dataesr/scientific-paragraphs-categorization huggingface dataset:
The Editors may consider questions such as, 'Does data need to be redacted in order to prevent harm and protect or preserve life?' or, 'Is personal information being shared without consent?' For more information about the Editorial process for reviewing data quality and ethical considerations, see the section 'Editorial and peer review of STARDIT reports' of the Additional file 1 'STARDIT Manual Beta Version'. Once approved by the Editors, the STARDIT data will be entered into the database in a machine-readable format using structured data, based on the widely used Resource Description Framework (RDF) developed by the World Wide Web Consortium (W3C), which is used by Wikidata  [111]. Each STARDIT report is assigned a unique Wikidata item number and all previous versions are navigable in a transparent history.
In future versions, it is proposed that stakeholders will be able to submit reports directly via an application programming interface (API), which will facilitate machine automation of STARDIT report creation. In addition, machine learning algorithms could be programmed to generate STARDIT reports from existing databases. As humans and machines submit reports, categories or meta-tags will be suggested (such as 'patient' , 'member of the public'), with the option of adding, or co-defining, new categories using the Wikidata system for structured data  [112]. The database will generate a unique version number for the report with a Digital Object Identifier (DOI). To create an immutable version, the report will also be using the Internet Archive (a charity which allows archives of the World Wide Web to be created, searched and accessed for free)  [113].
Updating reports STARDIT will enable reports to be updated as initiatives progress over time. Updates will be reviewed by the STARDIT Editors. Once an update is approved, the system generates a new version number, while also preserving the original report. Updates might include, for example, information about involvement in the initiative, or about dissemination, translation, co-creation of new metrics to assess impacts, or longer-term outcomes  [114]. A minimum dataset is required for a STARDIT report. This is called the Minimum Contribution Report (MICRO) and the required categories are highlighted in green and marked with an asterisk (*). Relevant Wikidata items and qualifiers for these fields are provided in the Additional file 1 in the section 'Developing taxonomies and ontologies' and on the Science for All STARDIT Beta webpage  [109].
The STARDIT Preference Mapping (STARDIT-PM) tool provides a standardised way to report the preference of multiple stakeholders. Anyone can be involved in creating a STARDIT report, which means that data on the impacts and outcomes of participation can be contributed by diverse stakeholders. Such reports will help researchers make informed decisions when planning participation in research. For example, a recent study showed how a charity for people affected by a rare disease involved a small number of people affected by the rare disease. They were involved in discussing preferences for how best to involve the wider community of people affected in future research prioritisation and planning  [42]. Those involved had a good understanding of any specific needs or preferences for involvement, and shared preferences for the tasks (such as overseeing data access), method (facilitated discussions) and mode of involvement (online text-based discussion).
As a consistent mapping tool for use across all initiatives, STARDIT would allow both comparison of diverse stakeholder views and exploration of similarities and variations in relation to preferences for involvement. Used alongside other planning tools, this information could help align initiatives with stakeholders' preferences. In this way, how stakeholders are involved throughout an initiative could be co-designed from the outset. Analysis of the data about preferences should involve stakeholders from multiple groupings to ensure that a diversity of perspectives are involved in assigning meaning to any data.
STARDIT is the first tool that enables sharing of standardised data about initiatives across disciplines. It enables reporting of who was involved, any impacts of stakeholders' involvement, and outcomes of initiatives over time. This functionality addresses a serious limitation of the current peer-reviewed publication process in which articles are not easily updated. However, there is no single process for making decisions that would improve and refine the processes, language and taxonomies associated with reporting initiatives, including who was involved in which tasks  [150]. Similarly, based on feedback from Indigenous community leaders, patient representatives and others, it is essential to ensure access to learning and development opportunities is available to support people to both access and create STARDIT reports.
