dataesr/scientific-paragraphs-categorization huggingface dataset:
Antigen design and antibody production. PsaL sequences are not sufficiently well conserved for antigen design from a single consensus sequence. To produce one antigen that could be used to antisera against all PsaL of interest in this study, that is, Chroococcidiopsis sp. TS-821, Arabidopsis, T. elongatus BP-1 and Synechocystis sp. PCC 6803, a chimeric protein (CATSPsaL) containing fragments of the different PsaLs was designed. The Kolaskar and Tongaonkar antigenicity method  47  via the Immune Epitope Database (http://tools. immuneepitope. org/bcell/) and ABCpred  48  (http://www. imtech. res. in/raghava/abcpred/index. html) were used to predict suitable epitopes. The antigen sequence was aligned against different PsaL fragments and the predicted antigens to ensure a high probability of obtaining an antibody for each PsaL (Supplementary Fig.
Isolated PSI samples were dialysed before analysis. For the proteomic comparison between PSI trimers and PSI tetramers, isolated PSI oligomers were further purified by BN-PAGE and gel slices were analysed. Low temperature (77K) fluorescence spectra were acquired with excitation wavelength at 430 nm and the emission spectra at 600-800 nm were recorded. Each emission spectrum was averaged from ten technical repeats to enhance signal-tonoise ratio. Absorption spectra of isolated proteins and pigments were taken using a Cary 300 UV-Vis spectrometer (Agilent). The sample buffers were used as blanks. For PSI oligomer absorption spectra comparisons, A 680 was adjusted close to 0. 5 or 1. The PSI absorption differences were calculated after normalizing the data at A 680. Electron microscopy and single particle analyses were done as described in previous studies  21, 22.
7. 307  53  ; ambiguous and saturated regions were removed with BMGE v. 1. 12 (with the gap rate parameter set to 0. 5)  54. The best-fitting model of amino acid substitution for this dataset was selected with ProtTest v. 3. 2  55. A maximum-likelihood phylogenetic tree was generated with the alignment using PhyML 3. 1. 0. 2 56 using the LG amino acid substitution model with gamma-distributed rate variation (six categories), estimation of the proportion of invariable sites and exploring tree topologies. One-hundred bootstrap replicates were performed. The phylogenetic trees were displayed and annotated using the interactive tree of life (iTOL) online tool  57. Genomic context of psaL, psaI, psaF and psaJ genes and PsaL protein sequences were extracted from the MicroScope platform  58  , public databases and data generated in a previous study  59  or this study Supplementary Data).
Acknowledgements Support to B. D. B. , M. L. and J. T. N. was provided by the  Gibson Family Foundation , the  Bredesen Center for Interdisciplinary Research and Education , the  Tennessee Plant Research Center , a  UTK Professional Development Award , the  Dr. Donald L. Akers   Faculty Enrichment Fellowship  to B. D. B. and  National Science Foundation  support to  B. D. B. ( DGE-0801470  and  EPS-1004083 ). M. L. has been supported as a CIRE Fellow at  University of Tennessee, Knoxville. A  Professional Development Award  from the  Graduate School  at  UTK  supported travel of B. D. B. to the Netherlands and to the  Institut Pasteur. NWO Chemical Sciences  supported work at  University of Groningen. J. P. W. has been supported by  NIH   P30 DK063491. The  Institut Pasteur supported Pasteur Culture Collection of cyanobacteria.
Data availability The cloned cyanobacterial psaL sequences have been deposited in GenBank with the accession numbers KY575410, KY575411, KY575412, KY575413, KY575414, KY575415, KY575416, KY575417, KY575418, KY575419, KY575420, KY575421, KY575422, KY575423 and KY575424. The TS-821 whole-genome shotgun project has been deposited at DNA Data Bank of Japan/European Nucleotide Archive/ GenBank under the accession MVDI00000000. The version described in this paper is version MVDI01000000.
This case study critically examines ongoing developments in contemporary scholarship through the lens of research data management support at KU Leuven, and KU Leuven Libraries in particular. By means of casebased examples, current initiatives for fostering sound scientific work and scholarship are considered in three associated domains: support for policy-making, the development of research infrastructures, and digital literacy training for students, scientists and scholars. It is outlined how KU Leuven Libraries collaborates with partner services in order to contribute to KU Leuven's research data management support network. Particular attention is devoted to the innovations that facilitate such collaborations. These accounts of initial experiences form the basis for a reflection on best practices and pitfalls, and foreground a number of pertinent challenges facing the domain of research data management, including matters of scalability, technology acceptance and adoption, and methods for effectively gauging and communicating the manifold transformations of science and scholarship.
