dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions and future perspectives Resources such as MassIVE. Quant, ExpressionAtlas, and Pro-teomicsDB have recently started to systematically reanalyze proteomics public quantitative datasets  4, 11, 19. However, the lack of sample metadata that allow associating sample properties with data files makes this task complex and unscalable. Capturing metadata is challenging, since this generic term ranges from concepts such as the title of the experiment to sample-related information, to technical details like the instrument-configuration parameters used for data acquisition. To represent these different levels of proteomics metadata, MAGE-TAB-Proteomics builds upon a popular and flexible data format adopted from transcriptomics that relies on IDF and SDRF files. The IDF-Proteomics format closely resembles the PX XML format that PX resources have long been used to capture dataset information.
The SDRF-Proteomics format has a schema and data model that allows submitters to provide the minimum information about the samples as well as a more complete record of their sample and RAW file metadata. The SDRF templates define the minimum information PX partners have agreed upon, which should be provided for each submitted dataset. Additionally, the file format enables users and submitters to describe in much higher detail the sample, sample-processing steps, and the RAW files. Owing to this flexibility, MAGE-TAB is compatible with a wide range of experimental approaches (Table  1 ) and can be easily adapted to changing metadata requirements in the future. Importantly, the MAGE-TAB-Proteomics project is supported by the worldleading proteomics databases in PX. Due to the gradual and iterative implementation of MAGE-TAB-Proteomics into PX submission pipelines and related tools, the adoption of the standard will not create a major additional burden for users.
To support new users, we have created a tutorial in GitHub (https:// github. com/bigbio/proteomics-metadata-standard/wiki) and a video that introduces the file format (https://www. youtube. com/ watch?v=TMDu_yTzYQM). MAGE-TAB-Proteomics will facilitate the integration and annotation of proteomics studies, thereby enhancing their discoverability, reproducibility, and reusability. It holds great potential to facilitate the use of standardized workflows for the automated reanalysis of currently available and future annotated public proteomics datasets. This will increase the inherent value of proteomics datasets by making them more amenable for largescale meta-analysis, systems biology, and multiomics projects that can take advantage of large hardware resources in cloud-based environments. The proposed standard metadata representation will also facilitate the development of common submission systems to streamline the deposition of multiomics datasets to resources for different types of omics data.
It may even foster the creation of new multiomics resources, enabling submission, validation, and visualization of multi-omics data in one place  33, 34. As an initial step in this direction, the PRIDE group has started to reanalyze proteomics datasets represented in SDRF and integrates them into the multiomics resource Expression Atlas (e. g. , https:// www. ebi. ac. uk/gxa/experiments/E-PROT-18/Results). Expanding the MAGE-TAB-Proteomics format to additional use cases and more types of MS-based experiments, including metaproteomics and xenograft proteomics experimental designs, is currently under discussion (https://github. com/bigbio/ proteomics-metadata-standard/blob/master/sdrf-proteomics/usecases-under-development. adoc). Researchers interested in use cases that are currently not supported are encouraged to bring them to our attention by creating an issue in GitHub using the above hyperlink.
Analogous to the implementation process of other PSI formats, we aim to engage software tools and developers to support the MAGE-TAB-Proteomics format as an input file for data processing and as output file for the datasetsubmission process. Notably, the MAGE-TAB-Proteomics format has already triggered interest in the MS metabolomics field. Firstmetabolomics data have already been annotated using MAGE-TAB-Proteomics (Table  1 ) and more metabolomics datasets coming from the ReDU resource  35  have been added to the annotation repository, underscoring the open, collaborative, and community-driven approach of this project. In this spirit, we invite all interested parties to join the MAGE-TAB-Proteomics initiative.
Acknowledgements YPR, SK, DJK, and JAV would like to acknowledge funding from the  Wellcome Trust  grant number  208391/Z/17/Z  and EMBL core funding. MLP is supported financially by the  Novo Nordisk Foundation  (Grant agreement  NNF14CC0001 ). MT and TS are supported by  de. NBI , a project of the  German Federal Ministry of Education and Research (BMBF)  [grant number  FKZ 031 A 534 A  and  FKZ 031 A 535  A]. ME and JU are members of the  Center for Protein Diagnostics (PRODI) , a grant from the  Ministry of Innovation, Science, and Research of North-Rhine Westphalia, Germany. TVDB is supported by the  Research Foundation-Flanders  (SB grant  1S90918N ). EWD acknowledges  NIGMS  grants  R01GM087221 ,  R24GM127667 , and  NSF  grants  1933311  and  1922871. SS was supported by the  NSF  grant  1817518.