Future versions should be informed by a regular, systematic search, review and appraisal processes, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) data set  [151] , used for reporting in systematic reviews and meta-analyses. While there are multiple methods for mapping values  [152, 153] , there is currently no agreed, standardised way to map the values (beliefs and personal ethics) of those involved in initiatives and those creating reports in STARDIT. Further research is needed to facilitate mapping of values and detect whether certain perspectives are being consciously or unconsciously excluded. To establish that person's views on which stage of the research the specified groups of people should be involved in STARDIT seeks to be an easy-to-use way for people from multiple disciplines to share data about initiatives.
However, amassing sufficient reports to create a useful database is estimated to take at least 5 years, and will likely require machine learning. For example, machine learning may be used in parallel with humans (for verifying data) to generate STARDIT reports from existing publicly accessible data at a scale and speed otherwise impossible for humans alone to achieve. In addition, both the potential and limits of machine learning should be transparently reviewed in relation to the field of adversarial machine learning  [154]. Similarly, the process of creating 'living systematic reviews' from STARDIT reports is currently theoretical and would require significant development and rigorous testing to realise. It is important to note that access to Wikidata is actively blocked by governments or internet service providers in some countries.
While such censorship limits people's ability to contribute or critically appraise data, STARDIT has been designed to be both interoperable with existing standards, and 'future proofed' by being system and language agnostic, to allow interoperability with existing and emerging data systems beyond Wikidata. Science for All will continue to host the co-creation process and to monitor and evaluate the project. However, an open, transparent governance process that enables anyone to be involved in decision making and ongoing co-design of STARDIT will need to be established, and is proposed in more detail in the Additional file 1. Ensuring that the STARDIT development process is inclusive and ethical, and that the database is quality assured, is paramount to ensuring that STARDIT is credible, useful and trustworthy.
Conclusion This article summarises work to date on developing Standardised Data on Initiatives (STARDIT), an open access web-based data-sharing system for standardising the way that information about initiatives is reported across diverse fields and disciplines. It provides a way to collate and appraise data about how different people have been involved in different tasks of multiple types of initiatives. The current usage by multiple initiatives demonstrates to usability of STARDIT, and will inform the next stages of development. In accordance with the principles of transparent participatory action research, the authors invite the involvement of any interested persons in developing and improving the next version of STARDIT, Version 1. 0. Detailed and up-to-date information about STARDIT is available on the Science for All website (Sci-enceforAll.
Acknowledgements The authors wish to acknowledge the support the following people have given to the STARDIT project so far. The following people have agreed to be named as project supporters (in chronological order of project support):  (1)   The authors would also like to thank: Pauline Sanders for her valuable assistance in editing versions of the Beta manuscript Pauline Stobbs for her assistance in editing versions of the abstract and plain English summary Saloni Swaminathan Aidan Levy for their valuable assistance in supporting the associated scoping review and collating data  [86]  Paul Lacaze for his valuable input throughout the STARDIT project so far, including recommending the use of the Minimum Contribution Reporting (MICRO) Dave McCall for his pro-bono support with designing logos and brand guides for STARDIT Alicia Merrick (Secretary, Science for All) for her support throughout this project The charity Science for All, and all the volunteers for supporting with hosting and facilitating online discussions and providing practical support to the project.
CD8 + T EM expressing GZMK have a distinct transcriptional program and correlate with worse outcome To better characterize infiltrating GZMK high CD8 + T EM cells and define their prognostic potential, paired T and NAT tissues from 8 HN patients were profiled by single cell RNA sequencing (scRNAseq). We collected data from a total of 52316 cells. Mean reads per cell were 94183 (min:37500; max:235694) with a median number of genes detected per cell of 3779 (min:1433; max:5392). Sequencing saturation was >49% for all samples, indicating comprehensive sampling of the available transcripts. After processing and normalization, dimensionality reduction was performed with uniform manifold approximation and projection (UMAP). Shared nearest neighbor (SNN) clustering distinguished eleven different clusters that were annotated to nine broad cell lineages, including epithelial (EPCAM + ), endothelial (CD34 + ), mesenchymal (MMP2 + ), B cells (MZB1 + ), and T/NK (CD3D + ) (Supplementary Fig.
