dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets used in the study For this project, we used four freely available sc-RNA-seq datasets of murine cells. The Embryo_dataset_1  (Hou et al. , 2020; GSE139389)  corresponds to embryonic tissues, as it contains 1432 single cells from 29 mouse embryos captured with high-precision single-cell transcriptomics. This dataset covers relevant EC populations at continuous developmental stages (E8-E11) which corresponds to the process of EHT. Single cell libraries were sequenced on Illumina HiSeq 4000 platform in 150 bp pair-ended manner. UMI-based scRNA-seq method was used to measure the gene expression profiles within individual cells. Cells expressing endothelial markers were used for data integration with adult endothelial cells. The Embryo_dataset_2  (Vink et al. , 2020; GSE143637)  is sc-RNA-seq of AGM cells processed with the Smart-Seq2 protocol.
The Endothelial cell cluster was used for data integration. The Embryo_dataset_3  (Zhu et al. , 2020; GSE137116)  is composed of single cell RNA-Seq profiles of cells involved in EHT from mouse embryos at embryonic day 9. 5, 10. 5, 11. 5. Only the largest data subset called 'E10. 5 E+HE+IAC' was used in our study. It corresponds to the clusters called 'Conflux E', 'Endo', 'HE', 'IAC' and 'pre-HE'. Cells from the developmental trajectory of pre-hematopoietic stem cell formation was processed for library preparation using the 10x Genomics Chromium Single Cell 3′ Reagent Kit v2. The last dataset was Tabula Muris that is an atlas of cell types from the mouse Mus musculus which comprises single-cell transcriptomic data from 100,605 cells isolated from 20 organs from three female and four male mice  (Tabula Muris Consortium, 2018).
Data integration In order to consider the relative differences between datasets, we applied a data integration technique and integrated all datasets together. The workflow of data integration was based on Seurat v3  (Butler et al. , 2018 & Stuart et al. , 2019). Firstly, this method aims to identify 'anchors' between each pair of datasets. This process reflects pairwise correspondences between individual cells that are hypothesized to originate from the same biological state. These 'anchors' are then used to reconcile the datasets, or transfer information from one dataset to another. We performed all the necessary steps of the data integration workflow: 1) The first step was a preprocessing of the datasets that was to create a list with gene expression matrices from each dataset and separately run Seurat preparation procedure ー normalization using the NormalizeData function and feature selection with FindVariableFeatures.
Normalization Most of the research, starting with the normalization part, was done using the R package Seurat, it showed good performance in analyzing single cell RNA-seq data and has most of the necessary functions with relevant methods: normalization, feature selection, clustering and data integration. Since the sequencing data from the Tabula Muris dataset was generated with Smart-seq2 protocol, which does not allow for usage of UMI's, we used the well-proven global scaling normalization method "LogNormalize" that normalizes the gene expression measurements for each cell by the total expression, multiplies this by a scale factor (10,000 by default), and log-transforms the result. For the embryonic datasets, which use the UMI-based sc-RNA-seq protocol, we applied a recent normalization approach called ScTransform  (Hafemeister et al.
Restoring cell trajectories with PAGA In an analysis of single cell expression datasets that contain cell populations with transient processes (like EHT) it is important to capture this conversion and reflect it in the downstream analysis. In our research we performed trajectory inference with the Python library Scanpy and its framework PAGA  (Wolf et al. , 2018 & Wolf et al. , 2019). This method provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions. We ran the PAGA with standard workflow:.
Clustering With the Tabula Muris dataset we used the annotation, provided by the authors of the original article. With the Embryo_dataset1, we used the SNN-clustering method available in Seurat: 1) First step is calculating the PCAs of the expression matrix with the function RunPCA. 2) Second step is running the function FindNeighbours that computes SNNgraph. 3) Finally, to cluster the cells we applied the function FindClusters to the calculated graph with use of Leiden algorithm and resolution 0. 3  (Traag et al. , 2019).
