dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Description Phase 1 Phase 1 of data collection ended in August 2019. A static version of the database was archived and made available to collaborators through the website (sgp-search. io) and via tailored queries. Time was allowed for vetting and any errors discovered were corrected before the final freeze in February 2020. The Phase 1 data freeze includes 82,578 samples, with 2,701,236 analytical results, and was made public through our search website in December 2020. This paper should be cited in the future use of Phase 1 data downloads. More complete information on the Phase 1 data product can be found on the SGP wiki (https://github. com/ufarrell/sgp_phase1/wiki), including summaries by age, lithology and geochemical methodology, as well as the specifics of how USGS databases were incorporated into the SGP structure.
SGP The SGP-contributed dataset includes 20,811 samples with 518,291 results. Approximately two thirds of the data (64%) come from 160 published sources (https://github. com/ufarrell/sgp_phase1/wiki/SGP-data-references). The remaining 36% are from unpublished sources, including new and legacy data. The samples come from 942 individual sites from 46 countries (Fig. 3 ). Consistent with the Phase 1 goals, 84% of samples were from the Neoproterozoic-Paleozoic (Fig. 4 ). Sixty-four percent of samples are fine-grained siliciclastic rocks (shale, mudstone or siltstone), as are the majority of uncoded lithologies (Fig. 5 ).
USGS NGDB The data from USGS NGDB that are incorporated into the SGP database include 48,234 samples with 1,769,696 results. Nearly all (99%) of the samples are from the United States. Nineteen percent are sandstone, 13% are shale, and 29% do not have a specific lithology (although lithological details may be available in verbatim fields; Fig. 5 ). Contextual details, including depositional environment and low-grade metamorphic bin, are mostly not available for these samples, and methodological information is sparse. In general, the USGS NGDB samples skew younger than the SGP samples: 39% are from the Paleozoic, 25% from the Mesozoic and 33% from the Cenozoic (~3% of samples are from the Proterozoic/Archean). The USGS database provides excellent coverage of the United States, but given the remit of the organization, with strong focus on economic deposits (petroleum-producing units, phosphatic units, sedimentary mineral deposits), the sampling may not be representative of the entire country.
This is distinct from the bias present in geochemical data produced by academic researchers, which are often focused on mass extinction intervals, Earth system perturbations, and other stratigraphic boundaries. this is a small percentage now, we anticipate continued growth given the popularity and utility of handheld XRFs. A 'samples' search will list an individual sample on each row, with geological context information and geochemical analytes taking up the columns. Data are converted to one standard unit, oxides are converted to elements (e. g. , Al2O3 to Al), and values are averaged if more than one analysis was made per sample. Note this search may average values produced using different analytical methods, although the number of samples in the database with multiple analytical values for a specific analyte is relatively small.
Further, any analyses below or above detection limit are removed, as these cannot be averaged. This has implications for queries involving very low abundance elements (e. g. , Ag in sedimentary rocks), as only results above detection limits, and thus higher values, will be included. We anticipate that this search will produce the optimal data output for most end-users interested in Earth history: a file with age, geological context and geochemical data for each sample. If users are looking to delve deeper into the data and understand the analyses and procedures that were executed to obtain each sample's geochemical data, then the 'analyses' search is useful because it lists every analysis recorded in the database in a separate row. The 'analyses' search also allows users to show data relating to the lab where the sample was analyzed, the person who made the measurement, geochemical methodology, etc.
At the current time, aside from the ability to exclude handheld XRF data, the 'samples' and 'nhhxrf' search types will not report information about, or have the ability to filter by, geochemical methodology. Users who are interested in methodological details or who would like to export a data file beyond the size limit (10 Mb) should contact the SGP Leadership Team regarding a custom SQL query. Once the user has selected a search type, samples can be filtered based on both geological context and geochemical attributes. Note that for many samples some aspects of geological contextual information are incomplete. Thus, for example, a search filtering for samples deposited in a rift basin will only return samples positively described as such and not necessarily all samples in the database deposited in rift basins.