The presence of polymorphisms affecting C and/or G in the CpG was indeed observed in 2352 out of 3252 methyl-Kit DMCs, suggesting that in our dataset, this algorithm tended to select genetic rather than epigenetic variations. At these sites, the methylation differences between fertility groups were probably emphasized by a biased distribution of genotypes, resulting in a marked intra-group inter-individual variability that precluded their identification as DMCs by DSS but not by methylKit. To suppress the polymorphisms affecting CpGs that could interfere with DMC detection, all putative variants recorded in the 1000 Bull Genome database and targeting the CpGs covered by RRBS were filtered out, resulting in a background of 1,548,563 CpGs10 covered in at least 22 samples per fertility group (22 samples representing half of the smallest group).
Using methylKit, 490 DMCs and 46 differentially methylated regions (DMRs) were identified, which contrasts with no DMCs being obtained with DSS, and suggests that sources of intra-group interindividual variations still existed in our dataset. This was also visible from the hierarchical clustering run on the CpGs10 before and after the filtering of sequence variants, which demonstrates that inter-individual epigenetic variations unrelated to fertility shape the sperm methylome independently of the presence of polymorphisms (Additional file 1: Fig. S1A, E ). These inter-individual epigenetic variations were clearly not due to the presence of variable amounts of somatic cells in the samples (Additional file 1: Fig. S2 ). Although a certain degree of overlap existed between the fertile and subfertile bulls, they (See figure on next page.
To increase the number of genes in the analysis at this step, all DMCs were considered independently of their hypo-or hyper-methylation status in subfertile bulls. A Database for Annotation, Visualization and Integrated Discovery (DAVID) analysis highlighted a unique cluster of enrichment that reached significance (EASE score of 1. 84, 1. 3 being the lower limit for significance; Fig. 5A ). Fig. 5  Differentially methylated genes are relevant to male fertility. A functional enrichment analysis was performed using DAVID, where genes containing DMCs (170 genes) were compared to those covered in the background CpGs10 (19,829 genes). The enrichment cluster with an EASE score of 1. 84 is represented, with genes in the cluster displayed on the x-axis and the different terms on the y-axis. A correspondence between a gene and a term is indicated by a light blue color on the heatmap.
B A systematic review of the literature was undertaken for all genes associated with DMCs that contained a gene name (n = 139). The NCBI Pubmed database was interrogated using the gene names successively associated with each of the following terms: "embryo*", "sperm*" and "fertility". Some genes matched with several terms and are represented in several categories, explaining why the total number of counts exceeded the number of unique genes of interest (46 unique genes) Three genes located on three different chromosomes, PLXNA4 (chromosome 4), PLXNB2 (chromosome 5) and PLXNA1 (chromosome 22), which belong to the Plexin family, were associated with all the terms in the cluster. Plexins are present on the surface of cells and interact with the semaphorin family of proteins to trigger a rearrangement of the cytoskeleton and to regulate cell shape, differentiation, junctions, motility and survival.
The CpGs analyzed by pyrosequencing are numbered according to their 5′-3′ position along the genome. The CpGs identified as fertility-related DMCs are indicated in black text and red boxes, while non-DMCs are indicated in grey. B For each gene region, the average methylation percentage measured by pyrosequencing (y-axis) was calculated for the DMCs included in the region and plotted against the average methylation percentage measured by RRBS at the same DMCs (x-axis). Each dot represents one sample from the fertile (in red, n = 9 to 10) and subfertile (in blue, n = 9-10) groups. The least squares lines of best fit and Spearman's rank R correlation coefficients are indicated. All correlations were highly significant (Spearman's rank correlation test; p < 0. between 0. 88 and 0. 97; p < 0. 05), thus validating our RRBS data from a technical perspective (Fig.
