dataesr/scientific-paragraphs-categorization huggingface dataset:
In silico mixtures preparation The in silico simulated mixtures were computed as follows: firstly, weights for all included populations were chosen randomly. Pure transcriptomic profiles for all populations were computed with the expression of all genes being the mean expression over all the corresponding samples in the Haemopedia and ImmGen ULI datasets. Finally, the mixture transcriptome was computed as follows: T ¼ P Â C where T is the transcriptomic matrix with genes in lines and samples (mixtures) in columns, P is the pure profiles matrix with genes in lines and cell populations in columns, and C is the mixture composition matrix, with populations in lines and samples (mixtures) in column, the sum of each column being equal to 1. To evaluate the various scoring algorithms, 24 mixtures were simulated with random proportions of each cell population.
For the comparisons between mMCPcounter and other methods, 50 sets of mixtures were generated from the Haemopedia data (accessed as TPM- normalized data from www. haemosphere. org and log2transformed) and ImmGen ULI data (accessed as raw counts from Gene Expression Omnibus (accession code GSE109125), normalized at the 75th percentile and log2transformed). For the Haemopedia data, the random proportions for 50 mixtures were simulated using a Dirichlet distribution with shape parameters 2. 8 (CD8 T cells), 2. 2 (other T cells), 1. 8 (B cells), 0. 5 (monocytes), 1. 7 (macrophages), 0. 2 (mast cells), 0. 5 (eosinophils), 0. 3 (neutrophils). For the ImmGen ULI dataset mixtures, the shape parameters were set to 2. 8 (CD8 T cells), 0. 2 (gamma-delta T cells), 2 (other T cells), 0. 2 (NK cells), 0.
8 (memory B cells), 0. 8 (other B cells), 0. 5 (monocytes), 2 (macrophages), 0. 2 (mast cells), 0. 2 (eosinophils), and 0. 3 (neutrophils). Comparison with other published methods mMCP-counter, DCQ, and ImmuCC were run independently on each of the 50 sets of 50 mixtures defined above, aggregated by gene. The ImmuCC algorithm and signature matrix was accessed on GitHub (https:// github. com/chenziyi/ImmuCC) and ran on the mixtures locally. DCQ was run using the dcq function from the ComICS R package. All methods were run using default parameters. When the granularity of cell populations differed between mMCP-counter and ImmuCC or DCQ, to allow for comparisons, we forced a similar granularity, summing the scores of subpopulations corresponding to a larger population. For ImmuCC, populations "T Cells CD8 Actived," "T Cells CD8 Naive," "T Cells CD8 Memory," "T Cells CD4 Memory," "T Cells CD4 Naive," "T Cells CD4 Follicular," and "GammaDelta T Cells" were summed as "T cells"; "T Cells CD8 Actived," "T Cells CD8 Naive," and "T Cells CD8 Memory" were summed as "CD8 T cells"; "NK Resting" and "NK.
Spillover analysis For spillover, mean profiles for CD8 + T cells, B-derived cells, monocytes/macrophages, neutrophils, eosinophils, and mast cells from the Haemopedia dataset, and the above populations plus NK cells from the ImmGen ULI dataset were computed. All three methods were applied on these pure profiles, and the results were aggregated to higher-level cell populations as in the above paragraph. The noise ratio was computed with the following formula: noise ratio ¼ noise noise þ signal where noise is the sum of all off-targets scores (i. e. , scores for all other populations than what was included in each case) and signal is the sum of target scores (i. e. , the scores for the correct populations). For DCQ, we substracted the minimum value to all scores in order to only have positive values.
