cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.13721899
title: Figure 4 - raw data

description: Below are the raw data of the experiments referring to figures 4A, 4B, 4C and 4D separately.Data in figure <b>4A</b> refer to the immobility time of mice administered with S-ketamine or vehicle and submitted to FST.Data in figure <b>4B</b> refer to the distance covered by the mice administered with S-ketamine or vehicle and submitted to the OFT lasting 20 minutes (the data shown refer to the distance covered in the last 5 minutes of the test).Data in figure <b>4C</b> refer to the immobility time of mice administered with vehicle, CBD, S-ketamine or CBD + S-ketamine and submitted to FST.Data in figure <b>4D</b> refer to the distance covered by the mice administered with vehicle, CBD, S-ketamine or CBD + S-ketamine and submitted to OFTlasting 20 minutes (the data shown refer to the distance covered in the last 5 minutes of the test).<br>
DOI: 10.15784/601433
title: Two-year velocity and strain-rate averages from the Thwaites Eastern Ice Shelf, 2001-2020

description: This dataset includes GeoTiffs of two-year averages of ice flow velocity (including x- and y-components and flow speed) and longitudinal, transverse, and shear strain rates for the Thwaites Eastern Ice Shelf (TEIS) from 2001-2020. The grids were derived from feature tracking on MODIS, Landsat-7, and Landsat-8 imagery. Each pixel in a grid represents the median value of a stack of all available pixels for each time period. Data are gridded at a 500 m spatial resolution in a polar stereographic (EPSG:3031) projection. Speed units are m/day and strain rates are in units of /day. In addition, we provide videos of each variable (excluding x- and y-velocity components) placed alongside a MODIS image of the same extent and from around the same time to provide context. In addition to the variables noted above, we include videos for flow direction (in degrees from grid north in an EPSG:3031 projection) and a zoomed-in version of flow direction, which were calculated from the provided grids.
DOI: 10.6084/m9.figshare.14067122
title: Social Media data for exploring the association between Cannabis use and Depression

description: The tweets from the time period of January 2017 to February 2019 are collected from Twitter using Drug Abuse Ontology (DAO) terms related to Cannabis and depression lexicon. A sample of 9888 tweets is manually annotated by domain experts and research students under the supervision of domain experts. The domain experts who are co-authors of this data are substance use epidemiologists with experience in Interventions, Treatment, and Addictions Research.<br>The annotation scheme has the following coding:<br>1. Treat: Cannabis is used to help/treat/cure depression.2. Cause: Cannabis causes depression or makes symptoms worse.3. Others: Implies other types of relationships, or too ambiguous/unclear to interpret. 4. Addiction: Lack of access to cannabis leads to depression, showing potential symptoms of addiction.5. Ambiguous: If word related to weed/cannabis or other cannabis products like dabs or edibles is missing or if the mention of depression-related words is missing.<br>We will continue to add and manage the dataset with new annotations and versions using machine learning techniques like weak supervision and transfer learning. For core computer scientists, this dataset can be useful while working on NLP problems like Relation extraction, Entity disambiguation, Relation prediction, etc. For interdisciplinary experts, this social media dataset can be explored to understand the association between cannabis and depression. <br>We acknowledge the contribution of Jason Roden and Melissa Wahl from Wright State University for participating in the coding under the supervision of domain experts. This project is sponsored by the National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02 to the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Interventions, Treatment and Addictions Research (CITAR) titled: Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use. Any opinions, findings, conclusions or recommendations expressed in this material are those of the investigator(s) and do not necessarily reflect the views of the National Institutes of Health.
DOI: 10.6084/m9.figshare.14067122.v2
title: Social Media data for exploring the association between Cannabis use and Depression

description: The tweets from the time period of January 2017 to February 2019 are collected from Twitter using Drug Abuse Ontology (DAO) terms related to Cannabis and depression lexicon. A sample of 9888 tweets is manually annotated by domain experts and research students under the supervision of domain experts. The domain experts who are co-authors of this data are substance use epidemiologists with experience in Interventions, Treatment, and Addictions Research.<br>The annotation scheme has the following coding:<br>1. Treat: Cannabis is used to help/treat/cure depression.2. Cause: Cannabis causes depression or makes symptoms worse.3. Others: Implies other types of relationships, or too ambiguous/unclear to interpret. 4. Addiction: Lack of access to cannabis leads to depression, showing potential symptoms of addiction.5. Ambiguous: If word related to weed/cannabis or other cannabis products like dabs or edibles is missing or if the mention of depression-related words is missing.<br>We will continue to add and manage the dataset with new annotations and versions using machine learning techniques like weak supervision and transfer learning. For core computer scientists, this dataset can be useful while working on NLP problems like Relation extraction, Entity disambiguation, Relation prediction, etc. For interdisciplinary experts, this social media dataset can be explored to understand the association between cannabis and depression. <br>We acknowledge the contribution of Jason Roden and Melissa Wahl from Wright State University for participating in the coding under the supervision of domain experts. This project is sponsored by the National Institute on Drug Abuse (NIDA) Grant No. 5R01DA039454-02 to the Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis) and the Center for Interventions, Treatment and Addictions Research (CITAR) titled: Trending: Social media analysis to monitor cannabis and synthetic cannabinoid use. Any opinions, findings, conclusions or recommendations expressed in this material are those of the investigator(s) and do not necessarily reflect the views of the National Institutes of Health.
DOI: 10.6084/m9.figshare.13961546
title: TX_Fires_Lat_Long.txt.xml

description: MODIS detected Fire occurrence data 2001-2016. Cut to extent of study area (Texas). Projected to WGS 1982.
DOI: 10.6084/m9.figshare.14115620
title: 2050 GF25 MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. The climate data are representative of the GF25 climate scenario in 2050. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.6084/m9.figshare.13961546.v1
title: TX_Fires_Lat_Long.txt.xml

