pair ”data” and ”sharing”.
Focusing on research data from the perspective of the Max Planck Society as
well as on its practices this paper acknowledges two ways of understanding
this term. On the one hand, research data is the basis for scientific findings
and their publication.222See the old ”Rules of Good Scientific Practice” of
the Max Planck Society from 2000 resp. 2009, [55, p. 4]. On the other hand,
published research data is increasingly emerging as a genre of scholarly
output in its own right. The two come together again when we ask about the
accessibility of the data underlying published texts.

The two sides of the research data coin – wanting to commit to data sharing,
but failing to do so – are the focus of the first section. A general spectrum
of data sharing is briefly outlined. The main emphasis is on developments
since 2010. Here, the positions and initiatives involved in bringing data
accessibility more into focus are traced. The second section examines the
perspective of the Max Planck Society by presenting the open data and sharing
practices of the decade of the 2010s. The main aim is to show which
institutional frameworks have been established and which intentional vagueness
(in the sense of a desired added value) has been maintained. The third section
takes a closer look at the Max Planck Society and shows what its publication
behaviour looks like in 2020 in terms of research data. This evaluation will
take up the most space, as it formulates the data collection and the
descriptive analysis of the results. The paper concludes with an outlook on
the decade of the 2020s and possible developments in data sharing for the Max
Planck Society.

##  2 Observations on Sharing Research Data

Data sharing is not a phenomenon of the 21st century. Sharing knowledge has
long been an ingrained cultural practice of mankind. At the same time, data
sharing seems to be on the rise since the beginning of the third millennium,
not least because of the possibilities digitalization brings. This development
is new because it has given rise to a new genre of publication called ”data”
or ”dataset”. This data sharing is accompanied by several phenomena.

First and foremost, the open exchange of data is becoming increasingly
important in this context. Open means barrier-free access to the data. This is
usually done by attaching the data to the textual publication or by publishing
the data as a separate dataset in a repository. Especially in the latter case,
there are already signs that data publishing is becoming a genre in its own
right. This goes hand in hand with increased visibility of research data. They
can gain visibility through open or restricted access options. Nevertheless,
they are accessible, or at least there is knowledge of their existence.
Therefore, especially for research data with restricted access, it is
important that the metadata, i.e. the descriptive information about the data,
is freely available. This makes it much easier and faster for other
researchers to learn of the existence of the research data and then request
access. This is adding to the recognition as an entity in their own right.

Regardless of this, data can also be reused in ways that were not foreseen by
the original authors. The more conscious use of data licences facilitates such
reuse and reinterpretation. Particularly in a scientific context, future
questions cannot always be guessed at from current perspectives. That is why
it makes sense to present research data in such a way that new questions can
be discussed in the future. Parallel to software, licensing is increasingly
becoming a familiar process. This creates legal certainty, which makes access
easier – in a legal sense. In many cases, this increase in visibility is
accompanied by an increase in citations by other scientific results. It is
therefore not surprising that the bibliometric study by Colavizza and
colleagues found that there is ”a citation advantage, of up to 25.36% (±
1.07%), with articles that have a [..] link to a repository via a URL or other
permanent identifier”.333[7, p. 14].

For the phenomena of sharing data openly, we see – in view of Germany – a
strong acceleration of discussion contributions in the 2010s, but an
increasing acceptance of the forms of data publication. For this reason we
present a chronology for the recent past. An important milestone and turning
point are the FAIR Data Principles444[77]., which were published in 2016. Not
only do they cover the decade of the 2010s, but they also provide explicit
guidance on the publication of research data. At the same time, for the first
time, the FAIR Principles provide a simple and concise way of articulating –
FAIR – what has often been expressed in more descriptive terms when dealing
with research data.555Due to the abundance of introductions, handouts and
presentations, an explanation of the FAIR principles will not be given here.
For the German context, however, the following is a suitable overview see
[51]. Such a German frame of reference is naturally integrated into European
and worldwide developments. Research is mostly international. Nevertheless, in
the area of normative discussions, the Max Planck Society mainly operates in
Germany, so this is the primary focus of this text.

To reflect these developments, the following chapter discusses the evolution
prior to the FAIR Data Principles 2016. This is followed in chapter 2.1 by a
description of the FAIR acceleration phase from 2016 to the present. These two
parts serve as a derivation to the concrete study year 2020. We will then
focus in chapter 2.2 on specific aspects. They become particularly relevant
again in the following chapter, i.e. the empirical analysis of publication
behaviour. First, the text focuses on the particular aspect of data
availability statements to illustrate how the approach to data accessibility
has changed. However, this would be difficult to explain without the
institutional framework. Therefore, the following two subsections discuss
developments in institutional data policies in 2.4 and funders 2.5 to conclude
the general perspective on research data sharing. Finally, this chapter also
serves as a general introduction and transition in terms of content and timing
for the next parts of the text.

###  2.1 Discussion on Sharing Research Data before 2016

Looking back, it is often difficult to pinpoint the exact start of a
discussion. Therefore, temporal categorisations often have something
artificial or arbitrary about them. The same is true here. Nevertheless, the
dilemma remains that start and end points must be specified. One of the first
publications with specific recommendations worldwide on research data was a
53-page publication by the OECD in 2007.666[62]. Particularly worth reading
are Principles A to M, which attempt to define concrete guidelines for the
design of data access. In particular, the aim was to develop a set of
guidelines, based on commonly agreed principles, to facilitate cost-effective
access to publicly funded digital research data.777[62, p. 3]. The sequencing
of human DNA – successfully completed in 2003 – was used to study many
biological processes, so open sharing of research data was already implicit in
the project.

In the German context, the first significant contribution to the discourse can
be seen in 2009. One of the DFG’s first decentralised recommendations for
action on research data was the ”Empfehlungen zur gesicherten Aufbewahrung und
Bereitstellung digitaler Forschungsprimardaten” of January 2009.888[3]. An
English translation of this would be ”Recommendations for the Secure
Preservation and Provision of Digital Research Primary Data”. This was shortly
followed by the Alliance of German Science Organisations’ ”Principles for the
Handling of Research Data” in 2010.999[1]. Both recommendations were intended
to set initial standards for the concrete handling of research data.