Given that samples will have non-overlapping missing data, too many filters may result in a smaller-than-expected dataset. Search results will appear in a 'preview' window that can be used to check the output. Each sample also has an information icon associated with it; clicking this icon will bring up a lightbox with detailed sample information. Finally, the user may request to show reference information for their search. For 'analyses' searches (where every analysis is shown as an individual row), this will return the specific literature citation for that analytic result. For other search types, this will return, for every sample, a concatenated list of all references whose geochemical data contributed to that specific search. When the user is satisfied with their search, they can then download a.
csv file of the data and export a map showing the location and age of samples in their search. The SGP website uses an API to interact with the database, and users can make a copy of the API call using the API icon next to their search results. However, users can also bypass the user interface entirely and access data via a direct API call. This comprises three parts: • type: Selects the search type (samples, analyses or nhhxrf) • filters: Contains a list of search options that are logically ANDed in the results • show: Contains search options that determine which columns will appear in the results Thus, an example API call would be: {"type":"samples","filters":{"country":["Argentina","Brazil","Chile","Bolivia","Colombia","Ven ezuela"],"toc":[2,100]},"show":["toc","fe","height_meters","section_name","country","interprete d_age"]} This API call is making a 'samples' type search for samples that originate from Argentina, Brazil, Chile, Bolivia, Colombia, or Venezuela and have 2-100% total organic carbon (TOC) content.
Future goals and directions The overarching goal of SGP is to provide intellectual and geoinformatic resources for the Earth Science community to advance our understanding of environmental changes on Earth through time. A better understanding of Earth's history requires sufficient data density, but equally importantly it means training a new generation of researchers with the data science and statistical skills to make meaningful conclusions from large sedimentary geochemical datasets. Much of the focus in SGP Phase 1 was in initiating the consortium and increasing the data product to the point where it was useful for analyses by the community. We aim now to increasingly move towards developing a community-initiated set of best practices for data management, a culture of publishing metadata, and a shared intellectual framework for analyzing such datasets.
Over the course of Phase 2 we plan to continue holding annual meetings at Goldschmidt, while also beginning regular video calls to share progress and ideas for data analysis, as well as accessible geochemistry 'short courses' to help the geobiological community understand the strengths and weaknesses of different proxies. Beyond these broad community and educational goals, we have the following more concrete goals during SGP Phase 2: • Expand the geological and geographic scope of samples in our database. Most samples with complete context information (SGP direct entry), and indeed most samples, are Neoproterozoic-Paleozoic in age and from North America (Fig. 4 ). Younger and older samples, and worldwide sampling, are necessary for accurate analyses through the full swath of Earth history.
• Expand the carbonate geochemical record. Our database structure is appropriate for carbonate data (and indeed, >8000 carbonate samples are already in the database). However, this goal will require community discussion regarding how best to incorporate methodologies and phase-specific analyses. • Continue correcting errors in previously entered data. Although we have been as careful as possible during data entry, mistakes are inevitable in a dataset of this size. Paleobiological analyses and basic statistical logic suggest that such mistakes (random error) will not affect results as long as they are not biased (systematic error)  (Sepkoski, 1993). Nonetheless, we would like to present the most accurate results, and we welcome users to notify us of true errors (rather than geologic disagreement) that are found during their database searches.
Acknowledgements We thank  Sufian Lattouf  for developing the initial version of the SGP website, and  Kai Lenz ,  Kassie Sharp ,  Aaron Cole ,  Clare Swan ,  Lyna Kim , and  John Freshwaters  for computational assistance. We thank  Erin Saupe ,  Itay Halevy ,  Jordon Hemingway ,  Minming Cui ,  Maya Gomes ,  Matthew Granitto ,  Alf Lenz ,  Charles Henderson ,  Chengsheng Jin ,  Clint Scott ,  David Champion ,.