For the model construction and validation, the cohort was split randomly into a training set (2/3 of the animals) and a testing set (1/3) with the same proportion of fertile and subfertile bulls as in the original dataset (Fig. 7A ). Using a Random Forest approach, which is described as suitable for molecular biology data  [58] , a model was then built using the training set, and the prediction was assessed by comparing the estimated fertility and actual fertility of the testing set. The average performances of the model were calculated from 50 iterations of this process, with resampling of the training and testing sets at each iteration. This cross-validation indicated satisfactory performance, with values for the area under the receiver operating characteristics (ROC) curve (AUC) and accuracy of 0.
Confidence ellipses are indicated. Misclassified bulls were found in the fertile class only and displayed a DNA methylation pattern similar to that of the subfertile class largest cohorts of any mammals, including humans. Field fertility was very precisely assessed for 120 bulls using two different indicators and we also demonstrated that the experimental design was devoid of any obvious confounding effects. Using this valuable resource, we generated high quality and technically validated genome-wide DNA methylation data using RRBS. Our main finding was that although they did not differ in terms of semen functional parameters at 17-19 months of age, fertile and subfertile bulls displayed subtle DNA methylation differences in the same semen samples, and these differences could be used successfully to build models predictive of fertility status for the whole career of the bulls.
To our knowledge, this study represents the most comprehensive attempt to model bull fertility from the sperm methylome, which was probably enabled by the large size of the main cohort. Inadequate sample size has indeed been proposed as a major obstacle to replicating fertilityrelated DNA methylation signatures in human sperm  [29]. In line with this view, the important inter-individual variability we report here makes it unlikely that the methylation differences previously reported between small numbers of bulls of contrasting fertility (n = 3-9 per group;  [35] [36] [37] [38] [39] [40] ) can reflect those in a larger population with less extreme differences in fertility, thus limiting modelling approaches adapted from these datasets. Only Takeda et al. [39]  confirmed the differential methylation between low and high fertility bulls using a wider population (n = 50) at 10 loci and were able to construct a predictive model that has now to be assessed on an independent cohort.
Bull cohorts and semen samples Semen samples from 120 French Montbéliarde bulls, grouped in two independent cohorts, were used for this study. All samples were prepared from frozen semen straws commercialized for AI and stored in liquid nitrogen. The main cohort was used to generate DNA methylation data using RRBS, identify fertility-related DMCs and construct and validate the predictive model. This included 100 bulls born between 2011 and 2014 and commercialized by the breeding companies Umotest and Evajura, and maintained in two different semen collection centers located 100 km distant from each other in France (n = 56, center 1 and n = 44, center 2), 57 of which were classified as fertile and 43 as subfertile. Fertility was defined at the bull level based on the non-return rate at 56 days post-insemination (NRR 56), obtained as follows.
After a further wash in phosphate buffer saline (PBS 1×), the sperm pellets were resuspended in PBS 1× and divided into aliquots for various experimental purposes. The equivalent of one straw (20 million sperm cells) was used for DNA extraction. The independent cohort was used to produce RRBS data and evaluate the potential of the model built on the main cohort for field applications. This included 20 bulls born between 2009 and 2012 and marketed by the breeding company Umotest. One ejaculate was analyzed per bull, whose age at semen collection ranged from 15 to 39 months. Ejaculates were defined as fertile (n = 16) or subfertile (n = 4) based on the corrected NRR 56 calculated for each bull and as explained above for the main cohort. One straw was used to assess semen functional parameters and extract DNA, as described above.
Semen functional parameters Semen motility was assessed by computer-assisted semen analysis (CASA, IVOS II, Hamilton Thorne, IMV Technologies). Five µL out of 15 µL pooled semen were mixed with 10 µL Easy buffer B (IMV Technologies) and incubated for 5 min at 37 °C. After incubation, 4 µL of this mix was loaded into a standardized four-chamber counting slide (Leja), which was then placed into the CASA system. Sperm motility was averaged from 10 microscope fields analyzed using the predetermined starting point set within each chamber. Results were expressed as percentages of motile sperm in the sample. Sperm viability and mitochondrial status were assessed from 5000 cells using the easyCyte 8HT flow cytometer and CytoSoft software (Guava, IMV Technologies). For sperm viability, membrane integrity was assessed using EasyKit 1 Viability and Concentration (IMV Technologies) that stains viable spermatozoa with intact membranes in green.