For Foxp3 staining, cells were fixed and permeabilized with eBioscience Foxp3/Transcription Factor Staining Buffer Set according to the manufacturer's protocol (eBioscience). All stainings were done with Fc block (BD Biosciences). Cells were then analyzed on a BD LSRFortessa cell analyzer (BD Bioscience). Flow cytometry data analysis was performed using Flowjo analysis platform (FlowJo, LLC). Living cells were identified by LIVE/DEAD Fixable aqua dead cell stain kit (ThermoFisher Scientific) and singlet cells were gated before further analysis. T cells are identified as CD19 -B220 -CD11b -NK1. 1 -TCRβ + cells, CD8 + T cells are CD19 -B220 -CD11b -NK1. 1 -TCRβ + CD8 + cells, T reg cells are CD19 -B220 -TCRβ + CD4 + CD25 + Foxp3 + cells, NK cells are CD19 -TCRβ -NK1. 1 + cells, B-derived cells are CD19 + B220 + TCRβ -cells, memory B cells are CD19 + B220 + CD38 + CD80 + IgD lo cells, neutrophils are CD19 -TCRβ -CD11c -CD11b + Ly6G + cells, eosinophils are CD19 -TCRβ -CD11c -CD11b lo Ly6G -SiglecF + cells, basophils are CD19 -TCRβ -CD11c -CD11b + Ly6G -CD117 -FcεR- Ia + CD49d + cells, mast cells are CD19 -TCRβ -CD11b -FcεR- Ia + CD117 + peritoneal cells, monocytes are CD19 -TCRβ -CD11c -F4/80 -CD11b + CD115 + cells , and macrophages are CD19 -TCRβ -F4/80 + CD11b + peritoneal cells.
Constitution of a training series To obtain enough transcriptomic samples mapping to each of the nodes (cell categories) of the prior hierarchical model, we collected transcriptomic profiles of sorted cell populations from the ImmGen Microarray datasets. To include non-immune non-stromal negative controls, parts of additional datasets were also added to our training data, including those from epithelial cell lines, hypothalamic cell lines, melanoma, pancreatic ductal adenocarcinoma, and breast cancer cell lines, myoblasts, and hepatocytes. Curation and normalization of data are explained in the "Methods" section.
Signatures discovery Only categories that were fully represented, i. e. , of which all subcategories were included in the dataset, were considered for signature discovery. For each of the 55 remaining populations (Additional file 1: Fig. S1b ), all available transcripts (probes) were screened for (log2) fold-change (FC), specific fold change (sFC), and area under the ROC curve (AUC) (see the "Methods" section). Features were considered as transcriptomic markers for a given population if they respected 3 criteria: FC > 2. 1, sFC > 2. 1, and AUC > 0. 97. Figure  2  a and b illustrate an example with probe 10442786 (Tpsb2), which qualified as a transcriptomic marker for mast cells. On Fig. 2a , we observe an overexpression of the marker in mast cells, with a log2 FC of 6. 48 for the median expression as compared to the median expression in all non-mast cell samples, as well as a log2 sFC of 5.
Scoring metric Given a cell population and its signature (i. e. , the set of corresponding transcriptomic markers), the next step consisted in defining a metric, taking as input the expression value of this set of markers, and yielding as output a score of abundance of the cell population. To select a scoring metric, we performed tests on a dataset composed of in silico-simulated RNA mixtures (see the "Methods" section). Six scoring metrics were considered: arithmetic mean, geometric mean, harmonic mean, quadratic mean, energetic mean, and median. Each metric was tested on the mixtures by analyzing the correlations between the derived scores and the known proportions of all cell populations. The score for each scoring metric and each population are reported in Additional file 1: Fig. S4. All metrics were found to perform similarly.
Comparison with other published methods Other methods have been previously reported to analyze the composition of heterogeneous samples in murine models  [21, 22]. DCQ (Digital Cell Quantification) is an algorithm that, given gene expression data and prior knowledge on immune cell type transcriptomic profiles, returns the cell abundances for a wide variety of immune cells  [21] ; it was designed using RNA-Seq data. ImmuCC  [22]  is derived from the method proposed by CIBERSORT  [23]  and adapted by finding markers for murine populations; it was first designed for microarrays, but an updated version is adapted for RNA-Seq data  [24]. We applied both methods on 50 sets of in silico RNA mixtures (each with 50 samples, see the "Methods" section) generated from the Haemopedia dataset  [25]  and 50 similar sets of 50 in silico mixtures generated from the ImmGen ultra-low-input RNA-seq (ImmGen ULI) dataset  [26] , another dataset composed of purified immune cells independent of the dataset used to generate the signatures, to assess each method's performance on cell subtypes that were quantified by both mMCPcounter and another.