The ”Kommission zur Zukunft der Informationsinfrastruktur”101010A translation
of this may be ”Commission on the Future of the Information Infrastructure.,
the first joint institution with the focus on research data in the Federal
Republic of Germany was established in 2009 on the recommendation of the
Gemeinsame Wissenschaftskonferenz (GWK). This commission, with the
participation of many German scientific organisations and institutions, also
had its own working group on research data.111111[48, line 490-506]. This was
followed in 2012 by recommendations from the German Science and Humanities
Council (WR), which called for the professional communities or actors in
interdisciplinary research fields to develop quality criteria for the
generation of research data and guidelines for appropriate data management,
where these do not already exist.121212[78, p. 56]. As an interim result, it
was noted in 2015 that the field of research data is a very dynamic and
diverse field.131313[28, p. 2].

In addition to these recommendations for action by institutional stakeholders,
research and education institutions have been issuing their own data policies
since the beginning of the 2010s. Based on the recommendations mentioned
above, it can be observed for the German context that the number of data
policies and universities and research institutions has increased
significantly since 2014.141414The best overview of research data policies in
the German-speaking world is provided by
www.forschungsdaten.org/index.php/data_policies. It is worth mentioning the
recommendations of the German Rectors’ Conference (Hochschulrektorenkonferenz)
of 2014, [39]. The two-year period between the WR recommendation and the first
policies can be reasonably explained by an internal development and
establishment process. The introduction of a data policy in an institution
simply requires different resources and time. Already in 2011, the German
debate on data policy is characterised by opposite poles of recommendation –
as recommendations in the hope that they will be followed out of conviction –
and obligation – so real commitments.151515For the German context, see in
particular [63]. This also fits in well with the fact that in Horizon 2020
(running from 2014 to 2020) the European Commission has for the first time
called for a conscious handling of data, for example through data management
plans (with the possibility to opt-out).161616[24]. In retrospect, the long-
term development towards obligatory requirements regarding research data at
Horizon Europe is already indicated here.

With these developments towards active management of research data, the
positive aspects of data sharing and citation are being observed. Publishers
have also begun to adopt data policies and request research data associated
with submitted articles.171717An example would be PLOS Biology, with the
intention of increasing data availability and transparency, [4]. In this
context, we are beginning to see how a change in publishing behaviour –
towards data sharing – is affecting the behaviour of scientists. For example,
for some supporters, the willingness to share research data is linked to the
strength of the evidence and the quality of reporting of statistical
results.181818[76].

Some evaluations have shown that the behaviour of scientists is moving towards
open sharing of research data. ”However, there is increased perceived risk
associated with data sharing, and specific barriers to data sharing
persist.”191919[72, p. 1]. It is interesting to note that the scientific
communities have given themselves their own guidelines or recommendations on
how best to deal with research data in their environment.202020See for example
[32]. For some aspects, this development can be repeated as well at a general
level, towards universal principles for the handling of (research) data.

###  2.2 Understanding Data Sharing Practices after Announcing the FAIR Data
Principles in 2016

The publication of the FAIR Data Principles212121[77]. in 2016 can, in
retrospect, be seen as an important point on the way to an evolving awareness
of data sharing. Two things in particular were central to this. First, there
was broad acceptance of the principles. And secondly, it was now possible to
give a name to both the process and the outcome, namely FAIR. This naming, as
an explicit accumulation of knowledge, should not be underestimated in the
success of the FAIR Principles.222222A general overview of the introduction of
the FAIR Data Principles in the first years can be found in [74]. At the same
time, this already contains an existing criticism of the FAIR Data Principles,
namely that they are often mentioned performatively, but the concrete
application sometimes leaves much to be desired.

The FAIR Data Principles were adopted quite rapidly by the European Commission
in 2016 and documented in the ”Guidelines on FAIR Data Management in Horizon
2020”; 232323[19]. See also later the report on turning FAIR into reality from
2018, [21]. However, it did not stop at the introduction of guidelines.
Already in 2017, the Open Research Data Pilot covered all thematic areas of
Horizon 2020, which ”aims to improve and maximise access to and re-use of
research data generated by Horizon 2020 projects”.242424[20]. The aim was to
create incentives to convince scientists to organise their data according to
the FAIR Data Principles.

Two years later, the European Commission produced an analysis of the cost of
FAIR research data and the lack of application of the FAIR data principles.
According to this document, ”at €10.2bn per year in Europe, the measurable
cost of not having FAIR research data makes an overwhelming case in favour of
the implementation of the FAIR principles.”252525[22]. From this perspective,
the opportunity cost of not having the FAIR Data Principles was high. Beyond
the EU as a major initiator, there have been many other developments towards
the concrete application of the FAIR Data Principles.

According to a survey conducted by the European Commission in 2022,
recognition of research data management and sharing is already partly present,
but could be significantly higher, according to the participants.262626See in
particular [61, pp. 552-553] with a geographical focus on Germany and similar
discussions is particularly interesting in this context. There are different
approaches to encourage different actors to structure their data according to
the FAIR Data Principles and, where possible, to make them publicly
accessible. There are explicit commitments, e.g. through research data
policies of institutions, deliverables from funders or demanded data
availability statements by journal publisher. At the same time there are
incentives to promote the conscious use of data, e.g. a data index272727[40].
as a bibliometric measure, to help the independent genre of ’data publication’
gain more recognition.282828The development of data journals are an example of
this. There are many initiatives, ideas and new ways of doing things in the
scientific communities that try to facilitate and promote the application of
the FAIR Data Principles. However, it is difficult to generalise such
developments in a meaningful way by subject or other criteria. Because ”data
sharing perceptions and practices are highly variable among academic
disciplines.” 292929[65, p. 238]. In this whole discussion about the
implementation of the FAIR Data Principles, however, it is striking that there
is comparatively little explicit criticism of them.

Nevertheless, the genre of analysis of research data sharing behaviour has
become common.303030See also, for example, the meta-analysis of various
studies [15]. There are two broad categories into which this can be grouped.
The first category is working on data sharing behaviour within a research
field. Such publications are particularly concerned with understanding how
data is handled within a discipline.313131See for example [6], [8], [41],
[47], [50], [52], [69] and more. For example, to what extent is open sharing
of research data a quasi-standard there? Or are there good reasons for
restricting access to data? Are the FAIR data principles widely applied? How
is the reproducibility of research data valued within the scientific
community? And where is research data published? The overall aim of these
analyses is to understand what patterns of behaviour currently exist and
evolve. These questions are often compared to a desired state. At the same
time, there are also comparisons between different disciplines, as well as
cross-disciplinary analyses.323232See for example [17], [25], [29], [71], [73]
and more. This list could be extended. However, it should be clear that such
analyses are being carried out and are available.