The current discourses, practices and services surrounding research data and RDM thus not only frame data as a scientifically and economically coveted resource ('the new gold,' 'the new oil,' '(renewable) energy'), but also, as it is considered in the present case study, as a shared resource. That is, a resource co-created, transformed and managed by numerous stakeholders. It is in this regard that research libraries play a key collaborative role in this domain. Research data management, as a series of practices for dealing with resources, indeed foregrounds some of the expertise and functions which libraries have held since antiquity, and which they have perfected since they, as Jeffrey Schnapp and Matthew Battles have it, moved 'beyond the book' to embrace (digital) technologies  (Schnapp & Battles, 2014).
Already at the library of Alexandria, for instance, the challenges of creating, transferring and preserving knowledge were faced by theoreticians and practitioners alike, as ideas conceptualized by philosophers and scholars were inscribed or copied onto scrolls and classified by means of a shelving system, with the aim of opening up knowledge to others. Under one roof, gaps were thus bridged between knowledge management strategies such as the creation of ontologies and taxonomies, and knowledge management practices including scholarly primitives such as the physical acts of writing and copying. As a result of the digitalization and library automation efforts of recent decades, research libraries can operationalize the extended guises of these early resource practices, such as ontology engineering, metadata assignment, data structuring, information retrieval, and an intensified involvement in the research life cycle through efforts in scholarly communication.
This is not to say, however, that library participation in research data management comes without its proper challenges. Support for RDM initiatives on the levels of policy-making, infrastructure development and digital literacy training requires continuous innovations of existing services and infrastructures. The present case study explores these dynamics based on the process through which KU Leuven Libraries equipped itself for durable participation in the changing scholarly domain. To this end, the case study offers a series of comparative analyses that relate KU Leuven's RDM initiatives to theoretical reflections from the relevant literature, as well as to ongoing practical developments at other (European) research institutions. Notably, Section 2 of the case delves into concrete examples of how RDM services were developed in the context of the 2018 call for proposals of the Research Foundation Flanders (FWO).
Research Data Management Support at KU Leuven Libraries KU Leuven Libraries is an organization that consists of 24 libraries and learning centres across Flanders. Through a series of talent development projects for staff in 2014-2015, KU Leuven Libraries, together with the Research Coordination Office, has been involved in the early conceptualization of services and infrastructures to face the challenges of research data management. To consolidate this involvement, support for research data management has since been included as a concrete point of action in the libraries' strategic plans, first for the period 2015-2017, and then -even more explicitly -in the strategic plan for 2018-2021. A lot of practical information and inspiration was drawn from mapping best practices from other universities, such as TUDelft and UGent, and consequently fine-tuned to meet the needs at KU Leuven.
Since 2016, RDM support at KU Leuven is organized around a central RDM support desk, located in KU Leuven Libraries, and currently staffed by two employees. In order to provide sufficient first-line support for RDM, this central RDM support desk is complemented by an interdisciplinary network of subject-specific data experts and data stewards. The data experts consist of a centralized group of approximately five library staff members that have backgrounds in research and research support, and who have the responsibility of staying informed about (international) developments in the field of RDM, participating in the university-wide working groups concerning RDM, and acting as intermediaries between the central support desk and the data stewards. These data stewards are a group of approximately twenty-five information specialists and reference librarians.
Embedded within various faculties and libraries across KU Leuven, the data stewards are positioned closely to researchers and are thus best placed to act as first-line support. A key point in this regard was that, until now, no new staff were hired, but that investments were made to retrain staff already on board. The network of data experts and stewards collaborates with the university's IT services, the Legal Department, KU Leuven Research and Development, the Research Coordination Office, and the library systems developer LIBIS. Researchers can contact the central support desk through a central e-mail address and (since 2019) corresponding ticketing system. Inquiries concerning data types, meta data and documentation, repositories and frequently asked questions are handled by the support desk in collaboration with the KU Leuven Libraries' network of data experts and data stewards.
Apart from questions that are centred around core library services (data types, metadata, repositories), topics of a more specialized nature, such as those concerning data storage and ethical or legal aspects of research, are delegated to the responsible units at KU Leuven. Depending on the topic, the other units either take complete control of the issue raised, or feed back to the support desk, which then communicates this input to the researchers. In 2017-2018, an internal assessment of the state of digital scholarship services was made at KU Leuven using the RISE framework  (Rans & White, 2017). This tool guides research institutions through the process of assessing their research infrastructures by grading them on a scale of one to three, each number indicating increasing degrees of maturity of the infrastructure.
