dataesr/scientific-paragraphs-categorization huggingface dataset:
Open resources We recognize that altruism is unlikely to offer enough motivation to ensure widespread participation in the OTN. The sharing of trait datasets is not merely a technical problem to be solved; it relies on custodians having the skills, incentives and motivation to contribute. The key incentives for individuals to join the OTN include increasing the findability of their data and expertise and having access to a ready-made network of trait scientists and institutions engaging in relevant initiatives. Data are a powerful asset for researchers, and release under open-license schemes accompanied by well-defined metadata offers great potential for new collaborations and increased visibility. A persistent concern is that scientists will lose control of their hard-earned data under open licensing, though this underestimates the potential for new collaborations and may unnecessarily increase distrust within the scientific community  67.
Activity 1: Maintaining a global registry of trait-based initiatives. Several data gaps impede synthetic analyses across taxa, geographical locations and ontogeny. The heterogeneous ways in which trait data have been collected to date have resulted in a patchy and unrepresentative data landscape across trait types, taxa, regions and times of the year  68, 69. The OTN bridges these gaps by maintaining a Trait Dataset Registry that can be accessed at http://opentraits. org/ datasets. html. The OTN Registry contains information on existing open (or embargoed) datasets so that gaps can be identified and ultimately filled through collective effort. Core information for the registry includes Digital Object Identifier (DOI), taxonomic coverage, curator and format. The OTN Registry also provides the opportunity for contributors to identify if and where code to process and manipulate raw data is located (see Activity 2).
As it develops, the OTN Registry will relate trait concepts to ontologies provided through the Open Biomedical Ontologies Foundry (http://www. obofoundry. org). The OTN Registry maps to several Open Science principles (for example, Open Source, Open Data and Open Access; Box 2) and is designed to support data retrieval and integration. The OTN does not place restrictions on what members may consider traits of importance to a taxonomic group. Most traits can be measured from individuals and fit into existing definitions, though this may not be appropriate for organisms where individual or taxonomic boundaries are unclear (for example, microbes  70  and fungi  71  ). It can be argued that traits encompass emergent properties of populations (for example, abundance and geographic range size) or represent interactions among species (for example, diet type).
Within the OTN, we believe that more important than imposing strict definitions around traits is engaging the community in discussion about the utility of available data for answering novel ecological and evolutionary questions. data with seamless piping of data from one software tool to the next. OTN contributors have already developed several open-source tools such as the traitdataform package, which assists R users to format their data and harmonize units (http://ecologicaltraitdata. github. io/traitdataform). The code for the Coral Traits database  32  (https:// github. com/jmadin/traits) could be modified to guide the creation of databases on other organisms. The FENNEC project provides a tool for accessing and viewing community trait data as a self-hosted website service  72  (https://github.
com/molbiodiv/fennec). The OTN can act as a connector between developers and the broader community seeking to synthesize trait data, facilitating the training of scientists in all aspects of reproducible data management. Activity 3: Advocating for a free flow of data and appropriate credit. One goal of the OTN is to increase the use of open datasets and to ensure due credit is given to researchers who collect or synthesize primary data. Without effective reward or motivation for collecting new trait observations or sharing legacy data, a trait synthesis across the Tree of Life will remain unattainable. Currently, motivation for collecting and sharing new primary data is not strong and direct funding for trait data management is scarce. The OTN can strengthen the attribution of credit to data providers and promote new data collection via two paths.
Firstly, the OTN will encourage citation back to primary source via a permissive license model that secures authorship attribution (for example, Creative Commons Attribution 4. 0 Int; CC BY 4. 0) and the use of DOIs and Open Researcher and Contributor ID (ORCID) identifiers. Open-access datasets with a DOI can be tracked to understand patterns of re-use and to assess the impact of the author's decision to share. There is an important distinction between sharing data within a network and making data publicly available under an open license. Clear license arrangements increase visibility and promote fair attribution and citation (for example, using Creative Commons licenses such as CC-BY or CC0). CC-BY requires attribution (that is, citation) to the original creator whereas CC0 does not legally require users of the data to cite the source, though this does not affect ethical norms for attribution in research communities (https://creativecommons.
org/share-your-work/public-domain/cc0/). Identifying who should be credited for prior work on legacy data is complicated by the involvement of many individuals. This issue could be solved, in part, by inviting organizations to be named as contributors or co-authors on outputs using their data or (looking forward) implementing new ways of documenting who should be credited for making specimens or datasets usable in trait science. Incentives to collect new trait data can be linked to the Open Science practice of pre-registration. In pre-registration, authors archive a public proposal for research activities (for example, via the Centre for Open Science; https://cos. io/prereg/) which, if approved, may receive in-principle acceptance from participating journals. As of March 2019, 168 journals are willing to give in-principle acceptance following pre-review of the study design prior to conducting field or experimental work.
