dataesr/scientific-paragraphs-categorization huggingface dataset:
Usually these repositories promote models of access that are open by default with restrictions or embargos possible for some type of contents. Data are usually stored and accessed for free as costs are covered by funders in the project main funding. Other repositories focus on services for sharing data that support the findings reported in journal articles and other scholarly publications. Such repositories may gain in importance, as medical journals are increasingly demanding a commitment to share data collected in clinical trials  [15]. For instance, in Dryad, all material is associated with a scholarly publication. The repository covers all research areas, including medicine, and hosts IPD from clinical studies published in medical journals. Similarly, Figshare is a repository system, available both as a Cloud-hosted service or integrated with local storage, that provides services for storing and make visible data objects linked to the published data of several scientific publishers, such as PLoS, F1000, Nature, and Wiley.
Figshare is also one of the systems providing data repositories for individual universities, often as a hosted service and sometimes integrated with local university systems. Universities increasingly rely on data repositories to improve the visibility of their research output with institution specific repositories, usually restricting upload of data and documents to the university's own staff. The Data Repository for the University of Minnesota (DRUM) and Edinburgh DataShare are two examples included in our analysis, although several other academic repositories may be available to store IPD  [27]. Three repositories were identified with a national scope, in terms of the source of the data and those able to upload it: UMIN (University Medical Information Network) in Japan, EASY (Electronic Archiving System) in the Netherlands, and the Swedish National Data Service (SND), though the latter has accepted a small proportion of data from elsewhere.
In addition, the NIH repositories described earlier are clearly mostly, if not exclusively, intended for US-based researchers. Figure  2  is an attempt to summarise the suitability of each repository for hosting IPD from clinical studies, by evaluating it against the key attributes listed earlier (see Additional file 2 for details). None of the repositories demonstrated completely all the items we included in the indicator set; however, three repositories (Dryad, DRUM, EASY) were judged as demonstrating or partially demonstrating all the items. All three are generic repositories. In the case of four repositories (SND, Edinburgh DataShare, Vivli, and Open Science Framework) we did not judge any of the considered items as not demonstrated, but we were not able to retrieve the information needed to judge one or more items.
Summary of main findings The study has shown that an increasing number of repositories are available for sharing of IPD from clinical studies. There are many different types of repositories, such as generic repositories for all kinds of life-science data, generic repositories exclusively for clinical research data and specific repositories for study data with a specific focus (e. g. disease-specific, stakeholder-specific, institutional-specific). Only two repositories may be considered 'general clinical research' , the Japanese National Repository linked to the National Registry for Clinical Trials (UMIN), and Vivli that was launched in 2018. This scenario may reflect the fact that data sharing in clinical researchwhilst encouraged and accepted in principle by many for some time  [1-4, 22, 28]  has only been given a strong push towards implementation in recent years, especially with data-sharing requirements defined by funders  [9] [10] [11]  and journal editors  [15]  changing the expectations about data release.
Given the lack of a consensus about the services required from a data repository, each organisation has implemented its own policies and systems to meet its own priorities. This may create major hurdles if datasets from different repositories are needed for a research project. The disease-specific repositories have often evolved from particular collaborations or research communities; the more general repositories from conscious decisions by funders, academic organisations and occasionally governments aiming at supporting 'open science'. The data suggest that the availability of different options for data access could be improved. Only seven of the 25 repositories offered two or more clear choices, though a further five did offer a limited choice (e. g. adding an embargo period to open access, or two different forms of managed access).
Despite our attempts to identify all the possible candidates to be included in our analysis we could have missed important initiatives, such as those promoted in languages other than English or those not having a strong web presence. Thus, we do not claim that the sample of repositories is complete, though we believe that it is representative of the current situation. Though we started by evaluating a relatively large sample of candidate repositories (55), the scoping rule (suitability for data deposition by non-commercial researchers) resulted in a final sample of 25. Platforms that enable access to industry trial datasets, such as CSDR and YODA, now also list studies from non-commercial sponsors, so they may become valuable options too. We based our evaluation of repository features on an arbitrary indicator set intended to indicate the likelihood that data and documents would be stored securely but flexibly and shared appropriately.