The second category of analyses is primarily interested in data publication
from an institutional perspective. This perspective is mostly taken by
infrastructure providers such as libraries and data centres. It focuses on the
question of where data should be published by researchers affiliated to their
own institution.333333Examples of such studies are [5], [66], [68], [30] and
more. In addition to the collection of bibliometric statistics, this is a
motivation for the continuous improvement of institutional research data
services. In the German context, the Charité Dashboard on Responsible
Research343434https://quest-dashboard.charite.de. See also [44]. is
particularly worthy of mention. There, the institution’s open data and code
publications can be viewed in quasi-live mode. Most of these studies of data
sharing behaviour focus on the issue of restricting access without
justification. It is interesting to emphasise here that in many cases after
2016, the main focus was on the concrete implementation of the FAIR Data
Principles.

###  2.3 Data Availability Statements: Make Data ”Accessible” on Reasonable
Request

”All that glisters is not gold.”353535William Shakespeare (1564–1616),
Merchant of Venice, act II, scene 7. The same sometimes applies for research
data. In theory, reproducibility is part of good research. But in reality,
reproducibility of data-based findings is sometimes difficult or almost
impossible. The stumbling block is often a data availability statement, which
publishers now routinely require for scientific publications. The aim is to
document where the data, on which the hypotheses of the publication are based,
is available. The proportion of data availability statements varies. In some
cases, the coverage of articles is quite high, depending on the discipline and
on the journals’ data policies. For example, in 2018, 93.7% of 21,793 PLOS
articles and 88.2% of 31,956 BMC articles had data availability
statements.363636[7, p. 7].

Unfortunately, the existence of an availability statement says nothing about
its content. Often, it is stated the data is ”available upon reasonable
request”. In one reasoned study with N=1792 statements about ”93% authors
either did not respond or declined to share their data” after a
request.373737[29]. As a comparison of preprints with their published versions
has shown, that ”data availability statements […] are a good first step, but
are insufficient to ensure data availability”.383838[58, p. 2]. To be clear,
there is a lot of frustration by getting research data with these statements
of data availability that end up coming to nothing.

As already indicated, data sharing practices and the availability of data on
request vary between scientific disciplines.393939See a meta-study such as
[71]. On the one hand, it is quite a challenge to work out the different
disciplinary cultures in order to compare these different frames of reference.
On the other hand, it is also unsatisfactory to postulate that everything is
complicated without showing a higher level of detail. Ultimately, however,
there are many different reasons why data sharing does not take
place.404040See generally [31]. With a view to the Max Planck Society and the
year 2020, we will take a closer look at some of these reasons in chapter 4.8.

###  2.4 Open Research Data Policies in 2020

Open sharing of research data is not the norm and, unlike the application of
the FAIR principles, it is not always appropriate in all areas. At the same
time, many European scientific institutions have policies that provide legal
certainty for the free sharing of research data.414141But it should be clear
that this is not a purely European phenomenon. Similar issues are being
debated in other regions and countries. See for example the review of open
research data policies in China [80]. This issue is also increasingly being
taken up by publishers, so that more and more data policies are being
established.424242See for example the overview [42]. For a general overview of
research data policies in journals in the 2010s and their evolution, see in
particular [9]. Research organisations, such as the German Helmholtz
Association and the French Institut Pasteur, have issued guidelines on the
management of research data.434343[36] and [45]. What is generally striking
about these processes towards a data policy is that they require a lot of time
and energy from all stakeholders. Internal coordination and the development of
a common understanding are resource-intensive. Funders are well aware of this.
The Swiss National Science Foundation, for example, evaluated the introduction
of its Open Data Policy in 2020 and calculated the associated funding
expenditure.444444[59, p. 4-5].

For the Max Planck Society, it should be noted that there is no general
research data policy for 2020. Individual institutes and departments have
dealt with this issue in different ways. Based on the Harnack Principle,
however, quite different solutions have emerged locally in the institutes and
departments.454545[49]. One example is the internal guideline on the handling
of research data at the Max Planck Institute for the Study of Collective
Goods. It was made mandatory for all employees in 2018. 464646[53]. It defines
research data, regulates ownership, documents local data management and agrees
on the implementation of the regulations. Another example of a local policy is
the Biomaterials Department at the Max Planck Institute of Colloids and
Interfaces.474747[70]. This defines the procedures for handling samples in a
departmental data policy.

At the same time, it should not be hidden that in many cases there are no
explicit procedures, recommendations, etc. at the Max Planck Institutes. This
can be seen as a sign that the existing workflows with research data are
already carried out at a high level. At the same time, it should be noted that
there is often an implicit procedure for research data. This does not always
lead to a common approach to research data management and sometimes causes
local problems.

###  2.5 Perspective of Third-Party Funders on Open Research Data

In addition to this intrinsic motivation, there is also the perspective of
funders on data sharing. There is a clear tendency to make open access to
research data a condition of grant approval. This applies not only to
European, but to national funders. For the Max Planck Society, both the
European Commission and the German Research Foundation were particularly
important in 2020, each accounting for nearly a third of approved external
funding.484848[56, p. 45]

German federal administration/federal state EU DFG Other
$21\%$$31.5\%$$31.2\%$$16.3\%$ Figure 1: Distribution of third-party funds of
the Max Planck Society in 2020

Research data has become increasingly important to the European Commission
since the 2010s. Horizon 2020 explicitly includes research data and, in
particular, data management plans.494949For a good overview of these
developments with static evaluations, see especially [23, p. 49-63]. At that
time, a data management plan (DMP) was not yet mandatory. At the same time,
the availability of data management plans led to increased writing of such. In
a highly competitive environment, it was no longer always possible to opt-out
this element in relation to competitors. The already mentioned Open Research
Data Pilot provided an opportunity for the Commission to promote the openness
of research data.505050See for example [24]. In addition, the Commission has
repeatedly tried to point to new developments. Only two years after the
publication of the FAIR Data Principles, an attempt was made to put them into
practice by releasing concrete financial resources, not just statements of
intent.515151[21].

With the European Open Science Cloud (EOSC), the Commission tried early on to
establish both a funded infrastructure for research data.525252For a good and
concise summary of the history of the EOSC, see in particular [67, p.13]. For
a perspective on the EOSC for Max Planck scientists see also [35]. Open
research data played a special role. On the part of the Max Planck Society,
the MPCDF was particularly involved, especially at EOSCpilot with its own
science demonstrators.