Ten of these participating journals regularly feature papers on trait-based science (for example, BMC Ecology and Ecology and Evolution). We envision a situation where the OTN Trait Registry (Activity 1) could be used to identify spatial or taxonomic gaps in trait data that could be coupled to pre-registered hypotheses. Together, pre-registration and in-principle acceptance of findings could incentivize the collection of new data, circumventing the growing reliance on available data with known gaps. Activity 4: Creating a trait core to facilitate synthesis and standardization. Trait science requires its own 'core' terminology or data standard that is flexible enough to capture the complexity of trait data. Building on efforts to standardize occurrence data (that is, Darwin Core  58  ) and biological inventories (that is, Humboldt Core  43, 59  ), the OTN envisions a trait core offering a set of crossdomain metadata standards and controlled vocabularies that are (ideally) connected to trait ontologies via unambiguous identifiers.
obofoundry. org). Progress towards a trait core is already being made through the development of a prototypal Ecological Trait Standard 76 (Box 3). However, the development and adoption of a trait core requires consultation and coordination within the broader scientific community, a goal which the OTN is ideally placed to advance. The OTN can mobilize expertise for cross-domain workshops and advocate for funding, which allows not only meetings of experts but also the creation of cyber-infrastructure for synthesis nodes (dark-green nodes in Fig. 2c ). Links to emerging initiatives for biodiversity data standardization (for example, Species Index of Knowledge 57 ) will also be vital for success, as will ratification of the core through the Biodiversity Information Standards (TDWG, www. tdwg.
org). Activity 5: Facilitating consistent approaches to measuring traits within major groups. The OTN will share new developments towards protocols and handbooks for major clades that standardize approaches to capture trait observations. Protocols are necessary because downstream activities such as developing metadata standards (Activity 4) will be impossible if trait measurement protocols do not exist. Some research communities have adopted standardized terms  56, 62  and data collection protocols (for example, plants  20, [77] [78] [79] [80]  , invertebrates  29, [81] [82] [83]  , mammals  36  and aquatic life  30, 32, 84  ), though these may not always fit the requirements of some studies (for example, where trait variability rather than the average trait of species is targeted 85 ). Protocols and handbooks may not emerge rapidly and should have the flexibility to be open to innovation through a commitment to version control and updates as techniques evolve.
Two versions of the plant trait measurement handbook have been published  77, 86  and several online resources exist that can be updated regularly (see http://prometheuswiki. org/tiki-custom_home. php). Standardizing approaches to trait measurement across research communities will reduce ambiguity when aggregating data and improve the quality of resulting datasets. Integrating trait standardization and databasing into taxonomic workflows constitutes a challenge and an opportunity 7 that holds the promise of bridging the long disconnect between structural and functional traits. The presence of a range of biodiversity collections personnel in the OTN and an open invitation for more to join is expected to catalyse the adoption of trait-based thinking into taxonomic practices.
Concluding remarks This is the opportune time to push towards a new approach to sharing and synthesizing trait data across all organisms. Trait science has great potential to increase its taxonomic, phylogenetic and spatial scopes by leveraging data-science tools, embracing Open Science principles, and creating stronger connections between researchers, institutions, publishers and funding bodies. We hope that trait enthusiasts, regardless of field and research stage, will engage with the OTN via our website (www. opentraits. org) and help build new connections between disciplines, institutions and taxonomic domains. By adding metadata profiles for datasets to the OTN Trait Dataset Registry, trait collection efforts become more findable, as do the researchers who have compiled them. We envision that by connecting people with common goals, we can work collectively towards a synthesis of global trait data to preserve the nuances of taxon-specific expertise while also facilitating collaboration across domains.
By supporting a reciprocal exchange of expertise and outputs using Open Science principles between researchers and institutions, we can mobilize data for a cross-taxa, worldwide, trait-based data resource to examine, understand and predict nature's responses to global change. As a better-connected OTN emerges, data streams and coordination will improve, allowing us to deliver information to support globally important research agendas (Box 1) as well as specific data and knowledge to the public through integration with third-party portals. Lessons learned along the path to a global synthesis of trait data across all organisms will provide a framework for addressing similarly complex, context-dependent challenges in biodiversity informatics and beyond. 1. Data collection and data handling within the project context.
Applying project-specific methodology and storing data in tables suitable for data analysis, applying project-specific terms for taxa, traits and column labels. Adopting standard methodologies and terminologies from the start will simplify steps 2 and 3 and facilitate data publication. OTN Activity 5 aims to build consensus for common trait definitions and measurement methodologies for major organismal groups. 2. Harmonization of taxa and traits into standard terms using ontologies. Prior to publication, all taxa should be harmonized into accepted names linked to ontologies using uniform resource identifiers (URIs). Those can be provided in metadata or secondary data tables. Ontologies for traits are scarce, but if available should also be referred to via URIs to deliver unambiguous trait definitions.