On the Haemopedia dataset, mMCPcounter outperformed both ImmuCC and DCQ for T cells, B-derived cells, monocytes/macrophages, monocytes, mast cells, and neutrophils (all p values below 5e-12), performed similarly with ImmuCC and outperformed DCQ for CD8 + T cells, and was outperformed by ImmuCC only for eosinophils. On the ImmGen ULI dataset, which comprises more populations, including NK cells and memory B cells, on all considered populations, mMCP-counter outperformed both ImmuCC and DCQ (p < 4e-06 for all comparisons, with the exception of memory B cells, p = 0. 0298 as ImmuCC computed non all-zero scores for the population for only 2 sets of mixtures). mMCP-counter also found to consistently perform well, with the median correlation between mixture compositions and scores above 0. 7 for all considered populations in both datasets, whereas ImmuCC and DCQ performance greatly varied depending on the populations (Fig.
4 ). We also estimated the spillover effect, that is the fact for a method to estimate the presence of other cell types in a setting with only one cell population present  [5]. This is illustrated in Additional file 1: Fig. S6 , for which the analysis was performed on the mean expression profile of CD8 + T cells, B-derived cells, NK cells, monocytes/macrophages, neutrophils, eosinophils, and mast cells from the Heamopedia (Additional file 1: Fig. S6a ) and ImmGen ULI (Additional file 1: Fig. S6b ) datasets. We found that mMCP-counter had a slight spillover where pure mast cells could have a non-zero score for B-derived cells, pure neutrophils a non-zero score for monocytes/macrophages and eosinophils, and monocytes/macrophages and eosiophils a non-zero score for each other. However, the overall noise ratio, measured as the ratio of off-target scores to total scores, was low (0.
28 and 0. 24 for Haemopedia and ImmGen ULI, respectively). ImmuCC had the lowest overall noise ratios of all three methods (0. 07 and 0. 19), but on the ImmGen ULI dataset, pure eosinophils are estimated as principally monocytes/macrophages. DCQ was found to have stronger spillover effects and overall noise ratios of 0. 54 and 0. 66.
In more details, we also analyzed the association between the scores for each population and response, in both models (Fig. 5b ). This revealed associations with responses that are found in both kidney cancer and mesothelioma models. Indeed, the two models showed that Fig. 4  Comparison of the of mMCP-counter with other published methods. three methods have been applied to 50 simulated RNA mixture sets, each comprising 50 randomized mixtures, generated from the Haemopedia (a) and ImmGen ULI datasets (b). This graph shows the Pearson correlation between the mixture compositions and the scores returned by the methods for each population on all mixture sets. Full lines indicate correlation equal to 0 and 1. Dashed lines indicate correlations equal to 0. 7 and 0. 5. An asterisk indicates that for memory B cells, there were only 2 datasets where ImmuCC returned non-all-zero scores and where its performance could therefore be assessed responsive tumors had an increased infiltration by T cells, CD8 + T cells, and monocytes/macrophages as compared to tumors that resisted the ICB treatment.
For several populations, a very strong correlation was found, in part due to the fact that we included samples with both very low and very high content in these populations. While this limits the possibility to demonstrate a proper linearity between the mMCP-counter scores and the flow cytometry estimates, it shows that no outlier was found, and that mMCP-counter consistently returned extremely low or high scores for samples with low or high content, respectively. We also showed that mMCP-counter allows a significant improvement over the existing methods by comparing its performance to previously developed methods on large simulated mixture datasets from two independent datasets. Applied to mouse models of kidney cancer and mesothelioma treated by combination of immune checkpoint blockade therapies, mMCP-counter allows to decipher the differences of tumor microenvironment composition between both tumor types and between responders and non-responders to immune checkpoint blockade.
To assess the applicability of mMCP-counter beyond the field of cancer, we analyzed a model of Alzheimer's disease. Thus, we applied mMCP-counter to hippocampal transcriptomics data from a murine model of Alzheimer's disease, comparing mice with induced AD with controls. The most striking difference was an increased expression of the monocyte/macrophage signature in AD mice. This may be explained by the detection by mMCP-counter of microglia, which have been shown to be a prominent marker of AD  [32]. Conversely, the role of B cells is disputed and looked as inessential in AD  [32]. Here, we also noticed an increase in B lineage cells. Finally, we also noted an increased presence of endothelial vessels in AD mice, in line with reports of angiogenesis in AD  [33]. mMCP-counter could help further analyze the immune and stromal impacts of AD and other neurodegenerative syndromes.