Compared to the European Commission, the DFG’s developments in open research
data up to 2020 are rather modest. This is primarily due to the self-governing
nature of German science. Structurally, this is preceded by discussions within
the discipline until, for example, a recommendation from a DFG commission is
adopted. An early example of this is the 2015 Guidelines in the context of
biodiversity research.535353[10]. In retrospect, the emergence of the German
National Research Data Infrastructure (NFDI) at the national level is probably
the point at which research data management gained importance in Germany, and
in the DFG in particular. The first call for NFDI consortia was launched in
2019. In 2020, the first consortia were named and the second call was
published.545454For statistical evaluations of these two rounds, see in
particular [12] and [13]. For discussions within the scientific community, the
developments around the NFDI on Open Research Data can hardly be
overestimated.555555For an evaluation of the first two NFDI calls with a focus
on the participation of the Max Planck Society, see [33].

##  3 Open Research Data in the Max Planck Society

Open research data has been of declared importance within the Max Planck
Society since the beginning of the millennium. The Berlin Declaration of 2003
also refers specifically to data in its definition of Open Access
contributions.565656”Open access contributions include original scientific
research results, raw data and metadata, source materials, digital
representations of pictorial and graphical materials and scholarly multimedia
material.” [54]. In 2020, however, the Max Planck Society did not have a
general data policy or other recommendations for dealing with research data.
At the same time, this does not mean that research data, and in particular
open access to them, are not important to Max Planck scientists. An analysis
by the Max Planck PhDnet Open Science Group, for example, highlights the
potential of open research data for early career researchers.575757[75, p. 3].
Such discussions within the Max Planck Society are always embedded in the
normative framework provided by the rules of good scientific practice.

###  3.1 Good Scientific Practices within the Max Planck Society

In 2000, the Senate of the Max Planck Society adopted new rules to ensure good
scientific practice.585858[55]. These were amended in 2009 and replaced by new
rules in 2022 without widespread communication.595959[57]. It is therefore
important to bear in mind that the 2000 and 2009 regulations still applied in
the year 2020. But it was already clear in 2020 that the regulations would
change. This was initiated by the DFG and the adoption of the Guidelines for
Safeguarding Good Research Practice, which became binding for applicant
institutions such as the Max Planck Society.606060[11]. The situation in 2020
was therefore characterised by an impending change in the normative framework
for research data within the Max Planck Society.616161For an Max Planck
internal perspective on this situation see especially [27].

In the year 2020 Max Planck Rules for Safeguarding Good Scientific Practice,
research data (in German ”Forschungsdaten”) were not yet referred to by this
term. Rather, it was usually referred to as data or primary data.
Nevertheless, the data had to be processed according to discipline-specific
rules. Primary data had to be kept for ten years. It was also necessary to
ensure that clear and comprehensible documentation was given, for example in
laboratory notebooks. Access to the data had to be guaranteed for authorised
interested parties.626262[55, p. 2 and p. 4]. However, there was already open
research data. Various services for open research data are already existing in
2020.

###  3.2 Max Planck Services for Open Research Data

The central infrastructure and service units of the Max Planck Society have
been offering open research data services for some time. Some services for
open research data were therefore already available within the Society in
2020. Three services are briefly presented here as examples to document an
impression at this point in time.

In summer 2020, the MPCDF launched a CKAN instance for Pandora. This is a data
repository for open archaeological data for the Max Planck Institute for the
Science of Human History636363The former Max Planck Institute for the Science
of Human History was renamed the Max Planck Institute for Geoanthropology in
2022. and its collaborators. The repository has since supported the Pandora
initiative to make historical and archaeological data more accessible and
discoverable.

Since 2014, the Max Planck Digital Library offers Edmond, a repository for
open research data. Here, Max Planck scientists and their collaborators could
freely publish their data. A total of 84 datasets were published through this
service in 2020.646464https://s.gwdg.de/G14Bgw.

With GRO.data, the GWDG offers a service comparable to the Göttingen eResearch
Alliance. Here, open research data can be made available via a repository,
too. This service, as well as advice on open research data, was one of the
services provided by the GWDG to Max Planck researchers in 2020.

With the World Data Climate Center, the German Climate Computing Centre (DKRZ)
offers a community-specific data repository. Research data can also be
published there as Open Research Data. The Max Planck Institute for Chemistry,
for example, made use of this service in 2020 quite far.656565See also [79, p.
259-260].

###  3.3 Max Planck Lighthouse Projects for Open Research Data

In 2020, the Max Planck Society had a number of Open Research Data projects
that have developed quite successfully.

An illustrative example is the FACES666666See https://faces.mpdl.mpg.de. For
more information, see the [16]. platform of the Max Planck Institute for Human
Development. The website, developed in 2009 in collaboration with the Max
Planck Digital Library (MPDL), offers a collection of high quality images of
human faces, grouped by age group, gender and a set of six different facial
expressions (emotions).
Strictly speaking, this data is not entirely freely available: use of most of
the data is restricted to scientific purposes and requires registration/login.
However, this unique set of data has proven to be extremely fruitful, with a
steady stream of diverse scientific publications over more than a
decade.676767Scopus metrics show a steadily growing annual citation rate for
FACES since 2010 until today, with a total of more than 700 citations.

Another very successful project that has been running for a number of years is
Movebank686868https://www.movebank.org., which is run by the Max Planck
Institute for Animal Behaviour together with various partners around the
world. This platform collects and processes current and historical animal
movement data from various sources around the world. As most of the data is
freely available, it can be used in a variety of ways.
With a project start in 2007, Movebank has been around for a long time - with
continuous growth and development of the platform. The number of annual
publications that can be traced back to Movebank data has also remained high:
many hundreds of publications in
total.696969https://www.movebank.org/cms/movebank-content/literature.

Max Planck’s Fritz Haber Institute is heavily involved in the NOMAD Repository
& Archive707070https://nomad-lab.eu/services/repo-arch.. Maintained by the
Novel Materials Discovery (NOMAD) Laboratory, it is the world’s largest
repository of input and output files from all major computational materials
science programs. The repository contains data in raw format, while the
archive provides normalised data in a common, machine-processable format.
After starting with the repository in 2014, the project has expanded and
developed significantly in the following years as part of the Horizon 2020
European Center of Excellence, NOMAD CoE.717171[79, p. 260-261].

##  4 Publications by the Max Planck Society in the Year 2020

The previous two chapters show that research data and open access were already
widely discussed and in some places already implemented within the Max Planck
Society in 2020. At this point in time, there were no general obligations to
make research data publicly available. Therefore, this year can be used as a
baseline from where to observe future developments. In order to put this
understanding on a statistically sound basis, we present in detail our sample
of publications by Max Planck scientists in 2020. The focus of this analysis
is the handling of research data. It will demonstrate how and where the Max
Planck scientists published their data in 2020.