Results were expressed as percentages of viable sperm in the sample. Sperm mitochondrial status was assessed using the EasyKit 2 (IMV Technologies) and expressed as the percentage of polarized mitochondria that appeared in orange fluorescence. Both kits were used as described by Sellem et al. [92]. In order to account for variations between the different series of experiments, the data were corrected for the batch effect using a linear model, with the sample preparation batch (Additional file 2: Table  S1 ) set as a fixed effect. Residuals of this model were extracted for further analyses. The results obtained for viability, mitochondrial status and motility, with and without correction for the batch effect, are provided in Additional file 3: Table  S2  and Additional file 1: Fig. S8.
PCA and hierarchical clustering were then computed on the matrix of methylation percentages for each CpG10 with no missing values, using the FactoMineR R package  [98]. For hierarchical clustering, the distance between samples was calculated using Pearson correlation coefficients and Ward's method was applied as the linkage function. A subset of 1,548,563 CpGs10, covered in at least 22 samples per fertility group of the main cohort, constituted the background. The DMCs were identified from this background using methylKit v1. 0. 0  [49]  and DSS v2. 14. 0  [48]  in the default mode. With methylKit, a CpG10 was considered as a DMC when the adjusted p value (q value, obtained after SLIM correction) was lower than 0. 01 and the average methylation difference between the two groups was at least 10% according to the methylKit calculation mode, which takes account of the coverage per sample.
With DSS, the same minimal methylation difference applied, but the threshold for the adjusted p value was set at 0. 1, and p value adjustment was performed according to the Independent Hypothesis Weighting method, using the alpha parameter set at 5% and the average methylation per group as a covariable  [99]. DMRs were defined as regions containing at least 3 DMCs with an inter-distance between each DMC of 100 bp or less. It should be noted that both the DMC and DMR datasets contained missing values because of samples in which the coverage thresholds were not reached. For Fig. 2C , an average DNA methylation value per DMR and per sample was calculated from all the CpGs10 included in the DMR. The heatmap was then generated for 18 DMRs displaying DNA methylation values for all 100 samples using the R package pheatmap (v1.
The lists of fertility-related DMCs and DMRs with annotation features are provided in Additional files 4 and 5: Tables  S3  and S4 , respectively. Genes containing DMCs in intragenic regions and/ or in the upstream (up to -10 kb from the TSS) and downstream (up to + 10 kb from the TTS) regions were subjected to an enrichment analysis using DAVID with default parameters  [100]. The 19,829 genes covered by RRBS (i. e. , all genes containing at least one background CpG10) were used as the reference. Clusters with an enrichment score above 1. 3 were taken into account. To better characterize repetitive elements, an artificial genome containing the consensus sequence of each bovine repeat was constituted from the Repbase database  [46]. Reads were aligned on this artificial genome as explained above, and the average methylation rate was calculated per CpG10 for each repeat and each sample.
3 µM pyrosequencing primer, using the Pyromark Q24 device and Pyromark Gold Q96 reagents (Qiagen). Each CpG was analyzed in duplicate, and inconsistent duplicates (more than 5% difference) were repeated. For each sample, the DNA methylation percentage per CpG was obtained by calculating the mean of all consistent replicates that passed quality control by the Pyromark Q24 software. DNA methylation values obtained by pyrosequencing were compared between fertile and subfertile bulls using a Wilcoxon test. The significance of correlations between the average DNA methylation values obtained by RRBS and pyrosequencing for each region was tested using Spearman's rank correlation test.