Beyond the field of cancer, mMCP-counter may also have broad applications in murine models of diseases in which immunity, inflammation, or angiogenesis play crucial roles. In particular, it can be applied to models of neurodegenerative syndromes such as Alzheimer's disease, well-known auto-immune  [44]  and inflammatory  [45, 46]  diseases, but also atherosclerosis  [47]. Contre le Cancer, grants from Institut National du Cancer (HTE-INSERM plan cancer, C16082DS), Cancer Research for Personalized Medecine programme (CARPEM T8), "FONCER contre le cancer" program, and Labex Immuno-Oncology (LAXE62_9UMRS972 FRIDMAN). This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 754923. The materials presented and views expressed here are the responsibility of the authors(s) only.
The EU Commission takes no responsibility for any use made of the information set out. FP was supported by CARPEM doctorate fellowship. Availability of data and materials mMCP-counter can be accessed as an R package (https://github. com/citbioinfo/mMCP-counter) [9]. The RNA-seq data from murine spleen, peritoneum, peripheral blood, and tumors are available from ArrayExpress (accession code E-MTAB-9271)  [48].
Introduction 1 Until the past decade, most studies exploring the quality, reach and impact of research outputs have focused on data relating to journal articles  (Torres-Salinas et al. 2014 ). Relatively little work has been done to explore the extent to which altmetrics approaches are capable of producing meaningful information about the role of specialist scholarly books in the research and knowledge sharing practices of scholarly communities  (Neylon et al. 2018 ). However, digital traces of online interactions relating to scholarly books have the potential to shed light on how people use, discuss, and share monographs, as well as the role that individuals, institutions and platforms play in this process. These traces may also make it possible to better understand the effects of OA status on patterns of digital dissemination and use for scholarly books.
2 This paper explores the extent to which social media data is capable of shedding light on the digital lives of OA scholarly books. The paper begins with a discussion of key differences between monographs and journal articles and considers the implications of these differences for efforts to identify, capture, and analyse data associated with the use of OA scholarly books. It then goes on to report on a study of data related to 28 OA monographs in the humanities and social sciences. The study captured mentions of the study-set of monographs via Twitter, Facebook, Wikipedia and online blogs; as well as user ratings on Google Books, Amazon and Goodreads. Information relating to the ways in which the books were bookmarked and cited was captured via the online reference managing platform Mendeley.
Strategies used to both capture and interpret these data are discussed, before the paper goes on to take a deeper dive into social media data associated with a single OA monograph title: examining the ways in which discussions and mentions of the book unfolded across the Twittersphere, and exploring correlations between traditional measures of use and impact, and altmetrics. The study demonstrates the potential of social media data to provide useful insights into the ways in which OA monographs are being used by communities; and provides practical lessons for researchers interested in developing robust altmetrics approaches for scholarly books. What is different about monographs? Monographs and journal articles are both key forms of research publication. Both forms are used by researchers to communicate new knowledge, theories, and insights.
2018). In this context, there is a growing demand for new tools capable of helping publishers, authors, and research funders to understand the impact and effectiveness of different approaches to publishing specialist scholarly books. Both publishers, and authors, increasingly operate in funding landscapes in which the capacity to demonstrate the reach and impact of research is valuable. Citation and usage data relating to books are one aspect of this. Altmetrics data -particularly data relating to the reception, circulation, and use of books, also represents a potentially valuable source of information for stakeholders in OA monograph publishing.
Readers interact with books and journal articles in different ways There are also differences between the ways in which readers interact with books in digital landscapes, and the ways in which they interact with journal articles. These differences create potential sources of altmetrics data for books that simply do not exist for journal articles. Online reviews of books are one example of this. In contrast to journal articles, which are rarely if ever reviewed by readers on commercial retail sites, online reviews are a popular resource for potential readers of long-form publications. Reviews provide readers with insight into whether an investment of time in a book will be worthwhile. Because reading books is time consuming, reviews remain important, even when books are available in OA at no financial cost to a prospective reader.