###  4.1 Method

We manually analysed publications with at least one author from a Max Planck
Institute. First, we assessed whether the publication was either
empirical727272We defined an empirical paper as one that draws conclusions
based on information from the real world. After a long discussion we decided
not to consider literature as such information. For example, literature
reviews are treated as non-empirical works or non-empirical, i.e. theoretical.
Theoretical publications, which are often found in legal research, but also in
mathematics or engineering, are not usually expected to contain research data.
We then looked for a statement on data availability or similar. If data was
stated to be available, we tried to access the data and categorised the result
(all data, some data, no data) and the type of data obtained (raw data,
analysed data, summarised data).737373i.e. only tables for figures or single
values If the data availability statement said that the data were ”available
on request”, we contacted the corresponding author (not necessarily from a Max
Planck Institute) and asked for the data. We also checked whether the use of
software was mentioned and whether this software was available.

###  4.2 Approach

The publication repository of the Max Planck Society MPG.PuRe contained 15,850
publication references from 2020 (the population, measured on 29/11/2022) that
had a publication type for which research data could be expected (journal
article, monograph, book, book chapter, edited volume, contribution to edited
volume, conference paper, dissertation). Since December 2021, we took a
pseudo-randomised747474we created the MD5 hash of the publication identifier
and sorted the list by this hash. Later updates were merged into the list
sample of 1,040 publications. We then tried to access the publications,
preferably on the publisher’s platform. Where this was not possible, we tried
to obtain a post-print version from the repository, the author’s institute,
other libraries or through interlending (n=985). Finally, we looked for a
preprint (n=12) or manuscript (n=20). There were 21 publications that we were
not able to access despite thorough searches. We excluded these from the
sample, which left us with a total Ntotal=1,019.

###  4.3 Descriptive Analysis

The vast majority of publications were journal articles (85,3%), other
publication types followed far behind (see table 1).

Table 1: Publication Types. | Frequency | Percent | in population
---|---|---|---
Journal article | 869 | 85.3 | 83.3
Thesis | 52 | 5.1 | 5.2
Book chapter | 35 | 3.4 | 4.1
Conference paper | 31 | 3.0 | 4.2
Book | 13 | 1.3 | 0.9
Contribution to collected edition | 13 | 1.3 | 1.5
Monograph | 5 | 0.5 | 0.6
Collected edition | 1 | 0.1 | 0.3
Total | 1019 | 100.0 | 100.0

The Max Planck Society is divided into three sections: the Chemical, Physical
and Technical Section (CPTS), the Biological and Medical Section (BMS), and
the Humanities and Social Sciences Section (GSHS). Publications are
distributed among the sections as shown in the table 2. Combinations of
sections reflect collaborations between institutes from different sections.

Table 2: MPG sections | Frequency | Percent | in population
---|---|---|---
CPTS | 555 | 54.7 | 55.6
GSHS | 245 | 23.9 | 23.4
BMS | 214 | 20.0 | 19.4
CPTS,BMS | 2 | 1.1 | 1.4
Central | 2 | 0.2 | 0.1
GSHS,BMS | 1 | 0.1 | 0.0
Total | 1019 | 100.0 | 100.0

In 2020, Max Planck scientists published their research results in many
different places. The absolute numbers of all publications by Max Planck
scientists certainly show similarities to the national figures provided by the
ESAC Market Watch for Germany.757575For more details and the code see mainly
[46]. For the publications selected in this study, this means that the top
three publishers were Springer Nature, Elsevier and Wiley. As figure 2 shows,
in 2020, one fifth of the selected empirical publications were published at
the top three.

Springer NatureWileyElsevierAmerican Chemical SocietyAmerican Physical
SocietyIOPOxford University PressSpringerRoyal Society of ChemistryEDP
Sciences$0$$1$$2$$3$$4$$5$$6$$7$$7.2$$7.2$$5.8$$4.7$$4.5$$4.2$$3.8$$3.1$$2.5$$2.3$Percent
of Total Selected Publications$21\%$$31.5\%$$31.2\%$$16.3\%$

Figure 2: Ten Most Frequent Publisher of the Empirical Selected Publications

###  4.4 Selected Publications

We analysed a total of 1,019 data publications in more detail. The CPTS
accounted for slightly more than half of this sample, see figure 3. This
result is to be expected due to the relatively large number of institutes and
staff in this section. In addition, about a quarter and a fifth of the
publications came from the BMS and GSHS, respectively. Collaborations
involving Max Planck scientists from several sections account for only 1.4% of
the total number of publications.

CPTS GSHS Mixed/Other BMS
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$ Figure 3:
Distribution of Publications by Max Planck Sections

An explicit look at the GSH section with its 244 publications is particularly
remarkable in this distribution. Here, comparatively different disciplinary
cultures are linked. The more detailed distribution in figure 4 shows the
distribution of research fields. The majority of GSHS publications in 2020
came from the human sciences. This is followed at a distance by publications
from the fields of law and the humanities.767676Our internal breakdown of the
GSH section can be found in the corresponding data publication, see 8.1.
Publications in the social sciences have the smallest share in the sample.

Humanities Human Sciences Social Sciences Legal Sciences
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$
Figure 4: Distribution of Publications within the GSH Section

The core of a bibliometric analysis of research data is mentioned in textual
publications. However, it is important to remember that theoretical work does
not necessarily have to include or be based on research data. Behaviours and
characteristics can be postulated on the basis of theoretical assumptions and
deductions. These would only have to be supported or refuted by empirical
data, for example, in a second step. For this reason, to gain this insight, we
inquired to what extent each publication could be considered theoretical. This
classification is important in the following, as we only analyse the handling
of research data in 2020 in the case of non-theoretical publications. This is
because we limit ourselves to dealing with the management of research data.
The inclusion of theoretical works, which are not supposed to have research
data, would put the results of the analysis into an additional negative
context.

According to this classification shown in figure 5, about 30% of the randomly
selected papers are non-empirical. In contrast Nempirical = 708 are empirical
publications.

Empirical Non-empirical
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$
Figure 5: Classification According to Empirical and Non-Empirical

In relation to the scientific domains described above, the highest ratio of
empirical publications are in biological/medical research (82%) whereas in
humanities, no empirical works appear in this sample (see figure 6). We
excluded theoretical work from the sample.