About 70% of the clinical trial units responding to a survey done in 2015 in the UK would be prepared, in principle, to transfer data to a central repository  [33]. Rathi et al. surveyed authors of clinical trials published in 2010 or 2011 in the six highest-impact general medical journals and found that only 18% of the respondents were required to deposit their trial data in a repository, and of these, half had actually done so  [34]. Respondents were also asked about concerns with sharing data through repositories, and more than three quarters reported actual or hypothetical concerns, mainly concerning the misuse of data  [35]. Other analyses explored the actual commitment to IPD sharing. Very few (5%) of the studies registered at ClinicalTrials. gov in 2016-2017 by high-volume registrants provided a data-sharing plan  [36]  and only 4% of the consent forms of a sample of antibiotic studies, both from public and industry funders, reported a commitment to share IPD  [37].
In dealing with these issues, data repositories may play a role in improving the support for managing sensitive datasets and, specifically, clinical research data. The process of defining a clear description of a 'high-quality repository' for clinical research data is still in its initial stages; a set of suitability standards or indicators built on the standards already proposed would need to be specific enough to be assessable and be agreed by the key stakeholders (e. g. research producing organisations, funders, and repositories) as appropriate. Standard instruments (e. g. checklists) for assessment of maturity and suitability of repositories could be of major help. Resource constraints would mean that any assessment process would probably involve the repositories themselves constructing an evidence portfolio against the criteria, similar to the current CoreTrustSeal scheme for general repositories  [23].
Final remarks Our study provides a detailedthough preliminarypicture of the available repositories that may, in principle, host clinical study data. Some useful conclusions and implications for future works can be drawn; however, it seems too early to provide a conclusive answer to the central question of the studywhere could non-commercial researchers store their datasets? With few exceptions, e. g. a researcher working in an area served by a disease-specific repository or in a country that has a national repository service available, the choice is likely to involve a trade-off between different desirable features. The use of data repositories can bring several advantages to the data depositor, such as the provision of secure long-term management of the data long after the original research team has disbanded, the application of identifiers to all material so that it is accurately cited and the original data generators are properly acknowledged when data are re-used, help in preparing data for sharing, and in managing the sometimes complex processes of applying for and granting access.
io/ scientific Virginia (USA) (Laura and John Arnold Foundation) 9 Swedish National Data Social and National University of Gothenburg Public/Academia Anyone (mainly Service, https://snd. gu. se/en health sciences (Sweden) (university consortium) Swedish researchers) UMIN, Clinical study National (Japan) University Hospital Medical Public/Academia Investigators of 10 http://www. umin. ac. jp/icdr/ Information Network - (trial registry) trials registered University of Tokyo Hospital Vivli, https://vivli. org/ Clinical study Global Center for global Clinical Non-profit Anyone 11 Research Data, Harvard organisation Zenodo, General Global CERN (Geneva) Public/Academia Anyone 12 https://zenodo. org scientific (EU Commission and other partners) Disease specific CancerData. Org, https:// Cancer National Maastro Clinic Health care corporate Not clear 13 www.
nhlbi. nih. gov/ blood diseases and Blood Institute funded-project home/ ProAct, https://nctu. Amyotrophic Global Prize4Life Israel & Not-for-profit/Pharma Data donated by 23 partners. org/ProACT lateral sclerosis Massachusetts project partners General Hospital TBI-IMPACT, http://www. tbi- Traumatic Global Antwerp, Rotterdam Public/Academia Not clear 24 impact. org/ brain injury and Edinburgh WWARN, www. wwarn. org/ Malaria and Global University of Oxford Public/Charity Anyone (malaria) 25 other infectious diseases Acronyms: CERN Conseil européen pour la recherche nucléaire; DRUM Data Repository for the University of Minnesota; EASY Electronic Archiving System; EBCTCG Early Breast Cancer Trialists' Collaborative Group; ITN Immune Tolerance Network; NDACAN National Data Archive on Child Abuse and Neglect; NDCT National Database for Clinical Trials Related to Mental Illness; NHLBI National Heart, Lung, and Blood Institute; NIDDK National Institute of Diabetes and Digestive and Kidney Diseases; NIH National Institute of Health; NIMH National Institute of Mental Health; TBI Traumatic brain injury, UMIN University Medical Information Network; WWAAR WorldWide Antimalarial Resistance Network.