Inferring regulatory networks using ScTarNet The important part of our research was the comprehension of how the seed transcription factors interact with the target genes. To answer this question, we inferred gene regulatory networks with the R package ScTarNet  (Bergiers et al. , 2018). This package provides a method that builds networks based on an input set of transcription factors and tests for indirect relationships and TF-TF interactions using distance correlations. We applied a standard workflow of the ScTarNet: • First step was initial correlation network inferring with function calculateTFstoTargets, which estimates relationships between TFs and target genes • Second step is identifying TF-TF interactions using partial distance correlation with function calculateConditionalCors 1.
This distance correlation metric is a new statistical approach which enables detection of both linear and non-linear dependencies between variables that is particularly important for analyzing highly-dimensional data like single cell expression. Its main limitation is that it is an O(n2) operation, where n is the number of samples. We optimized the ScTarNet running time with replacing the distance correlation function to the one from the package Rfast (https://github. com/RfastOfficial/Rfast). The output from this package are the target genes corresponding to the seed genes with three possible types of relationship: positive, negative and none.
Identifying the cells expressing TF-dependent genes One of the questions of the research was to identify the cells with specific gene signatures of the groups of target genes associated to TFs. We solved this problem with the use of the R package AUCell  (Aibar et al. , 2017). This package uses the AUC-metric to determine whether a crucial subgroup of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC-scores between every cell enables exploration of the relative expression. This scoring method is based on rankings which are built on the expression matrix. The outcome is the groups of cells linked to seed TFs. To find this groups we used default workflow of the package: 1) For each cell, the genes were ranked from highest to lowest value. The genes with the same expression value were shuffled.
Therefore, genes with expression '0' were randomly sorted at the end of the ranking. This step was done using the function AUCell_buildRankings. 2) To determine whether the gene set is enriched at the top of the gene-ranking for each cell, AUCell uses the "Area Under the Curve" (AUC) of the recovery curve. The function AUCell_calcAUC calculates this score, and returns a matrix with an AUC score for each gene-set in each cell. 3) The AUC represents the proportion of expressed genes in the signature, and their relative expression value compared to the other genes within the cell. We used this property to explore the population of cells that are present in the dataset according to the expression of the gene-set with function AUCell_exploreThresholds. The ideal situation is a bi-modal distribution, in which most cells in the dataset have a low AUC compared to a population of cells with a clearly higher value.
Detecting Runx1-specific signatures To uncover any Runx1-specific patterns of single cell expression we applied straightforward approach: 1) All endothelial cells expressing Runx1 were allocated to a separate cluster ("x cells"). 2) Next, we performed Seurat marker gene identification contraposing cluster of endothelial cells to the "x cells" cluster. 3) Obtained gene lists were compared between adult tissues and Mouse Embryo dataset, calculating the number of overlaps.
e consensus assembly genomes were aligned to the reference SARS-CoV-2 genome using Nucmer (MUMmer), and SNPs were predicted using the Nucmer tool to show SNPs  [10]. en, SNPs were annotated using SnpEff (SNPEFF) within the SARS-CoV-2 database  [11]. All computations were run at the National Laboratory for High-Performance Computing (https://www. nlhpc. cl) [12]. We performed a second bioinformatic analysis using an independent process and open-source tools. Sequence pairs were aligned to the SARS-CoV-2 reference genome using Bowtie 2 version 2. 3. Variants were called Freebayes version 1. 3. 5 with a ploidy setting of 1 and a minimum allele frequency of 0. 70. e coverage statistics were performed using VCFtools version 0. 1. 17 to determine the mutation rate between the samples. In addition, the Nextstrain clade assignment was performed using NextClade version 1.
9. 0 to compare and visualize our samples globally. e PANGO lineage was analyzed with the pangolin software  [13]. e Clustal12. 1 tool was used for identity analysis between the two samples. e BioProyect database PRJNA768603: 0606-201 (SAMN22047842) and 0308-063 (SAMN22047843) from BioSample data are available in the NCBI. e original virus sequence used to compare both samples was as follows: severe acute respiratory syndrome coronavirus 2 isolate Wuhan-Hu-1, ACCESSION MN908947, March 2020, from the GeneBank database.