Methodology and datasets The study data set comprises the 28 titles included in the first pilot collection of books made OA via Knowledge Unlatched in 2014. These titles, published by 13 scholarly publishers, are in the fields of anthropology, history, literature, media and communications, and politics. Data relating to how often each OA book was accessed included data arising from access of the books via the OAPEN Library. Country-based download reports for the titles hosted by OAPEN were provided by Institutional Repository Usage Statistics UK (IRUS-UK) 3. IRUS UK captures and cleans OAPEN usage data using the COUNTER methodology for identifying and removing bot-usage. These reports were downloaded and analysed using a script. To analyse mentions, usage, appraisal and citations, data were also collected from five social media platforms (Twitter, Facebook, Wikipedia, Goodreads and Mendeley) and queried on three citation databases (Scopus, WoS, and Google Scholar) in which the 28 title names were mentioned.
For each platform, the number of titles covered was examined. Using the Salesforce Radian6 platform 4 , public mentions of the 28 titles on Facebook and Twitter were collected. The findings from Facebook were analysed according to post type for each title. The number of tweets for each title was also examined, as well as the number of Twitter users mentioning this title. The number of tweets mentioning a title over varying periods from the release of the title was analysed. In addition, the Twitter capture tool 5 (TCAT) software was used to track how Twitter users disseminated information about a title. TCAT software tracks Twitter mentions along with the visibility of a user by the number of mentions they receive, how many followers they have, who follows them. It captures data using Twitter's live streaming traffic.
Because TCAT software cannot access Twitter's historical data to track the connections between users in the Twittersphere, a newly published OA title from UCL Press was chosen as an additional case study. Nodes disseminating information about the title to other groups were visualized and identified using the network analysis and visualization software Gephi 6 0. 9. 1. Mentions of books in the study set on peer-reviewed research blogs were tracked by querying the ResearchBlogging platform. The science-only aggregator ResearchBlogging. org (Shema et al. 2014 ) is a platform that was in operation until April 2017, direct readers to blog posts that refer specifically to peer-reviewed research. Wikipedia articles mentioning the titles were manually queried using the Google search engine. Reviews and ratings for each title on the Amazon, Google Books and Goodreads platforms were inspected through these sites' APIs using a script.
We identified whether or not titles were sold via Amazon and Google Books. In addition, the format in which the books were provided on the Amazon and Google Books platforms was noted. Using the Mendeley platform API we determined which users of this platform had collected the titles into their libraries and how the titles were being used. To identify how these titles were being read and to determine which titles were attracting more attention, the annotation platforms Hypothes. is and PaperHive were manually examined using each title name and also by using the PDF file of each title. Citations of the titles were also investigated using a script to connect to and query the citation databases Scopus, WoS, and Google Scholar. Subsequently, the yearly aggregated citations for each title on WoS were checked in order to determine whether patterns in usage could be identified.
Datasets COUNTER-compliant access reports were collected from the IRUS-UK website for the period of March 2014 to June 2017. A social network dataset was compiled for the period between 1 January 2014 and 1 July 2017. This database included 493 records from Twitter that related to all of the KU pilot collection titles and 96 records from Facebook that referred overall to 20 of these titles. In addition, the UCL Press title "How the World Changed Social Media" was chosen to be the subject of a case study investigating the dissemination of a scholarly monograph across the Twittersphere. Between 1 July 2016 and 1 April 2017, 181 tweets from 103 users referring to this title were captured. The Wikipedia dataset also included 23 articles referring to 13 monographs from the KU pilot collection. All of the 28 titles' pages were collected from Google Books, Amazon and Goodreads.
35% produced in the first two months. In Figure  1 , a plateau in tweet numbers can be seen after the first 850 days following publication. Eysenbach (2011)  refers to the period from the first tweet until the plateau (the first 850 days) the "network propagation phase", during which the new information is propagated through the Twitter network. He refers to the period following this phase as the "sporadic tweetation phase", where mentions only occur sporadically. (Image source: authors) Because this study was started two years after the KU pilot collection was made OA, it was not possible to track Twitter mentions of the titles by using the TCAT software to graph the network structure. Instead, as a case study, tweets mentioning the title "How the World Changed Social Media" were tracked for nine months (from 1 July 2016 until 1 April 2017) as part of a study involving titles from the OA monograph publisher UCL Press  (Montgomery et al.