The counts are then arranged into a matrix, where the rows are the features (here, the miRNAs), the columns are the samples, and the cells are the number of reads that match a given feature, in a given sample. This matrix is then used as an input of the standard (messenger) RNA-Seq protocol for differential expression, where differential expression is tested. Many tools have been presented to perform this last step, but DESeq2  [5]  and edgeR  [6]  are, by far, the most cited ones. This method has been used, for instance, by  [7] , who analyzed the sRNA-Seq data of lung tumors compared to adjacent normal tissues. This method can obviously only find features that are previous detected, and usually restrict the analysis to only one, or few, classes of small RNAs. The second option has been for instance used by an other article, that reused the previous dataset in order to find differentially expressed snoRNAs and piRNAs  [8].
Here, the authors followed the usual pipe-line of the messenger RNA-Seq. They mapped the reads to the genome, and compared the mapped reads with external annotations, here piRNAs (from piR-NABank  [9] ), and snoRNAs (from UCSC genome browser annotation  [10] ). The authors claimed that the approach is more exhaustive, since -especially in human-the annotation files which are provided by the existing repositories include a wide diversity of small RNAs. While this approach works fine on some well characterized sRNAs, such as miRNAs, tRFs, or snoRNAs, they are much less efficient for others, such as siRNAs or piRNAs, because the corresponding "genes" are not known (and possibly even not defined). Indeed, current piRNA databases are usually restricted to human and mouse, and their accuracy is not well established.
Some popular tools for sRNA differential expression, such as UEA sRNA Workbench  [11]  and sRNAtoolBox  [12]  use a combination of the two previous methods. While these method works fine for miRNAs, and other well-know sRNAs, they cannot detect other types of sRNAs, or be applied to an unannotated genome. It is, at least in principle, always possible to complement the annotation of the genome of interest using tools that detect a class of small RNAs de novo: tRNAscan-SE  [13]  for tRNAs, Snoscan  [14]  for snoRNAs, or infeRNAl  [15]  for small RNAs described in the RFAM database  [16]  are the most widely used tools. However, this requires expertise, as well as computational resources. It is, in practice, rarely done, and still restricts the analysis to well-characterized small RNAs. It is also possible to cluster unknown, co-localizing reads to putative sRNAs.
The dispersion is estimated using the mean, and is expected to follow a trend for the majority of the genes. If many genes depart from the trend, the expression is clearly poorly estimated. In Fig 1a in S1 Appendix, we show that, on the human dataset, the dispersion plot is close to the expected one. This confirms that the For the sake of memory compactness, the coverage is stored as a vector of pairs (coverage, length) per chromosome. B: The naïve method. In a first step, the samples are averaged for each condition, then the (log2) fold change is computed. All those regions with a fold change not less than n are kept. Regions distant than no more than d base pairs are then merged, and given as output of the method. C: First step of the HMM method. The coverage of each nucleotide is computed for every condition.
F: The merge step. Each interval i 1 to i 3 , is associated to a p-value, written on the right of the interval. A naïve approach would discard i 2 and i 3 because they are dominated by i 1 and i 2 respectively. However, i 3 may be an interesting interval, although the signal is not as strong as the signal of i 1. We can notice that i 2 both dominates (i 3 ) and is dominated (by i 1 ). Only this interval is discarded. https://doi. org/10. 1371/journal. pone. 0256196. g002 estimation step was correctly carried out. Second, we plotted the p-value distribution. The pvalue is expected to be uniform under the null hypothesis, and close to 0 under the alternative hypothesis. The Fig 1b in S1 Appendix confirms that we observe a mixture of the two distributions on the human dataset. In a second step, we build a hidden Markov model (HMM) on each chromosome, where the first state is "differentially expressed," the second state is "not differentially expressed," and the observations are the p-values (see Fig  2D ).
Implementation of srnadiff Example of use. srnadiff can be installed through R Bioconductor  [21]. As every Bioconductor package, it has a dedicated Web page (https://bioconductor. org/packages/release/bioc/ html/srnadiff. html) and contains an extensive description of the tool. We provide here an introduction on how to use the package. The information about the dataset can be conveniently stored in a data. frame. This table should contain three columns, and each row describes a sample. The columns provide the name of the BAM files, the name of the sample, and the condition (e. g. wild_type vs mutant). If the table is stored into a file named data. csv, the minimal code to run srnadiff is: 1 library(srnadiff) 2 data <-read. csv("data. csv") 3 bamFiles <-file. path(dir, data $FileName) 4 exp <-srnadiffExp(bamFiles = bamFiles, 5 sampleInfo = data, 6 annotReg = "annotation.