(c)). Between March and June 2020, viral samples belonging to clades 19A and 20 were reported, according to the Chilean GISAID database. From a global perspective, the most predominant clades between March and June 2020 were 19A, 19B, 20A, 20B, 20D, 20C, and 20F. For sample 0308-063, we observed that only clade 20B was reported in Chile. While between June and September 2020, the most represented clades were 19A, 19B, 20A, 20B, 20D,.
Acknowledgments e study was supported by the following: Basal Grant  CEDENNA AFB-180001 ;  FONDAP Grant 15200002 ;  Powered@NLHPC  (grant number  ECM-02 );  CORFO 10CEII-9157 ;  Proyecto COVID-1038  ( Agencia Nacional de Investigación y Desarrollo (ANID) ,  Goverment of Chile) ;  Fondecyt Regular 1201664  ( ANID, Goverment of Chile );  Fondecyt Regular 1211841  ( ANID, Goverment of Chile );  Fondecyt iniciación 11221308  ( ANID, Goverment of Chile );  DICYT-USACH 021943AC ; and  FONDEQUIP grant  (project number  EQM200016 ).
(c)). Between March and June 2020, viral samples belonging to clades 19A and 20 were reported, according to the Chilean GISAID database. From a global perspective, the most predominant clades between March and June 2020 were 19A, 19B, 20A, 20B, 20D, 20C, and 20F. For sample 0308-063, we observed that only clade 20B was reported in Chile. While between June and September 2020, the most represented clades were 19A, 19B, 20A, 20B, 20D, 1st positive COVID-19 2nd positive COVID-19 33. 2 16. 82 sample sample 33. 1 33. 0 16. 80 0606-201 0308-063 ORF1ab 32. 9 16. 78 Clade 20B Clade 20A 32. 8 32. 7 16. 76 Jun 6th Aug 4th ORF1ab Rnase P (a) (b).
(b) Apparatus Data were collected on mounted touchscreens in one of two testing rooms (15 sessions each) at Ape Initiative. Both touchscreens were Elo Touch touchscreens (24 00 in East Testing Room; 32 00 in West Testing Room) with a refresh rate of 60 Hz and resolutions of 1920 by 1080 pixels. Audio stimuli were presented via two Realtek high definition speakers that were either 1 m to the right (East Testing Room) or the left (West Testing Room) of Kanzi. The experimental procedure was written in Java by Ken Schweller and run on a HP Pavilion Laptop. Food rewards were delivered manually through a tube connecting researcher and ape areas (see the electronic supplementary material, SV1).
(ii) Regular pictures Using a Google picture search, two different images were found and used as tokens for each English audio recording (200 regular pictures total). All images were edited using ADOBE PHOTOSHOP CS5. 1 (Adobe Systems Incorporated, San Jose, CA, USA) to contain a black and white image against a black rectangular background to match the test trial pictures. All stimuli were displayed on a black screen background (figure  1 ). While Kanzi does not have any experience with the test shapes (see below), he does not necessarily have real-life experience with some of the regular trial images either (e. g. gorilla, phone, balloon). Original pictures ranged royalsocietypublishing. org/journal/rspb Proc. R. Soc. B 289: 20211717 in size between 132 and 4272 by 157-2848 pixels and were automatically scaled by the MTS program to a maximum of 558 width by 294 height while still retaining their original width to height ratio.
Funding. This work was supported by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany's Excellence Strategy  through  EXC 2025/1  ' Matters of Activity (MoA )' and by the 'The  Sound of Meaning (SOM)' ,  Pu 97/22-1  and ' Phonological Networks (PhoNet) ', Pu  97/25-1. K. M. was supported by the  Berlin School of Mind and Brain , by the  Onassis foundation , and by the Fyssen foundation. M. A. was supported by the  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/2007-2013) / ERC  grant agreement no. 609819 , SOMICS.