BMSCPTSHuman SciencesSocial SciencesLegal
SciencesHumanitiesMixed/Other$0$$50$$100$$150$$200$$250$$300$$350$$400$$450$$500$$550$$600$$168$$418$$96$$8$$7$$11$$37$$137$$44$$16$$38$$35$$4$Percent
of Total
PublicationsTheoreticalEmpirical$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$

Figure 6: Empirical and theoretical publications in different domains

###  4.5 Results

Figure 7 shows our sample, where 40% of the empirical publications provide
research data. Conversely, this means that research data is not available in
60% of the publications with empirical focus.

Not available Partly available Completely available
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$
Figure 7: Research Data Availability within Selected Empirical Publications

However, the claimed 40% of available research data Nempirical is even more
limited. As figure 7 visualises, open research data is actually completely
available for only 137 publications. This is partially the case for a further
43 (6,2%) publications. For a total of 84 publications it is stated that the
research data will be made available upon ”reasonable request”. In theory,
these publications should be accessible, but in reality this is not always the
case. This phenomenon will be discussed in more detail in the following
chapter 4.8. To anticipate this, in our case only a fraction of the research
data was actually made available; as shown in figure 8.

NoYesAfter requestPartlyAfter paymentAfter
loginOther$0$$50$$100$$150$$200$$250$$300$$350$$400$$450$$425$$137$$84$$43$$8$$10$$1$Numbers
of
Publications$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$

Figure 8: Types of Availability.

The handling of research data in theses, especially doctoral theses, is
striking: Only 18.2% of the 44 theses have (some) research data, whereas 81.8%
have none.

Table 3: Available data per publication type with $n\geq 5$ | partly(%) | complete(%) | sum(%) | n
---|---|---|---|---
Journal article | 6,2 | 36,0 | 42,3 | 641
Conference paper | 0,0 | 23,5 | 23,5 | 17
Thesis | 6,8 | 11,4 | 18,2 | 44
Total | 6,1 | 33,9 | 40,0 | 702

At the same time, a closer look at the aggregation level of the data in
empirical publications shows that 31.5% of the research data is in its raw
state, see figure 9. In contrast, only 11.7% of research data consists of a
series of individual values. Most of the publications (56.8%) offer research
data in an analysed form.

Analysed Raw Data Single Values
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$$56.8\%$$31.5\%$$11.7\%$
Figure 9: Data aggregation level

The availability of data also varies widely among the publishers that released
the works, shown in table 4. Data is mostly available in high-impact journals
like Cell and Nature and in the open-access journals of Frontiers and
Copernicus. At the lower end of the scale are the learned societies with the
exception of the American Geophysical Union where data is available for every
publication. However, data policies can be an important point for clarifying
these differences. Publishers have very different policies on data
availability and reproducibility. As shown by Canadian colleagues, data
policies have become increasingly common in journals since the
mid-2010s.777777[9, pp. 381-382]. It seems quite plausible that there is a
causal relationship between data availability and the existence of a
publisher’s data policy.

Table 4: Available data per publisher with $n\geq 5$ | partly(%) | complete(%) | sum(%) | n
---|---|---|---|---
American Geophysical Union | 0.0 | 100.0 | 100.0 | 8
Cell Press | 20.0 | 73.3 | 93.3 | 15
Frontiers | 0.0 | 90.9 | 90.9 | 11
Copernicus Publications | 16.7 | 66.7 | 83.3 | 12
Nature | 9.8 | 68.6 | 78.4 | 51
Academic Press | 25.0 | 50.0 | 75.0 | 8
BioMed Central | 0.0 | 71.4 | 71.4 | 7
Oxford University Press | 7.4 | 51.9 | 59.3 | 27
AAAS | 0.0 | 50.0 | 50.0 | 10
American Institute of Physics | 20.0 | 20.0 | 40.0 | 5
MDPI | 20.0 | 20.0 | 40.0 | 10
Wiley | 7.8 | 29.4 | 37.3 | 51
Springer | 4.5 | 31.8 | 36.4 | 22
Cambridge University Press | 0.0 | 33.3 | 33.3 | 6
EDP Sciences | 6.3 | 25.0 | 31.3 | 16
Pergamon | 0.0 | 28.6 | 28.6 | 7
Royal Society of Chemistry | 0.0 | 22.2 | 22.2 | 18
Elsevier | 0.0 | 22.0 | 22.0 | 41
American Chemical Society | 9.1 | 6.1 | 15.2 | 33
IOP | 3.3 | 10.0 | 13.3 | 30
American Physical Society | 0.0 | 3.1 | 3.1 | 32
Total | 6.4 | 36.2 | 42.6 | 420

Similar to the different publishers, there are differences in data
availability between the sections of the Max Planck Society. In terms of data
availability within the three sections, an average of 33.8% (NEmpirical with
data=239) of empirical publications are linked to accessible data. An average
of 6.2% (NEmpirical with partly data=44) of the publications in the sections
have partial data. The figure 10 shows the statistical variation between the
sections. The data availability within the CPTS is slightly below average,
while the BMS and the GSHS are slightly above average. Combining this with the
values for partial data availability, we see that within the BM section almost
every second publication contains research data. In the CPT section, however,
this is the case for every third publication.

At the same time, it is important to have a look at the similarities in these
figures. The difference between the BMS and the CPTS is only 15%. As a result
it can be argued that data availability within the Max Planck Society differs
only slightly between the individual sections. No section outperforms another
by a multiple in terms of percentage data availability. A long-term
perspective is particularly interesting for these figures. Here it might be
possible to observe whether different developments and internal discussions
regarding the availability of research data in publications take place in
different subject groups or times.

BMSCPTSGSHS$0$$5$$10$$15$$20$$25$$30$$35$$40$$45$$50$$55$$42.9$$28.5$$39.4$$10.1$$5$$5.4$Percent
of Publications within a Max Planck
SectionPartlyComplete$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$$56.8\%$$31.5\%$$11.7\%$

Figure 10: Data Availability within the Max Planck Sections of the Empirical
Selected Publications

###  4.6 Data Licenses

The FAIR principles mentioned in chapter 2.2 have increasingly become a quasi-
standard. The key idea behind these principles is to make the descriptive
aspects of data explicit. This concerns, for example, a statement about the
possibility of using the data, which is made explicit by convention through a
licence. It is therefore an interesting aspect to ask about the use of
licences within the here presented Max Planck sample. This provides a better
perspective on the concrete application of the FAIR principles, independent of
the often communicated relevance of the principles.