Benchmarking We benchmarked srnadiff on three real, already published, datasets, and a synthetic one. We selected several datasets meeting the following criteria. First, the experimental design should include replicates in each condition. Second, the differentially expressed elements found by the authors should be accessible as supplementary data of the article. Third, we wanted to include a variety of model organisms: Homo Sapiens, Arabidopsis thaliana and Drosophila melanogaster. Fourth, the sequencing machines should be different, in order to include the diversity of the machines in our benchmark. Last, the analysis pipe-lines should also be different. All the publications provided a list of differentially expressed sRNAs, and we compared the different methods with this list of sRNAs. Of note, it is not possible to assess, on real-life datasets, the number of false positives, i.
e. the regions detected by the evaluated tools which are not differentially expressed, because it is not possible to find all the differentially expressed small RNAs. Therefore, metrics such as specificity or precision cannot be collected on these datasets. The aim of the benchmark on published datasets is to test whether the evaluated tools are able to detect all the regions that have been classified as differentially expressed by the reference methods. In order to assess the number of false positives, we added a last, simulated, dataset. srnadiff was run with no annotation, and an adjusted p-value threshold of 5%. We also run derfinder  [20]  on the same datasets, with a q-value of 10%. We used a third method, which first clusters the reads with ShortStack  [19]  (comparing several clustering methods is out of the scope of this article), quantified the expression of the regions found by ShortStack with feature-Counts, tested for differential expression with DESeq2, and kept the regions with an adjusted p-value of at most 5%.
We refer to this method as the ShortStack method. The reason why we chose a q-value of 10% for derfinder, instead of 5%, is that the statistics produced by derfinder is significantly more conservative, and it produces much fewer predicted regions than other approaches. For a fair comparison, we decided to lower the stringency for this tool. No fold change threshold was applied. We compared the results of the methods with respect to each other. We also compared srnadiff with another straightforward method: we downloaded available annotations of different sRNA-producing loci, and followed the previously presented method: expression quantification, and test for differential expression. These regions can be considered as true positives. For the human dataset, miRNAs were taken from miRBase  [4] , tRNAs (in order to find tRFs) from GtRNAdb  [26] , piRNAs from piRBase  [27] , snoRNAs from Ensembl  [28] , and genes (to find possible degradation products), from Ensembl too.
We added new additional databases: tRNAs and snoRNAs from UCSC  [10] , and piRNAs from https://www. pirnadb. org for an additional validation. We also used miRDeep2  [29]  in order to find putative new miRNAs using the sequencing data. The cress data was extracted from the TAIR annotation file  [30] , and from the FlyBase annotation file  [31]  for the fly. We then compared these differentially expressed regions with the predicted regions. We stated that the two predicted regions A and B were similar when at least 80% of A overlaps with B, or at least 80% of B overlaps with A. The reason is that some annotation (such as genes, or tRNAs which are substentially largers that tRFs) are not expected to be differentially expressed. Moreover, ShortStack provides also significantly larger differentially expressed regions than srnadiff or derfinder.
For each tool, we plotted different results. First, we provided the size and the adjusted pvalue distributions of the regions found. Then, we provided the number of differentially expressed features (e. g. differentially expressed miRNAs, tRFs, etc. ) which overlap a given region found. The aim here was to test whether a method would "merge" several potential candidates into a unique, longer, differentially expressed region. Then, we focused on the regions found by srnadiff and another tool (derfinder or ShortStack). For each such region, we compared its size, and its (adjusted) p-value found by each method. The code used for the benchmarking and the versions of the tools used are given in S1 Appendix.