12-fold lower than CYP71A13. CYP79B2 and ATR1 were detected in the co-IP, but the respective enrichments (4. 5fold, p=0. 17 and 5. 0-fold, p=0. 022, respectively) were below the threshold of significance indicating weak or transient interaction with the observed CYP71B15containing protein complex. Interestingly, PDR12/ABCG40, which was recently identified as camalexin transporter  (He et al. , 2019)  was significantly enriched (7. 8fold; p=0. 00029), indicating that biosynthesis and transport of camalexin to some extent might be physically linked. For a comprehensive overview of the proteomics data, see Supplemental Dataset 1. To evaluate to which extent this result depends on the trigger of camalexin biosynthesis, IPs were also performed with UV-irradiated leaves. The general outcome was similar (Supplemental Figure  2B ): Besides the bait, which showed highest abundance, P450 enzymes such as CYP71A13, CYP83B1 and CYP71B6 were highly enriched, but also CYP79B2 and CYP71A12 were co-purified with CYP71B15 (Supplemental Dataset 1).
Screen for inducible physical interactors of CYP71A13 CYP71A13 was consistently identified as interactor in an untargeted screen with CYP71B15 as bait. As a complementary approach, a CYP71A13-YFP fusion protein was expressed in Arabidopsis under control of the 35S promoter. Microsomes of UVirradiated or untreated rosette leaves were isolated and solubilized, and a co-IP was performed to address (i) whether CYP71B15-CYP71A13 interaction is independent of the choice of baits, and (ii) which interaction partners specifically bind CYP71A13 in response to induction. A total of 875 proteins were reproducibly detected in the co-IPs of the UV-treated samples (Supplemental Dataset 2), including 26 cytochrome P450 enzymes, and the cytochrome P450 reductases ATR1 and ATR2. Constitutive expression of the bait allows to detect also binding partners under control conditions, where concentrations of CYP71A13 expressed under control of its native promoter are too low for quantitative work.
GSTU4 physically interacts with CYP71A13 and is relevant for the camalexin response In camalexin biosynthesis, activated IAN, presumably indole cyanohydrin, is glutathionylated, probably involving a GST  (Klein et al. , 2013). The untargeted co-IP screens (Supplemental datasets 1 and 2) revealed only few GSTs as proteins copurified with very low signal intensity. GSTF6, previously proposed to be involved in camalexin biosynthesis  (Su et al. , 2011)  was not detected. Possibly the interaction of the cytosolic GSTs with the P450 enzymes is not sufficiently strong to persist in presence of the applied Triton X-100 concentration. To evaluate which Arabidopsis GSTs are capable of this conversion, a qualitative screening was performed in a yeast strain in which four endogenous GSTs and three genes of glutathione conjugate catabolism were deleted (GTO1, GTO2, GTO3, TEF4, CPC, CPY, CIS2)  (Krajewski et al.
, 2013; Kowalski, 2016)  and in which expression plasmids for ATR1 and CYP71A13 were introduced. These yeast cells were transformed with each of the 54 Arabidopsis GSTs and after selection screened for biotransformation of IAN and glutathione yielding GS-IAN. When an empty vector was used instead of the CYP71A13 expression plasmid, no activity was detected. Also, when no Arabidopsis GST was expressed, no GS-IAN was synthesized. Strikingly, for 41 enzymes, including most of the phi-and tau-class GSTs, product formation was observed (Supplemental Figure  5 ). As an approach to identify which of the active GSTs is relevant in planta, transcriptomics data was surveyed for co-expression with camalexin biosynthetic genes. In particular, GSTU4 is strongly induced by pathogens and correlated with the genes of camalexin biosynthesis (CYP71B15, r=0.
, 2004; Sun et al. , 2012). In this case, an intermediate of the biosynthesis could be exported from the metabolon and metabolized. Alternatively, GSTU4 could have a regulatory function. The human GST Pi acts as inhibitor of Jun N-terminal kinase (JNK). In response to UV irradiation or H 2 O 2 treatment GSTp oligomerizes and dissociates from the GSTp-JNK complex  (Adler et al. , 1999). Whether such GST-dependent activation mechanism in response to stress is relevant also in Arabidopsis remains to be investigated. Also, it is unclear whether the interaction between P450/GSTU4 interaction is specific for the camalexin biosynthetic machinery or might play a more general role. In conclusion, CYP79B2, CYP71A12/A13, CYP71B15, and ATR1 form a metabolic complex (Fig. 10 ). FRET-FLIM data indicated, that, in addition, GGP1 can be recruited to this complex.