The topics covered by the framework are as follows: RDM Policy & strategy, Business plans and sustainability, Advisory Services, Training, Data management planning, Active data management, Appraisal and risk assessment, Preservation, and Access, publishing and discovery. Some of these topics are further divided into a number of (more concrete) sub-topics. E. g. the topic of business plans and sustainability covers the subtopics Staff investment, Technology investment and Cost modelling. One of the major gains of using this framework is not only that it offers a state of the art, but also helps to map the many partners and aspects of research data management support. The final report, prepared by KU Leuven Libraries, IT services and the Research Coordination Office, was presented to the vice rector for research policy in March 2018.
It led to the establishment in the summer of 2018 of a steering committee on RDM (with underlying working groups on policy, infrastructure, and advice and training). KU Leuven Libraries, naturally, is one of the units which play an active role in both the steering group and the working groups. In order to identify some of the challenges and required innovations associated with RDM, and to illustrate the working of the expert network, this section provides case examples from the RDM support that was provided at KU Leuven in the context of the 2018 call for proposals of the Research Foundation Flanders (FWO). Though some services had already been provided earlier in the context of a limited number of Horizon2020 applications, this was the first occasion during which a major funder of research in Flanders required applicants to include an outline for a data management plan, which Researchers who were awarded the grant were required to submit a fully-fledged data management plan (DMP) within six months after the start of their project.
Not to be underestimated in this regard are also the many unwritten rules, best practices and conventions for dealing with research data and information that govern disciplines or even individual departments. At KU Leuven, the current research data policy was first implemented in December 2014. This policy stipulates that in the course of a project research data should at all times be made available upon request to KU Leuven and that the data are to be retained for a period of at least five years after publication or after the end date of the research project grant agreement. 1 However, third-party agreements overrule the university guidelines. Through its policies, KU Leuven explicitly recognizes the fundamental role of research data in high-quality scholarship and scientific integrity. This is exemplified by the fact that all researchers who obtain substantial internal funding (over €600.
000) are required to fill out a full data management plan within six months after the start of their project. This way, the university's policy ties in seamlessly with the data management requirements as set out by Europe's Horizon2020 programme and the aforementioned FWO. Anticipating that the requirement of filling in a DMP will extend to other application procedures, not only at a postdoctoral but also at pre-doctoral level, data management plans are already a required milestone in the doctoral training of certain Faculties or Departments at the university, such as the KU Leuven Faculty of Arts. In order to assist KU Leuven researchers in complying with the various data management strategies, the KU Leuven Libraries RDM expert network is closely collaborating with various services within KU Leuven.
As such, the network fulfils an interfacing role between researchers and specialists in various aspects of research data regulations and policies. Firstly, the Research Coordination Office ensures funder compliance and liaises with the various Ethical Commitees. Secondly, the Legal Department offers tailored advice on the processing of personal data under GDPR and answers questions surrounding ownership. Similarly, the IT services provide technological support for various data types. Third, the Technology Transfer Office (KU Leuven Research & Development) manages agreements with third parties. The expert network's intermediating role between researchers and specialists not only benefits the researchers themselves, but also allows policy specialists to keep track of developments within the research community.
The network has for instance helped to procure researcher's feedback on a ledger for personal data as being developed and implemented by the Legal Department. From a research and policy perspective, research data management support thus provides KU Leuven Libraries with an opportunity to continue developing user-oriented services. For example, when assisting in the writing of data management plans (consisting of five questions or in more elaborate form), the data stewards become actively involved in planning and shaping a research project. In addition, the data stewards gain a valuable insight into the research projects that are being set up at the departments they service, for instance by answering researchers' specific questions or by conducting local surveys on data management practices.
, 2016). In both cases, however, knowledge needs to be obtained about researchers' current data management practices and the life cycle of their research data. Data management plans can offer some systematic insight into this matter, but to achieve a wider coverage, a survey for researchers is being developed by the KU Leuven RDM infrastructure working group. This survey gauges among others the types of data that are being gathered, storage practices and requirements, and needs for data sharing. Through involvement in the KU Leuven working group for infrastructures, KU Leuven Libraries thus also contributes to an environment in which researchers are offered opportunities to be involved in the development of research data infrastructures.
Data stewards were familiarized with RDM through information sessions catered to data stewards from the humanities on the one hand, and Liber Quarterly Volume 29 2019 biomedical sciences and science and technology on the other. The data stewards were given a brief introduction to the present day scholarly landscape, funding requirements and benefits of research data management. Next, they were given specific information on how the five questions of the FWO could be answered. Data stewards were not provided with readymade answers or templates, but rather given a basis for further 'on the job'learning. Questions submitted through the support desk where sent to a subject-specific data steward, each time with a data expert in CC, so that the answers could be fine-tuned and data experts and data stewards could learn from each other.