OTN Activity 2 will foster the development of ontologies linking trait data to publicly available resources. 3. Standardization of table descriptors and metadata using a standard vocabulary. Data should be published in tables using standard terms for column names, such as those provided by the Ecological Trait-data Standard vocabulary (ETS; DOI: 10. 5281/ zenodo. 2605377). This ETS implements a minimal terminology that can be adapted to include traits from a variety of organisms and uses of uniform resource identifiers for taxa, traits, methods and units, thereby following the standards for a semantic web of scientific data. Metadata should point to the applied standard to facilitate interpretation by humans and machines. OTN Activity 4 will engage in the cross-domain, community-based development for standard vocabularies for trait data.
4. Publication of data and upload to a public repository. Open Access file hosting services offer permanent hosting and findability of data by assigning a DOI to data publications and stating authorship and conditions for re-use under Creative Commons licenses. OTN Activity 3 will support recognition for data publications and thereby mitigate the investment into data standardization for smaller research projects. 5. Synthesis of trait data and re-use in downstream products. Open-access data publications with high-quality metadata are a valuable complement for meta-analysis or the functional analysis of abundance data. By keeping original author terms and values, the quality of the derived datasets can be assured and controlled for a better integration of multiple datasets. Availability of such highquality data will facilitate reproducibility and enable computeraided analysis of large databases.
Acknowledgements Ideas presented stem from initial discussions at three international meetings-the  Australian National Climate Change Adaptation Research Facility Roundtable on Species Traits , the iDigBio ALA Traits workshop, and the preliminary Open Traits workshop held at the  Ecological Society of America. R. V. G. is supported by an  Australian Research Council   DECRA Fellowship  ( DE170100208 ). D. S. F. is supported by an  Australian Research Council   Future Fellowship  ( FT160100113 ). R. S. -G. is supported by  NERC   R/142195-11-1. W. D. P. is supported by  NSF   ABI-1759965 ,  NSF   EF-1802605 , and  USDA Forest Service  agreement  18-CS-11046000-041. A. K. received financial support for M. J. A. by the  German Research Foundation  ( DFG KE1743/7-1 ). C. M. I. was supported by the  Biological and Environmental Research program  in the  United States Department of Energy's Office of Science.
The search was conducted according to the following main criteria specifying time-frame, databases and search terms: Time frame: 2011 to 2017 (the rationale for this is that 2011 saw the beginning of the Syrian and Libyan revolutions accompanied by an increase in the flow of migrants towards the EU 28 countries) 1 The MigHealthCare consortium includes 14 partners from 10 European countries among which universities, Non-Governmental Organisations (NGOS) and national authorities with a composite range of expertise on health, migration, equality and integration issues. Databases: PubMed, ScienceDirect, Scopus, PsycInfo, Social Services Abstracts (also in ProQuest), Cochrane library, CABI. Eurostat, OECD, Eurofound, CORDIS and any other databases that are available in the different languages included in the search.
We found 170 peptides oscillating in controls (Fig. 1e  and Supplementary Table  2 ), i. e. the same percentage (11%) as for gene transcripts. Nine proteins were oscillating in both proteomic and transcriptome datasets, which partly originates from the fact that 3% of peptides have a correspondence in the transcriptome dataset. Together these results demonstrate that 10% of the gene/protein map in the hippocampus of control mice undergoes a continuous remapping during the night/day cycle. We then performed the same analysis in age-matched TLE mice in order to determine whether such circadian regulation was maintained in pathological conditions.
Functional analysis The enriched terms reported from DAVID bioinformatics for oscillating genes found in control condition alone, epileptic condition alone, and oscillating in both conditions for three different p-certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted April 6, 2020. ; https://doi. org/10. 1101/199372 doi: bioRxiv preprint values are listed in Supplementary Tables 5-13. Many biological pathways, including those linked to metabolism, showed circadian regulation in control and TLE, and specific alterations in TLE as revealed by KEGG pathways gene sets, Gene Ontology biological process, Gene Ontology metabolic function and REACTOME pathway database (Supplementary Fig 3  -14 , Supplementary Table  14 15 16 17 18 19 20 21 22 23 24 25.
Consequences for antiepileptic drug targets and chronotherapy Another possible use of such resource is for drug target design and chronotherapy. Many common drugs (e. g. for gastritis, chronic pain, asthma, and rheumatoid arthritis) target products of genes characterized by circadian rhythmicity  21. We obtained information on drugs and genes from DrugBank database version 5. 0. 6  30  , which contains information about 8283 drugs including 62 drugs that exert antiepileptic and anticonvulsant effects. In the database, we found mammalian genes encoding 2230 drug targets, 35 drug carriers, 238 drug enzymes and 130 drug transporters. For the 62 antiepileptic/anticonvulsant drugs, we found genes encoding 102 drug targets, 1 drug carrier, 32 drug enzymes and 18 drug transporters. Among the genes that oscillate in control and TLE animals (JTK_CYCLE adjusted p < 0.