Label-free PXD008934 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD008934 TMT, CPTAC dataset not in PX https://cptac-data-portal. georgetown. edu/ study-summary/S029 36 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PMID33212010 SILAC PXD006877 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD006877 Phospho-proteomics PXD006482 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD006482 Label-free, multiple fragmentation modes and various enzymes PXD010154 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD010154 AP-MS interactomics PXD018117 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD018117 TMT PXD017710 https://github.
com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD017710 Label-free PXD004242 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD004242 DIA PXD003539 https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/PXD003539 Metabolomics MSV000086206 [https://doi. org/ doi:10. 25345/C5HV0S] https://github. com/bigbio/proteomics-metadatastandard/tree/master/annotated-projects/ MSV000086206.
Data availability The annotated datasets generated in this study are provided in GitHub (https:// github. com/bigbio/proteomics-metadata-standard/tree/master/annotated-projects). The raw data corresponding to the example datasets shown in Table  1  are available under the accession codes PXD008934, PXD006877, PXD006482, PXD010154, PXD018117, PXD017710, PXD004242, PXD003539, MSV000086206 [https://doi. org/doi:10. 25345/ C5HV0S]. The raw data of the TMT/CPTAC dataset are available at https://cptac-dataportal. georgetown. edu/study-summary/S029.
Code availability General documentation and tutorials as well as a script to generate IDF files are provided in Github (https://github. com/bigbio/proteomics-metadata-standard); however, please note that IDF files are also automatically generated during the PRIDE submission process. The code of the sdrf-pipelines tool developed in this study is deposited in GitHub (https://github. com/bigbio/sdrf-pipelines). The jSDRF Java library is available at https://github. com/bigbio/jsdrf. The Bio-MAGETAB Perl package is available at https:// metacpan. org/release/Bio-MAGETAB.
PRIDE database-submission workflow supporting IDF and SDRF files. The IDF file is automatically generated during submission; the SDRF file can be provided by the user in the PRIDE Submission Tool and is automatically validated in the submission pipeline. The sample information is shown on the web page of each PRIDE dataset and submitted to the EBI BioSample database, which assigns a unique accession to each sample. Shown are representative PRIDE and BioSamples outputs for the dataset PXD000561.
Methods: Re-uses of RCT data published up to December 2019 (cases) were searched for by two reviewers on 3 repositories (CSDR, YODA project, and Vivli) and matched to control papers published in the same journal. The Altmetric Attention Score (primary outcome), components of this score (e. g. mention of policy sources, media attention) and the total number of citations were compared between these two groups. Results: 89 re-uses were identified: 48 (53. 9%) secondary analyses, 34 (38. 2%) meta-analyses, 4 (4. 5%) methodological analyses and 3 (3. 4%) re-analyses. The median (interquartile range) Altmetric Attention Scores were 5. 9 (1. 3-22. 2) for re-use and 2. 8 (0. 3-12. 3) for controls (p = 0. 14). No statistical difference was found on any of the components of in the Altmetric Attention Score.
Cases (Re-uses) All published re-uses on 3 data repositories were assessed: clinicalstudydatarequest. com (CSDR), Yale University Open Data Access (YODA), and Vivli. These data repositories were chosen because they were identified as the 3 main data repositories in a previous scoping review  [20]. CSDR, funded by a consortium of clinical funders/ sponsors, seeks to be "the researcher-preferred and trusted" platform for responsible sharing of high-quality patient-level data for the purpose of facilitating innovative data-driven research leading to improvements in patient care. As of 31 January 2020 there was 2123 RCTs listed. The YODA project, funded by Johnson & Johnson (formerly funded by Medtronic Inc), "advocates the responsible sharing of clinical research data, open science, and research transparency".
The number of RCTs listed was 334 in July 2019. Vivli runs a data-sharing platform that includes an independent-access general data repository which is available to search for data from clinical trials conducted by researchers in academic institutions, industry, foundations and non-profit entities that can be hosted, shared and accessed. The number of RCTs listed was 4920 in March 2020. These 3 repositories, as part of their metrics, detail all published outputs related to the randomized clinical trial (RCT) data that they host  [20]. Any re-use mentioned on these platforms and published in a peer review journal in English, French, German or Spanish between the creation of the platforms and 31 st December 2019 was considered. Re-uses published only as part of congress proceedings were not included nor were re-uses not listed on PubMed.
Primary publications A "primary publication" group was also set up, including publications from which the re-uses were derived. For a given re-used RCT, a "primary publication" (i. e. the main publication of the dataset that was re-used) was defined as the first article reporting fully the primary outcome of the study. In many cases (e. g. IPD meta-analyses), there were several primary publications per re-use. Also, some primary publications were related to several different re-uses. These primary publications were sought on the data-sharing platform, the clinical trial register (e. g. clinicaltrials. gov) and the manufacturer website.