Datasets Preprocessing Published datasets were downloaded from SRA  [32]  using the SRA Toolkit. We cleaned the data with fastx_clipper (http://hannonlab. cshl. edu/fastx_toolkit/index. html), mapped them with bowtie  [33]  (because it was ranked favorably in a recent benchmark  [34] ). Human dataset. The first dataset compared healthy cells vs tumor cells of lungs of smokers. This dataset has been sequenced on a Illumina GA-IIx, and contains 6 replicates per condition, with about 26 millions reads per sample. It has been published by  [7] , and re-analyzed by  [8]. Both papers analyzed the sRNA-Seq data of lung tumors compared to adjacent normal tissues. In the first paper, the authors mapped the reads with bowtie  [33]  on miRBase  [4]. The miR-NAs were tested for differential expressed with edgeR  [6].
A. thaliana dataset This dataset evaluated the difference of expression of small RNAs in two different concentrations of CO 2 in A. thaliana  [36]. Each condition contained two replicates, with about 11 millions reads each. The authors mapped the reads with RazerS  [37]  on the reference genome. Reads co-localizing with known miRNAs were kept for further analysis. The authors then looked for other, unknown putative miRNAs. Reads overlapping with known cDNAs, miRNAs, tRNAs, rRNAs, snoRNAs, and other RNA sequences, were excluded. Remaining reads were then given to miRDeep  [38]  and miRCat  [39] , two miRNA finding tools. The new set was added to the previous, known set of miRNAs. Differential expression analysis was performed with a χ 2 test.
D. melanogaster dataset In this dataset,  [40]  sequenced small RNAs of young and aged D. melanogaster flies circulating in the hemolymph. Each condition contained 8 and 4 replicates, with an average of 13 millions reads each. The authors mapped the reads with bowtie on the reference genome. Detection of new miRNAs, and quantification of known and new miRNAs, was performed with miRDeep2, using data from miRBase. Micro RNAs were tested for differential expression with edgeR.
Synthetic dataset We also generated a synthetic dataset, extracted from the human genome. We first selected 1000 miRNAs from miRBase  [4] , and 1000 piRNAs from piRNABank  [9]. We randomly selected 100 upregulated miRNAs (2-fold change), 100 downregulated miRNAs (2-fold change), and the same for the piRNAs. We randomly assigned a baseline expression following a power law (k = 1. 5) on the miRNAs and the piRNAs, which reflected a low expression for most of the RNAs, and a few very expressed RNAs, as observed on our data. We generated 6 replicates per condition using the polyester package  [41]. Obviously some RNAs may be differentially expressed, but with a very low expression, they are almost impossible to detect. As a results, we quantified the expression of the features with featureCounts  [42] , tested for differential expression with DESeq2  [5] , and restricted to all the features with an adjusted p-value of at most 5%.
Human dataset Comparison to the truth set. The first article found 48 differentially expressed small RNAs, and the second, only 23 (see  Fig 3). Only 5 miRNAs were common in both analyses. We first wondered whether we could also detect these differentially expressed miRNAs using our method. Fig  4  shows the recall of the different methods. The recall (or sensitivity) is defined as TP TPþFN , where TP is the number of true positives, and FN is the number of false negatives. In our benchmark, a true positive is region which has been detected as differentially expressed by a reference set (here, the miRNAs published in the articles), such that at least 80% of the nucleotides overlap with a region detected as differentially expressed by srnadiff (or other evaluated tools such as derfinder, and ShortStack).
The main reason is that ShortStack accepts very large regions that may have a low p-value on the whole region even if the   [7]  and  [8]  for the H. sapiens dataset,  [36]  for the A. thaliana dataset, and  [40]   difference point-wise is not significant. The ShortStack method also finds a few more miRNAs found by the articles. However, srnadiff finds, in general, more than thrice as many regions. Then, we compared all the results with a "truth set" (see  Methods and Fig 4) , where we retrieved several annotations, performed differential expression, and kept the regions with an adjusted p-value of 5%. We found, for instance, 240 differentially expressed miRNAs, 95 differentially expressed tRFs, etc. srnadiff usually is the method that recovers the greatest number of regions, although ShortStack sometimes provides more.