The management of a UNESCO Global Geopark (UGGp) requires a vast wealth of miscellaneous scientific knowledge that can be successfully organised using a Geographical Information System (GIS). This paper presents a pragmatic GIS database to assist in the suitable governance of the Courel Mountains UGGp (2017) in Northwest Spain. The database is structured in 66 coverages compiled from public sources and previous works or produced through traditional mapping (combining fieldwork and photointerpretation) and GIS tools. The acquired data was later homogenised and validated by a multidisciplinary team and archived in independent coverages. Forty thematic maps illustrate the broad range of cartographic information included in the GIS database. Among them, 25 basic maps provide an overview of the UGGp and 15 new maps focus on crosscutting and technical issues.
All maps illustrate the huge potential of GIS to create new resources combining coverages and adapting the legend according to their purpose and audience. The database facilitates the suitable publishing of consistent outputs (e. g. , brochures, books, panels, webpages, web serves), as well as the elaboration of technical data to assist the park management. The database furnishes information on the design of education actions, touristic routes, activities and Geopark facilities. The GIS database is also a supportive tool for scientific research and provides the necessary knowledge to conduct geoconservation actions based on land use, geological hazards and the occurrence of natural and cultural heritages. Altogether, the GIS database constitutes a powerful instrument for policy-making, facilitating the identification and evaluation of alternative strategy plans.
g. , geography, geology, biology, soil sciences, archaeology, history, ethnography, mining and engineering) and the varied spatiotemporal scales of different studies. Whereas decades ago, this management would be tough, effort-demanding work, nowadays, geographic information systems (GIS) facilitate this work  (Williams and McHenry 2020)  as they allow capturing, storing, quantifying and transforming data into useful information that can be edited and displayed for specific requirements or decisions by local managers  (Rutherford et al. 2015; Reddy 2018). GIS allows the input of data from external sources such as the GIS datasets provided by European governments, following the INSPIRE European Council Directive 2007/2/CE, or from its acquisition by experts through fieldwork, remote sensing and other ways (e.
g. , Google Street View). Databases in GIS are organised by combining vector and matrix files (named all of their coverages), each one with specific information  (Reddy 2018). Vector coverages (points, polylines and polygons) combine spatial and attribute data. In each coverage, the spatial data describes the absolute location of each geometric feature (e. g. , geosite, tourism office), including its shape, size, coordinates and orientation. The attribute table describes details of the spatial feature (e. g. , lithology, age), which are stored in tables in which each row is a feature and each column an attribute of the feature. Raster coverages contain a numerical value per pixel and are often used for representing terrain properties (e. g. , altitude, slope and reflectivity). GIS offers the possibility to quantify each feature using measurement tools and statistical analyses, recognising changes in an area over a period of time (e.
Data Acquisition Data acquisition was performed with the cooperation of municipal officers and local people. The information was captured from four sources: public repositories, the IGME database, previous works, and fieldwork/photointerpretation. (1) Public agencies of Spain and Galicia conduct open-source repositories (with open-source license) that include datasets in raster and vector formats related to the topography, aerial photography, administrative boundaries, infrastructures, land uses, floods, forest cover and soil erosion (Table  1 ). Datasets from six repositories were cut by the limits of the geopark and put into GIS to perform mainly the technical data and vegetation coverage. (2) The Instituto Geológico y Minero de España (IGME; Spanish geological survey) belongs to the Consejo Superior de Investigaciones Científicas (CSIC; Spanish national research council) and manages an extensive database comprising geological features from local to national scales.
The database comprises the vector files of the GEODE digital geological map (at 1:50,000 scale) and the national geosites inventory (IELIG) (Table  2 ), which were projected into the ETRS89 reference system and imported into GIS. Additionally, the IGME database contains raw data of local mineral ore deposits, mining, and igneous and metamorphic petrology (e. g. , thin section descriptions under microscopy). These data were employed to conduct the new coverages related to mineralizations and rocks. (3) Previous works (reported in Supplementary information 1) supplied information for the elaboration of coverages (e. g. , hydrogeological datasets, paleontological and archaeological inventories) and the definition of the feature attributes. Several of these published maps (e. g. , geomorphological maps) were digitalized into GIS.