Study identification Matching and data extraction occurred in February 2020. Data on Altmetric metrics and the number of citations were extracted on the 10th of April 2020, as part of the final analysis. Sixty-three re-uses were extracted from CSDR and 28 from YODA. There were no re-uses found on the VIVLI repository. After deletion of duplicates and matching, a total of 89 re-uses (with 197 primary RCTs identified) and 89 matched controls was obtained (Fig. 1 ).
Contributorship statements were introduced by scholarly journals in the late 1990s to provide more details on the specific contributions made by authors to research papers. After more than a decade of idiosyncratic taxonomies by journals, a partnership between medical journals and standards organizations has led to the establishment, in 2015, of the Contributor Roles Taxonomy (CRediT), which provides a standardized set of 14 research contributions. Using the data from Public Library of Science (PLOS) journals over the 2017-2018 period (N = 30,054 papers), this paper analyzes how research contributions are divided across research teams, focusing on the association between division of labor and number of authors, and authors' position and specific contributions. It also assesses whether some contributions are more likely to be performed in conjunction with others and examines how the new taxonomy provides greater insight into the gendered nature of labor division.
Corrêa, Silva et al. (2017) -also using the PLOS ONE data set-confirmed this uncertainty between authors' order and contributions made. Using a network-based approach, they found that the relationship becomes increasingly random as the number of authors per paper increases. They also provided evidence of how division of labor increases as the number of authors increases and showed that contributions can be grouped into three categories: those who write, those who perform data analysis, and those who conduct experiments. These studies provided novel insight on the relationship between authorship and one coarsegrained contributorship taxonomy. However, the previously used five contributorship categories fail to account for the complexity of contemporary science. To address the need for a more refined taxonomy, an International Workshop on Contributorship and Scholarly Attribution was organized at Harvard in May 2012 at the initiative of the Wellcome Trust (IWCSA, 2012).
One outcome was a pilot project involving publishers, funders, and scientists to design a cross-disciplinary standardized taxonomy for contributor roles and contribution types, which would be practicable for all scientific fields. The goal was to be interoperable with different databases and to reduce the many ambiguities that remain with earlier contributorship typologies. In the eyes of its promoters, this standardized taxonomy would not only codify the contributions of each researcher with fine granularity, allowing for specific skills to be easily identified, but also rely on an infrastructure to manage the complex relationships between the information, its archiving, and its consultation in real time. An initial prototype comprised of 14 types of contribution roles was designed and tested among corresponding authors of work published in various (mostly biomedical) journals  (Allen, Scott et al.
Writing-review & editing Preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision-including pre-or postpublication stages. BMJ-have adopted it or, in the case of major publishers, have seen some of their journals adopt it. By early 2019, more than 120 journals had implemented the taxonomy  (Allen et al. , 2019) , a number that increased substantially at the end of 2019 with the adoption of the typology by 1,200 journals from Elsevier (Elsevier, 2019). Our analysis is based on one of these publishers-PLOS-which provided us with all of its contributorship information for papers published between June 15, 2017 and December 31, 2018 (N = 30,770). The data covered all PLOS journals and included publication date, Digital Object Identifier (DOI), journal name, author name as it appears on the paper, and associated CRediT contributions for each author 3.
Given the strong focus on medical sciences of the multidisciplinary journal PLOS ONE  (Siler et al. , 2020)  and of other PLOS journals, the results need to be interpreted as illustrative of the use of the CRediT taxonomy in those disciplines. Contribution information provided by PLOS did not, however, contain author order; to obtain this information we had to match each PLOS paper with its record in our in-house version of Clarivate Analytics' Web of Science based on the DOI; this was feasible for 30,054 papers (97. 7% of the PLOS data set; see authorships. Once the papers were matched with the WoS, we matched each author in both data sources to obtain their individual order in the authors' list. This was first based on a perfect match of the full name string (e. g. , Derek John de Solla Price = Derek John de Solla Price).
However, as several names could not be matched because they were written in different manners in both databases (e. g. , Derek de Solla Price, Derek J. Price, Derek Price), we performed additional matching focusing on specific parts of the name string. More specifically, we iteratively focused on the first and last 2-5 characters of the names; this allowed us to match 221,637 authorships (99. 4% of the sample). For this subset of authors who could be attributed an author order, we assigned a gender based on their given names. Such gender assignation of researchers has become a relatively standard practice and was shown to obtain relatively high precision and recall  (Karimi, Wagner et al. , 2016; Santamaría & Mihaljevic , 2018). In this paper, we used the algorithm developed in  Larivière, Ni, et al.
(2013) , which was created using several country-level lists of given names along with their gender. The algorithm has been tested for precision, and was found to be 98. 3% precise for men and 86. 7% for women (see the supplementary material in  Larivière et al. (2013)  for more details). The algorithm assigned a gender to 82. 2% of the authorships covered in this analysis (Table  3 ). This percentage varies by author order, however, with a higher proportion of last authors assigned a gender, and a lower proportion of first authors. The percentage of female authorships in the PLOS data set represents 39. 9% of authorships to which a gender could be assigned, which is slightly greater than the percentage of female authorships found in the WoS for disciplines of the medical sciences (about 35%).