This type of training proved to be an effective way of training a heterogeneous group of data stewards that came from different backgrounds, already took up a number of other tasks and responsibilities, and had varying degrees of familiarity with research methods and practices. The data experts not only provided internal training within KU Leuven Libraries, but also supported other training initiatives at KU Leuven, with a specific focus on data literacy. This included an optional workshop for PhD students in which the basic principles of RDM are explained and illustrated by the library's data experts, an optional workshop on peer reviewing DMPs that is taught by data experts and members of the research support office, an optional session on publishing data and research funding provided by the research coordination office and a mandatory session on research integrity that also features basic guidelines on research data management.
As was mentioned before, research data management training has also been integrated as part of the Doctoral Schools of the Biomedical Sciences and Science and Technology groups, and the doctoral training programme at the Faculty of Arts. Data experts from KU Leuven Libraries play a central role in this subject-specific RDM training, thus making sure that researchers in the early stages of their career are not only trained in RDM, but also know who they can turn to when they encounter questions or problems. What is more, research data management, and the model of the data life cycle in particular have proven to be a solid base to build new curricula, especially in emerging interdisciplinary fields such as digital humanities. At KU Leuven's Faculty of Arts, for instance, the data life cycle was used as an instrument to structure doctoral training in digital humanities as well as a more fundamental undergraduate course on data and information literacy.
At an organization with a high level of administrative complexity and a wide interdisciplinary scope such as KU Leuven, one challenge for RDM support is to strike a balance between developing common, generic services that can help large groups of researchers, and allowing for the necessary flexibility for tailoring those services to more local needs. Based on their past experiences in the fields that they service (either as researchers or information professionals), data stewards and members of the RDM expert network could provide input for mapping the requirements of various fields. To obtain a broader input at KU Leuven, all relevant units and stakeholders of the university are represented in the aforementioned RDM steering committee and the underlying working groups.
A pitfall that could be encountered in this regard is that the focus on generalizing research data life cycles might reduce science and scholarship to mechanical (predictable) endeavours. To be truly effective, research data management planning and support should acknowledge the exploratory and oftentimes meandering nature of scientific inquiry. Planning tools and infrastructures should offer sufficient flexibility so that researchers are not restricted in their ability to tailor their research to unexpected intermediary results and promising new approaches. In this regard, it should be made clear that striving for machine-actionable data does not necessarily entail that all human creativity is extracted from the research process, nor that scholarship can or should be left to algorithms. Specifically, the discipline-specific origins of the FAIR principles should be approached critically, and a lot of work remains to be done on how to translate these principles to domains that work with for instance more unstructured data.
Liber Quarterly Volume 29 2019 made the need for RDM services all the more urgent. The slimmed-down plan required by the FWO consisted of five questions (FWO, n. d. ): 1. Describe the datatypes (surveys, sequences, manuscripts, objects, …) the research will collect and/or generate and/or (re)use. (max. 700 characters) 2. Are the following provisions in place in order to preserve the data during and at least 5 years after the end of the research? Please motivate your answer. (max. 700 characters) 1. Designation of responsible person (if already designated, please fill in his/her name) 2. Storage capacity/repository 1. During the research 2. After the research 3. Is there a reason why you wish to deviate from the principle of preservation of data and of the minimum preservation term of 5 years? (max.
(4) The MDF enrichment factors of a reaction (ε xxx Hg reaction ) were determined by fitting a linear regression model (lm function of R) to the observational data following  Mariotti et al. (1981) : δ xxx Hg residual = δ xxx Hg 0 + ε xxx Hg reaction × ln f, (5) where δ xxx Hg residual corresponds to the Hg isotope signature of the residual Hg(0), δ xxx Hg 0 corresponds to the initial Hg(0) isotope signature, and f corresponds to the fraction of Hg(0) remaining in the gas phase. Note that for high f > 0. 4 the systematic error of this simplified approach is minimal. The MIF enrichment factor (E xxx Hg reaction ) was calculated as follows: xxx Hg residual = E xxx Hg process × δ xxx Hg residual. (6) The long-term precision was assessed through repeated analysis of the ETH-Fluka Hg standard, which yielded values of M.
Acknowledgements. We thank  Toolik Field Station and Polar Field Services  staff for their support in setting up the field site and maintaining its operation for 2 years, with special thanks to  Jeb Timm. We thank  Christine L. Olson ,  Dominique P. Colgrove ,  Jacques Hueber , and  Christopher W. Moore  for assistance in the field and  Jerome Chmeleff  for support with Hg stable isotope measurements. The authors gratefully acknowledge the  NOAA Air Resources Laboratory (ARL)  for the provision of the HYSPLIT transport and dispersion model and READY website (http://www. ready. noaa. gov,last access: 5 August 2019) used in this publication. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant no. PZ00P2_174101 ), the  H2020 European Research Council  (MEROXRE; grant no.