If we ask this question of the 239 available publications whose research data
we were able to access, the result is somewhat surprising. Three quarters of
the data do not have their own licence. It is therefore unclear how and under
what conditions the data could be reused. In contrast, only a quarter of the
data had an explicit licence. Of these, the CC BY 4.0 licence was clearly the
most widely used, at 15%. CC0 is another licence worth mentioning, with two
percent. The remaining, many licences are lost in a kind of background noise
at 6.8%.

###  4.7 Data Repositories

As with licences, looking at the used data repositories in our sample reveals
a behaviour in relation to data availability. Here, it is particularly
interesting to see which category of repository is represented. Figure 11
shows the distribution of publications by repository: the first value is
striking. However, it should be noted that the use of no repository rather
indicates that the data was for example attached directly to the publication.
Data papers as a genre are also possible. So there are other ways of making
data available than using data repositories.

The largest number of Max Planck researchers (4.8%) of the publications sample
have made their data available via the National Center for Biotechnology
Information. This US data repository specialises in the storage of molecular
biology data. It has become the quasi-standard repository for data in this
field of research. Despite its institutional reference, it can be classified
as a subject specific repository due to its focus on specific topics. In
addition, ENA, PRIDE and Allele have been used as other subject-specific
repositories for the availability of research data. They all have a focus on
bioinformatics. In this research environment it can be observed that a
research culture for the use of repositories already exists. Some of the
repositories are even internally differentiated, such as proteomics data in
PRIDE. In addition, SIMBAD stands out as a subject-specific repository for
astronomical research data. After all, almost 2.0% of all research data in our
example was published there.

In addition to subject-specific repositories, the use of generic data
repositories by Max Planck scientists can be observed in 2020. Figure 11 shows
that they were used frequently in our sample. It is remarkable that
https://github.com, a primary software repository, was used as a data
repository in 4.1% of cases. The institutional data repository of the Max
Planck Society, Edmond, had been used (1.7%).

No
repositoryNCBIGitHubOSFZenodoEdmondDryadENAPRIDEAlleleSIMBADUCIOther$0$$5$$10$$15$$20$$25$$30$$35$$40$$45$$50$$55$$60$$55.1$$4.8$$4.1$$3.4$$3.1$$1.7$$1.4$$1.4$$1.4$$1$$1.9$$1$$19.8$Percent
of with
Publications$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$$56.8\%$$31.5\%$$11.7\%$

Figure 11: Data Repositories at the Available Research Data

###  4.8 Data “Available” Upon Request

Data is not always readily available. As discussed in chapter 2.3, this
phenomenon is associated with some problems. In our sample, this is
exemplified by the 84 publications for which data are described as available
upon (reasonable) request. It was possible to obtain data on request from
about 20% of the publications. Specifically, about 8% of the corresponding
authors could not be contacted because their 2020 email address no longer
worked and about half of the authors did not answer at all. At the same time
it was also observed that, despite justified requests, the data was not
available. Figure 12 visualises this.

No answer Data fully shared Answer per email, but no data sharing Email of
corresponding author is broken Data sharing explicitly refused
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$$56.8\%$$31.5\%$$11.7\%$$46.88\%$$20.31\%$$7.81\%$$20.31\%$$4.69\%$
Figure 12: Distribution of responses to a data request

In fact, it is alarming that more than three-quarters of the research data
claimed to be available are in fact unavailable. With 84 publications related
to this, the data base is comparatively small. Nevertheless, it can be said
that the enquiries and correspondence leading up to the final result were
lengthy, time-consuming and often frustrating. Similar to other studies
described above, our experience shows that obtaining data usually involves a
long email correspondence with the authors.787878[71, p. 8].

###  4.9 Research Software in Publications

Software is often needed to reproduce the analysis of research data and to be
able to follow the path of scientific knowledge. Research software was
mentioned in some form in just over 41% of the selected publications (see
figure 13). Research software was only marginally the focus of this study.
Nevertheless, it is remarkable how clear the treatment of the accessibility in
comparison to research data is.

No Yes
$21\%$$31.5\%$$31.2\%$$16.3\%$$54.5\%$$24.0\%$$20.1\%$$1.4\%$$57.38\%$$14.34\%$$18.44\%$$9.84\%$$69.5\%$$30.5\%$$60.0\%$$6.2\%$$33.8\%$$56.8\%$$31.5\%$$11.7\%$$46.88\%$$20.31\%$$7.81\%$$20.31\%$$4.69\%$$59\%$$41\%$
Figure 13: Mentioned Software in the Selected Publications

Of particular note is the more explicit use of research software in
publications compared to research data. Research software is mentioned in 288
of the Ntotal = 708 selected empirical publications. And of these 288 software
packages, 226 – i.e. 78% – are openly accessible. This is a significant
difference compared to the figures from openly available research
data.797979However, it should not be concealed that even with research
software not all elements are always accessible and sharing with others is
refused here. Overall, however, this is on a much lower level. See also the
observations from [2, 832] This is in line with other research on the open
availability of research software. For example, in 2021, research software on
https://github.com was already mentioned in over 20% of all publications on
https://arxiv.org/.808080[18].

##  5 Discussion

”There is nothing either good or bad, but thinking makes it so.”818181William
Shakespeare (1564–1616), Hamlet, act II, scene 2. To cite Shakespeare, how we
value open research data depends on our own point of view and context. The
selection of publications from 2020 presented here can therefore also be
discussed from different perspectives on the part of the Max Planck Society.
We have identified five key aspects from the analysis of publications in 2020
and its relation to data availability.

1\. Expectations and actual results differ significantly by type of data
availability. Particularly in empirical work one would expect the research
data to be available in some way, whether as open research data or with
restricted access. Since in terms of good scientific practice, it should be
possible to reproduce the results. Considering this statement, only 40% of
research data available or partially available for empirical publications is
not much. Theoretically, one could expect 100% availability. In all previous
studies, however, the found numbers were at a very low level.828282See chapter
2.2. Open or restricted access – for which there are good reasons – would be
irrelevant for the time being. Reproducibility would mainly be guaranteed in
both cases. However, almost 60% of empirical work is without data. For an
excellent research organisation like the Max Planck Society, which sees itself
as one of the leading scientific organisations operating in the field of basic
research, there is still potential for a greater data availability.

2\. The aggregation of the available data is predominantly analysed. But there
is also raw data. There is a discussion about laboratory data and how far it
can or should be published.838383See for example [64, p. 192]. There is
therefore no definite answer to the question of when data should be published.
This is probably a case-by-case decision. For the Max Planck Society in its
diversity, it does not seem sensible to formulate a general rule for the
degree of aggregation.