Data Storage The database is hosted at the workstation of the Courel Mountains UGGp, which is managed by UGGp agents to preserve the privacy of the raw data. The information was compiled in 66 independent coverages according to their topic and data geometry (Table  3 ). Topics are grouped into technical and scientific data, which are divided into geosciences, biodiversity and cultural heritage. The technical data correspond to 36 coverages including topography, public administration, infrastructures, territorial planning and UGGp facilities. Topographic data represent the reference base map for data acquisition and comprise topographical contours, four digital models (elevation, slope, hillshade and aspect), aerial orthophotographs and watercourses (Table  3 ). Public administration and infrastructure data comprise municipalities (local authorities), parishes and communal areas (public lands managed jointly by neighbouring residents), as well as villages, individual buildings and communication routes (roads and railways).
Outputs Data Analysis Archived data were analysed using statistical and spatial functions available within GIS operations for the elaboration of educational contents and the technical advisory of UGGp actions. Statistics and spatial functions provided data of geological, biological and cultural features and their relationships, which can be used for outreach actions. For example, GIS functions were applied to retrieve the number of Roman mines, average slope at the watermill locations, average orientation of beehives, location of villages on ancient landslides, length and vertical range of routes, relative abundance of a rock type and distribution of particular species, or to compute the profile of a glacial valley. Spatial operations also helped the technical advisor in designing touristic products (e.
In this case, the geological units are represented according to their main lithology. (2) Lithology and canyoneering. The combination of ravines with the geological map provides an additional value to the distinctive gorges of the UGGp, linked to fluvial incision and alpine uplifting. The elaborated map named 'geological canyoneering' (Fig. 10b ) relates lithology and ravines, resulting in varied fluvial landscapes that would be appreciated in situ by canyoneers. Waterfalls are usually related to the presence of quartzite interbedded with slate, while the carbonate bedrock shows singular dissolution features. The geological canyoneering map constitutes a good example of the application of the GIS database in outdoor sports, which was used in the elaboration of the canyoneering guide of the UGGp (see Section 2 in Supplementary information 2).
14c ). The GIS database was used for planning and technical designing of touristic resources, such as routes, activities, touristic information documents and interpretation centres. For instance, the design of the Palaeozoic Villages Route  (Ballesteros et al. 2021a, b)  was supported by means of a density map of local services performed using GIS tools (Fig. 15a ). This route was devised following the highest concentration areas of local services in order to provide facilities for visitors and to attract customers to bars and restaurants. GIS also aids the management of the UGGp, mapping the visible and non-visible areas from the current viewpoint network (Fig. 15b ). The maps guide the construction of new viewpoints for watching the Courel Mountains. Therefore, GIS assists in the tourism scheduling of the UGGp, which would constitute a specific GIS-based tourism information system  (Nurpandi et al.
2020). The GIS database is a supportive tool facilitating scientific research in the UGGp, similar to in other previous works related to geosciences  (Perotti et al. 2019) , tourism  (Nandi 2019 ) and local community  (Stoffelen et al. 2019). Inventoried data in GIS can be included in scientific studies, and its results will provide feedback to the database. Most notably, the ongoing study of the Geosites of Courel Mountains is assisted by the created database. For example, studies using specific coverages of the database to develop new geomorphological and palaeontological studies could reveal the occurrence of new surface formations and fossil sites. These studies also supply new attributes of existing features, such as new data on rivers and fluvial deposits from a geomorphological point of view following Pérez-Alberti and Cunha (2016) and  Horacio et al.
Contributions to UGGp Management The GIS dataset provides the necessary knowledge to conduct geoconservation actions, considering protected areas, land use practices, geological hazards and the overlap of natural and cultural heritage. In fact, the use of GIS technologies for geoconservation is common (see  Williams and McHenry 2020). The combination of coverages allows the identification of potential impacts on geoheritage to mitigate its damage. Moreover, themes related to climate change enable to raise awareness on the potential impact of climate change on the UGGp. In summary, the GIS database offers a global and comprehensive vision of the UGGp supplying technical and scientific assistance based on relatively homogeneous, revised and structured scientific knowledge. Consequently, the GIS is a useful tool for policy-making towards development strategies, particularly during the phase of identification and evaluation of alternatives.