Formal analysis, data curation, and validation were, on average, performed by 42-45% of authors across all PLOS journals, with higher percentages of authors contributing to formal analysis at PLOS Computational Biology and PLOS Genetics, as well as a higher share of authors contributing to validation at PLOS Computational Biology. Contrary to what was observed in the previous typology used by PLOS  (Larivière et al. , 2016) , where more than half of authors (and as much as 80% in social sciences and physics, among others) had "written the paper," the writing of the original draft is a contribution done by a much narrower percentage of authors (39% across all PLOS journals). Tasks typically performed by principal investigators (resources, supervision, project administration, and funding acquisition), as well as contributions than can be considered to be more specialized (visualization and software) are performed by a minority of authors (between 31% and 38%), with higher percentages of visualization and software for PLOS Computational Biology.
4 A different editorial system for PLOS Biology made it difficult for PLOS to provide us with the data for this journal. Therefore, while the PLOS Biology contributorship data is included in the global analysis, individual data for the journal is not provided (i. e. , Figures1 and 2). Table 2. Number of papers published with CRediT contributions, mean number of authors and mean number of CRediT contributions per paper, by PLOS journal Journal No. papers No. papers in WoS % papers in WoS Mean No.
The aim of this study is to maintain up-to-date information about the current state of elderly people that are medically followed for risks of fall. Our proposal consists of an individual information database management system that can provide information on-demand on various variables. Such a system has to deal with several sources of uncertainty: lack of information, evolving information and reliability of the information sources. We consider that the features of the person may evolve with time causing uncertainty due to obsolete information. Our context includes new information received bit by bit, with no possibility to collect all required information at once. This paper establishes a first proposal to manage a set of uncertain observations, in order to reduce erroneous and obsolete information while keeping the benefit of previously collected information.
Main question and general approach The main question can be formulated as: what information can be provided by the system on a requested variable when there is no available observation on that variable in the observation set, or when the available observation may be out of date, and/or when the observation comes from an unsure source? In order to answer this question, information to consider comes both from specific observations realized on the patient and from generic knowledge to complete as much as possible the information that is provided. Generic knowledge concerns the dependency relations between the variables, their variability in the considered population, and their evolution over time, partly provided by experts and partly learned from related data. The data file that we used for the construction of the general knowledge model consists of information about 435 variables for 1173 patients who attended the Lille University Hospital Falls Clinic between January 2005 and December 2014.
We conducted a study of these data in collaboration with Lille hospital's experts on fall prevention. This study resulted in a taxonomy of the fall risk factors, represented by an ontology  [24] , and the selection of 45 relevant variables associated with the characteristics of the person, the main risk factors for fall and possible causes or consequences of them. From this result, we built and evaluated a first model of the generic knowledge, embedded in a Bayesian network  [25]. Specific observations concern one patient in particular and are made of collected data about this specific person. The observation set includes uncertain information about any subset of variables of the knowledge model. Among the 45 selected variables, some are frequently observed, meaning that corresponding information is frequently present in the observation set, whereas other variables are rarely observed.
Although four sets of 2D binary images were available, one for each of the four different flow rates, only the two extreme values v = 5 and v = 50 were considered in determining the estimators of the joint probability density distributions S 5 and S 50. The optimal values of (µ, d) that minimize the cost function and maximize the likelihood, given the local perimeter measurements, can be found by minimizing the cost function, M v (µ, S) defined by equation (  7 ), M v (µ, d) = M v (µ, S v (d)), (9) Fig. 7  shows the joint probability distributions S 5 and S 50 obtained using an implementation of the Nelder-Mead method in MATLAB TM  (Lagarias et al. (1998) ), along with the images generated by the STORMFLOW model using these joint probability distributions as first input. The last step is to define an interpolation function: s : [0; 1] → S (10) so that s(t) = (1 -t) × S 5 + t × S 50.
Wind speed and electricity prices data We consider a time horizon of 168 hours, which corresponds to the week of August 25-31, 2014. The ARIMA model uses the same structure and fit of the model proposed in  [19]. The electricity price time series has 12 weeks before the week studied, from the Iberian Peninsula electricity market  [54]. The raw data for the wind speed for a specific wind farm location consists of a wind speed ensemble with 51 members obtained from the European Centre for Medium Range Weather Forecasts (ECMWF). We use the wind speed ensemble and the KLE to generate additional samples of the wind speed for the SAA methodology. The electricity prices are considered independent of the wind speed available at the location of the considered VPP, due to the small capacity of the VPP in each case study, which has no market power to influence the electricity prices.