3\. A data policy can increase the availability of data associated with
textual publications. Such normative requirements on the part of publishers
lead and will increasingly lead to available data in the near future. It can
be assumed that publishers, funders and scientific organisations will
increasingly develop such normative frameworks for scientists. However, this
is not the same as demanding open access to research data. There may be good
reasons for publishing data with restricted access, for example in clinical
trials or industrial contract work.
”Nothing can come of nothing.”848484William Shakespeare (1564–1616), King
Lear, act I, scene 1. Greater visibility of their research results and the
application of Open Science can, for example, be motivation for a data policy.
Such a normative framework is indispensable if the Max Planck Society or
individual institutes want to motivate their own scientists in this direction.
Recent developments at the Helmholtz Association in particular show how such a
path could be taken in German basic research.858585See for example [37] and
[26].

4\. The concept of data availability statements like ”Data available on
reasonable request” do not work as expected. Response rates are low. The
communication effort is usually high. Both our experience and other studies
have shown that there is a mismatch between effort and return. Storing the
research data in a data repository – with open or restricted access – would
eliminate this problem.
For the Max Planck Society, this may lead to a kind of recommendation that
research data should be published in suitable infrastructures. These could be
research data repositories, data journals or similar solutions.

5\. The data handling culture of the sections of the Max Planck Society do
differ from each other. At the same time, the differences in the publication
of research data are not as great as one might expect. This suggests that the
Max Planck Society has already taken aspirations towards a data sharing
culture. For example, the new rules on good scientific practice mention
research data and the explicit handling of them quite often.868686A brief
overview of the research data aspect of the new rules can be found in [34].
However, if we compare the Max Planck Society with, for example, the Helmholtz
Association and its use of research software, or the Charité and its open
research applications, there is still a lot of potential that can be employed
within the Max Planck Society.878787[38], [44] and [60].

##  6 Perspectives

Analysing the publications by Max Planck researchers in 2020 is the past. What
has happened since then?

Open Science and Open Research Data are becoming increasingly relevant in the
German research landscape. This is clearly indicated, for example, by the
positioning of the DFG in autumn 2022.888888[14, pp. 4-5]. There are many
advantages to sharing data and code for a culture of science.898989See also
the detailed discussion of the advantages and disadvantages in [31]. With a
focus on Germany, the NFDI will play an increasing role in this. One of its
first successes is that research data and its management have not only reached
the scientific community but German decision-makers, e.g. in politics. It is
therefore not a far-fetched thesis to assume that research data will become an
increasingly important topic in the coming years. The debate has already lost
some of its drama. It is now less a question of ”why” and more a question of
”how”.

All this manifests itself in the fact that the topic of research data is
increasingly being dealt with locally at the Max Planck Institutes. The new
guidelines on good scientific practice from the end of 2022 also made a
significant contribution to this.909090[57]. There are local working groups,
initiatives and committed colleagues looking for local solutions in their
departments and institutes. At the same time, a Max Planck-wide network of RDM
experts is evolving. Events such as the regular RDM workshops of the Max
Planck Digital Library are evidence of this.919191See
https://rdm.mpdl.mpg.de/mpdl-services/workshops/. However, it remains to be
seen whether the developments at the individual institutes will be merged. It
also remains to be seen whether concepts such as ”data stewardship” will
become established in individual disciplines.

There is already a clear need for more knowledge about research data within
one’s own institution. Services such as the Charité Dashboard on Responsible
Research mentioned above, the HMC Dashboard on Open and FAIR Data in Helmholtz
or the French Open Science Monitor provide a first glimpse of what such
bibliometric services might look like. The transition to evaluation methods
and research assessment is, of course, imminent. Since the Max Planck Society
has a real interest in such metrics, it would certainly be in everyone’s
interest to be able to offer such services (internally) in the long term. Such
dashboards with a focus on research data can, for example, also open up
longitudinal perspectives on how Max Planck scientists deal with research
data. Nevertheless, it is clear that such analyses of data availability as we
have presented here should be carried out more frequently. It will be
interesting to see how publishing behaviour changes as a result of the
developments mentioned. In the end, however, it is said, ”[b]e great in act,
as you have been in thought.”929292William Shakespeare (1564–1616), King John,
act V, scene 1.

##  7 Abbreviations

  * •

BMC = BioMed Central

  * •

BMS = Biological and Medicine Section of the Max Planck Society

  * •

CPTS = Chemistry, Physics and Technology Section of the Max Planck Society

  * •

DFG = Deutsche Forschungsgemeinschaft

  * •

DMP= Data Management Plan

  * •

DOI = Digital Object Identifier

  * •

ENA = European Nucleotide Archive

  * •

EOSC = European Open Science Cloud

  * •

ESAC = Efficiencies and Standards for Article Charges

  * •

FAIR = Acronym of findability, accessibility, interoperability, and
reusability regarding data principles, see [77]

  * •

GSHS = Human Sciences Section of the Max Planck Society

  * •

GWK = Joint Science Conference

  * •

HRK = Hochschulrektorenkonferenz (German Rectors’ Conference)

  * •

MPCDF = Max Planck Computing Data Facility

  * •

MPDL = Max Planck Digital Library

  * •

MPI = Max Planck Institute

  * •

MPS = Max Planck Society

  * •

NCBI = National Center for Biotechnology Information

  * •

NFDI = National Research Data Infrastructure

  * •

OECD = Organization for Economic Co-operation and Development

  * •

ORDP = Open Research Data Pilot

  * •

CC = Creative Commons

  * •

PLOS = Public Library of Science

  * •

PRIDE = Proteomics Identifications Database

  * •

SIMBAD = Set of Identifications, Measurements and Bibliography for
Astronomical Data

  * •

URL = Uniform Resource Locator

  * •

WR = German Science and Humanities Council (Wissenschaftsrat)

##  8 Statements and Comments

###  8.1 Data and Code Availability Statement

The data and code are freely available in Edmond via
https://doi.org/10.17617/3.XI0LP5. Via Edmond the scripts can also run
directly in Mybinder using
https://mybinder.org/v2/dataverse/10.17617/3.XI0LP5.

###  8.2 Author contributions

The study design was set up by Franke and Grossmann. The drawing of the sample
was programmed by Franke. Ho and Matthiesen were mainly responsible for data
collection and research. Ho and Matthiesen were also in charge of the
graphical representations of the results. Ho and Franke have evaluated and
analysed the data collected. The texts for the open data examples were written
by Boosen. Other text elements including the bibliography were written by
Grossmann. Corrections and linguistic adjustments were done by Leiminger.

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