Based on the data of our untargeted co-IP screens ATR1 and CYP79B2 are likely to be less tightly associated with the core camalexin biosynthetic complex. This is in accordance with their different biological functions. ATR1 is required for many different biosynthetic processes in Arabidopsis. CYP79B2 is also involved in the biosynthesis of indole glucosinolates  (Hull et al. , 2000; Mikkelsen et al. , 2000) , the biosynthesis of auxin under specific conditions  (Brumos et al. , 2014; Tivendale et al. , 2014) , and the remodeling of root architecture  (Julkowska et al. , 2017). A possible interaction of CYP79B2 with CYP83B1, which is involved in indole glucosinolate biosynthesis and competes with CYP71A12/A13 for IAOx, was not detected in co-IP and split-ubiquitin-based yeast 2-hybrid screens  (Nintemann et al.
LC-MS/MS analysis of CYP71A13 experiments Peptides generated by in-gel trypsin digestion were analyzed via LC-MS/MS on a nanoLC-Ultra 1D+ (Eksigent, Dublin, CA) coupled to a Q Exactive HF mass spectrometer (ThermoFisher Scientific). Peptides were delivered to a trap column (75 µm x 2 cm, packed in house with Reprosil-Pur C18 ODS3 5 µm resin, Dr. Maisch) for 10 min at a flow rate of 5 µl/min in 100% solvent A 0 (0. 1% formic acid in HPLC grade water). Peptides were then separated on an analytical column (75 µm x 40 cm, packed in-house with Reprosil-Gold C18 120, 3 µm resin, Dr. Maisch) using a 120 min gradient ranging from 4-32% solvent B (0. 1% formic acid and 5% DMSO in acetonitrile) in A (0. 1% formic acid and 5% DMSO in HPLC grade water) at a flow rate of 300 nl/min. The mass spectrometer was operated in data dependent mode, automatically switching between MS and MS2 spectra.
Peptide and protein identification and quantification Label free quantification was performed using MaxQuant (version 1. 6. 1. 0) (Cox and Mann, 2008)  by searching MS data against an Arabidopsis thaliana reference database derived from UniProt (version 09. 07. 2016, 31424 entries) using the embedded search engine Andromeda  (Cox et al. , 2011). Carbamidomethylated cysteine was used as fixed modification; variable modifications included oxidation of methionine and N-terminal protein acetylation. Trypsin/P was specified as proteolytic enzyme with up to two allowed missed cleavage sites. Precursor tolerance was set to 10 ppm and fragment ion tolerance was set to 20 ppm. Label-free quantification  (Cox et al. , 2014) , match-between-runs and intensity-based absolute quantification options were enabled and results were filtered for a minimal length of seven amino acids, 1% peptide and protein FDR as well as common contaminants and reverse identifications.
Data analysis and visualization MaxQuant results were imported into the MaxQuant associated software suite Perseus (v. 1. 5. 8. 5)  (Tyanova and Cox, 2018). Label-free quantification intensities (LFQ) were filtered for at least 3 valid values for at least one experimental group and at least 3 peptides for identification per protein. Missing values were imputed from normal distribution (width 0. 2, downshift 2. 0). A two-sided unpaired student's t-test was performed to assess statistical significance. Protein p-values were corrected for multiple testing using a permutation based 1% FDR cut-off (1000 permutations). Standard functions in the SAM R-package were used to adjust s0 for each dataset  (Tusher et al. , 2001). For the CYP71B15 scatter plots proteins were filtered for at least 2 valid values for at least one experimental group and at least 3 peptides for identification per protein.
Data deposition Mass spectrometry data have been deposited to the ProteomeXchange Consortium (http://proteomecentral. proteomexchange. org) via the PRIDE partner repository  (Vizcaíno et al. , 2012)  with the dataset identifier PXD008812 (reviewer account: username: reviewer70359@ebi. ac. uk, password: MRXOTnDO). Yeast transformation, protein expression, yeast microsomes, enzyme analysis and yeast feeding experiments The Saccharomyces cerevisiae strain BY4741  (Brachmann et al. , 1998) , auxotroph for His, Leu, Met and Ura, was used for coexpression of ATR1 (on plasmid pGREG505), CYP79B2 (on plasmid pYeDP60) and CYP71A13 (on plasmid pSH62) or the corresponding vector control. Transformations were performed according to  Gietz et al. (1992). Yeasts were cultivated and microsomes were prepared essentially as described by  (Schuhegger et al.