05), we found 24 and 71 drug targets, 2 and 2 drug carriers, 2 and 7 drug enzymes, and 2 and 9 drug transporters, respectively (Table  2 ). There was little overlap between control and experimental TLE datasets, further highlighting the necessity to take into account the reorganization of gene variations that is specific to experimental TLE. We obtained similar conclusions when looking at genes which expression changes (one-way ANOVA, FDR < 0. 05) certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted April 6, 2020. ; https://doi. org/10. 1101/199372 doi: bioRxiv preprint 9 in at least one time point in control and TLE animals. We found 20 and 62 drug targets, 2 and 0 drug carriers, 2 and 11 drug enzymes, and 2 and 7 drug transporters, respectively (Table  2 ).
Additionally, we performed two PTZ seizure threshold determinations at the same time-point (ZT11:30) to compare PTZ certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint (which was not this version posted April 6, 2020. ; https://doi. org/10. 1101/199372 doi: bioRxiv preprint 4 thresholds at the beginning and at the end of the experiment, demonstrating the reproducibility of the method. Data were analyzed by using the Prism5 software from GraphPad (La Jolla, CA, USA). Thresholds of control and SE animals were analyzed separately using one-way analysis of variance (ANOVA) and Bonferroni post tests and combined by using a two-way ANOVA with Student's t-tests as post hoc analysis. All tests were used two-sided; a P < 0.
Quantitative PCRs were run on the ABI 79OOHT Fast Real-Time PCR instrument (Applied Biosystems). Gene expression analysis was conducted using a relative standard curve method with glyceraldehyde-3phosphate dehydrogenase (Gapdh) to normalise the expression levels of target genes. All reactions were performed in triplicates. certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Bioinformatic analysis. Analysis of microarrays was performed using R/Bioconductor (R Core Team, 2014; www. bioconductor. org). All microarrays were normalized with the Robust Multi-array Average (RMA) algorithm using oligo package (version 1. 28. 3)  9. Background value of intensity of probes was defined as median value of the intensity of antigenomic probes. The intensity of genomic probes below the background intensity was corrected to this background value.
Probes with intensity greater than background value in less than four samples were removed from analysis. Only probes that corresponded to a single gene were selected for further analysis. JTK_Cycle and BIO_CYCLE were used to identify oscillating genes at three p-value thresholds  10. Gene lists for transcripts oscillating at each p-value in the control condition alone, the TLE condition alone, or both conditions were identified for further analysis. Cyber-T was used to identify differentially expressed timepoints between control and TLE conditions for each animal  11. Cyber-T p-values found for each time point for core clock genes are reported (Figure  2C ). Proteomic data for both control and TLE conditions was also analyzed by JTK_Cycle to identify rhythmic proteins. All transcriptome data is available for search on Circadiomics along with JTK_Cycle and BIO_CYCLE results  1.
(http://david. abcc. ncifcrf. gov;  12, 13 ). Transcription factor binding sites overrepresented in promoters within clusters were detected with gProfiler (http://biit. cs. ut. ee/gprofiler/;  14, 15  ) with custom background defined as genes detected in the experiment to reveal the functional and regulatory groups presented in the data. Further transcription factor binding site enrichment analysis was performed using MotifMap  16, 17  and CHiP-seq datasets provided by UCSC Encode project  18. Transcription factor binding sites identified by MotifMap were found both 10 KB upstream and 2 KB downstream of transcription start sites (TSS) for all genes in each of the differentially expressed gene lists for all pair-wise comparisons. The motifs identified were found with a BBLS conservation score of greater than or equal to 1.
0 and an FDR of less than 0. 25. CHiP-seq datasets were used to locate experimentally identified binding site locations within the same interval around the TSS for differentially expressed genes. Only the 90th percentile of CHiP-seq peaks were used from each dataset provided by UCSC Encode. A Fisher's exact test was performed separately using the full background list of motifs from MotifMap and all target genes from the CHiP-seq datasets to compute the statistical likelihood of over representation in promotor regions.
Fragmented peptide ions were excluded from repeat analysis for 15 s. Raw data processing and analyses of database searches were performed with Proteome Discoverer software 1. 4. 1. 12 (Thermo Fisher Scientific). Peptide identification was done with an in house Mascot server version 2. 4. 1 (Matrix Science Ltd, London, UK). MS2 data (including a-series ions) were searched against mouse sequences from SwissProt (release certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.