, 2004). Determination of K m values was performed via GraphPad PrismGraph (Michaelis-Menten analysis). For feeding experiments yeasts carrying ATR1 (on plasmid pGREG505), CYP71A13 (on plasmid pYeDP60) and one out of 54 GSTs (on plasmid pSH62) were grown in SD medium with appropriate supplements (-His, -Leu, -Met, -Ura) until OD 600 = 0. 6 was reached. Protein expression was induced by addition of galactose for 16 h. Feeding was performed with 0. and 561 nm (RFP), and emission at 495-550 nm and 570-615 nm, respectively. Data was produced from at least three independent biological replicates, defined as separate plants independently infiltrated from glycerol stocks. At least twenty cells per combination were imaged in a randomized manner. FRET-FLIM analysis was performed according to  Kriechbaumer et al.
Targeted co-IP was performed with 10 µl GFP-Trap ® A beads each, in a total volume of 500 µl. The samples were analyzed via western blot using anti-FLAG (Sigma, F1804) and anti-GFP (Invitrogen, A-11122) antibody followed by staining with goat anti-mouse HRP (Bio-Rad, 172-1011) or goat anti-rabbit HRP (Life Technologies, 65-6120) respectively (dilution of all antibodies 1:3000). Replicates represent independent microsome preparations from independent plants. Protein identification by liquid chromatography and tandem mass spectrometry (LC-MS/MS)In-gel digestionProtein samples were reduced by 10 mM dithiothreitol, and alkylated by 55 mM iodoacetamide (CYP71B15 dataset) or 55 mM chloroacetamide (CYP71A13 dataset).
Acknowledgements We thank  Alfons Gierl  for hosting the Glawischnig lab during the early phase of the project,  Thomas Rauhut  for generation the CYP71B15-GFP construct,  Vasko Veljanovski  for generation of CYP79B2 and GGP1 expression constructs,  Alexandra Chapman ,  Verena Stork , and  Marion Lechner  for supporting co-IP establishment and generation of yeast strains, respectively,  Aaron Klepper  for providing homozygous gstu4 T-DNA-lines,  Ramón Torres Ruiz  ( CALM ) for supporting FRET-FLIM instrument handling, and  Danièle Werck  for providing CYP73A5-YFP construct as negative control. This work has been supported by the  Deutsche Forschungsgemeinschaft , grants  GL346/5  ( DFG Heisenberg Fellowship to Erich Glawischnig ) and  GL346/8 , the  Hans-Fischer-Gesellschaft  e. V. , the  TUM Junior Fellow Fund  and a  Science and Technology Facilities Council Program (FRET-FLIM  grant no.
Body weight measurements and fecal collection occurred prior to every flagellin administration. b-c Serum anti-flagellin IgA and IgG throughout the experiment, d-e fecal anti-flagellin IgA and IgG, f-g serum anti-flagellin IgA and IgG at day 56, h serum interleukin-6, and i CXCL1 at day 56 were analyzed using ELISA kits. j-k Fecal anti-flagellin IgA j and IgG k were also quantified up to 11 weeks after the final flagellin administration in mice receiving 6 weekly intraperitoneal injections of flagellin (10 μg per mouse). Data are the means ± S. E. M. Significance was determined using t test (**p ≤ 0. 01 ***p ≤ 0. 001 ****p ≤ 0. 0001, n. s. indicates non-significant) or using one-way ANOVA corrected for multiple comparisons with a Bonferroni test ( # p ≤ 0. 05 ## p ≤ 0. 01 ### p ≤ 0. 001 #### p ≤ 0.
16 S rRNA gene sequence analysis. Forward and reverse Illumina reads were joined using the fastq-join method  49, 50  , sequences were demultiplexed, quality filtered using Quantitative Insights Into Microbial Ecology (QIIME, version 1. 8. 0) software package  51. QIIME default parameters were used for quality filtering (reads truncated at first low-quality base and excluded if: (1) there were more than three consecutive low-quality base calls (2), < 75% of read length was consecutive highquality base calls (3), at least one uncalled base was present (4), > 1. 5 errors were present in the barcode (5), any Phred qualities were below 20, or (6) the length was < 75 bases). Sequences were clustered to operational taxonomic units (OTUs) using UCLUST algorithm  52  with a 97% threshold of pairwise identity (without the creation of new clusters with sequences that do not match the reference sequences), and taxonomically classified using the Greengenes reference database 13_8  53.