description: MODIS detected Fire occurrence data 2001-2016. Cut to extent of study area (Texas). Projected to WGS 1982.
DOI: 10.6084/m9.figshare.13961432
title: Contemporary MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.6084/m9.figshare.14115668
title: 2050 GF85 MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. The climate data are representative of the GF85 climate scenario in 2050. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.6084/m9.figshare.14115668.v1
title: 2050 GF85 MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. The climate data are representative of the GF85 climate scenario in 2050. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.6084/m9.figshare.14115644
title: 2050 GF45 MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. The climate data are representative of the GF45 climate scenario in 2050. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.6084/m9.figshare.13003037
title: alignment_matK_subulatum.nex

description: DNA alignment from matK marker<br>
DOI: 10.6084/m9.figshare.13003037.v1
title: alignment_matK_subulatum.nex

description: DNA alignment from matK marker<br>
DOI: 10.6084/m9.figshare.13003040
title: climatic data.xlsx

description: Climatic data from selected populations<br>
DOI: 10.6084/m9.figshare.13003040.v1
title: climatic data.xlsx

description: Climatic data from selected populations<br>
DOI: 10.6084/m9.figshare.13003043
title: alignment_psbA_subulatum.nex

description: DNA alignment from matK marker used in the study
DOI: 10.6084/m9.figshare.13003043.v1
title: alignment_psbA_subulatum.nex

description: DNA alignment from matK marker used in the study
DOI: 10.5281/zenodo.4562981
title: Example for closed access

description: Demo example to see closed access publication
DOI: 10.5281/zenodo.4562980
title: Example for closed access

description: Demo example to see closed access publication
DOI: 10.6084/m9.figshare.13003046
title: Secuences_RAW_Data.zip

description: Raw sequences data from nuclear and chloroplast regions (matK, psbA, ITS)
DOI: 10.6084/m9.figshare.13003046.v1
title: Secuences_RAW_Data.zip

description: Raw sequences data from nuclear and chloroplast regions (matK, psbA, ITS)
DOI: 10.6084/m9.figshare.13003049
title: microsatellites_RAW_data.zip

description: Raw microsatellites data<br>
DOI: 10.6084/m9.figshare.13003049.v1
title: microsatellites_RAW_data.zip

description: Raw microsatellites data<br>
DOI: 10.6084/m9.figshare.13003052
title: microsatellites_scoring_subulatum.xlsx

description: Microsatellites scoring<br>
DOI: 10.6084/m9.figshare.13003052.v1
title: microsatellites_scoring_subulatum.xlsx

description: Microsatellites scoring<br>
DOI: 10.6084/m9.figshare.13003055
title: alignment_ITS_monophyly_lepidium_cardaria_arabidopsis.nex

description: DNA alignment from ITS marker
DOI: 10.6084/m9.figshare.13003055.v1
title: alignment_ITS_monophyly_lepidium_cardaria_arabidopsis.nex

description: DNA alignment from ITS marker
DOI: 10.6084/m9.figshare.13003058
title: alignment_matK_psbA_subulatum_cardamines.nex

description: DNA alignment from matK and psbA markers
DOI: 10.6084/m9.figshare.13003058.v1
title: alignment_matK_psbA_subulatum_cardamines.nex

description: DNA alignment from matK and psbA markers
DOI: 10.5066/f7sf2vf5
title: Vertical Distribution of Longfin Smelt in the San Francisco Estuary (ver. 2.1, August 2020)

description: Field data for fishes sampled using mid-water trawls, otter trawls, and the SmeltCam during day and night in San Pablo Bay, CA. This data release includes all measured environmental parameters and fish taxa included in the analysis.
DOI: 10.6084/m9.figshare.13515098
title: McMahon AJP Lung 2021 Supplemental Material.pdf

description: Online Supplemental Material containingSupplemental Table S1 and Supplemental Figures S1-S3 for research article: “PAR-2–activated secretion by airway gland serous cells: role for CFTR and inhibition by Pseudomonas aeruginosa” by D. B. McMahon, et. al.<br>
DOI: 10.6084/m9.figshare.13515098.v4
title: McMahon AJP Lung 2021 Supplemental Material.pdf

description: Online Supplemental Material containingSupplemental Table S1 and Supplemental Figures S1-S3 for research article: “PAR-2–activated secretion by airway gland serous cells: role for CFTR and inhibition by Pseudomonas aeruginosa” by D. B. McMahon, et. al.<br>
DOI: 10.6084/m9.figshare.13515098.v3
title: McMahon AJP Lung 2021 Supplemental Material.pdf

description: Online Supplemental Material containingSupplemental Table S1 and Supplemental Figures S1-S3 for research article: “PAR-2–activated secretion by airway gland serous cells: role for CFTR and inhibition by Pseudomonas aeruginosa” by D. B. McMahon, et. al.<br>
DOI: 10.15468/skrtj4
title: Bipolarity in sea slugs: a new species of Doridunculus (Mollusca: Nudibranchia: Onchidoridoidea) from Antarctica

description: This dataset contains the digitized treatments in Plazi based on the original journal article Moles, Juan, Wägele, Heike, Uhl, Gabriele, Avila, Conxita (2016): Bipolarity in sea slugs: a new species of Doridunculus (Mollusca: Nudibranchia: Onchidoridoidea) from Antarctica. Organisms Diversity & Evolution 17 (1): 101-109, DOI: 10.1007/s13127-016-0309-z, URL: http://dx.doi.org/10.1007/s13127-016-0309-z
DOI: 10.6084/m9.figshare.14115620.v1
title: 2050 GF25 MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. The climate data are representative of the GF25 climate scenario in 2050. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.6084/m9.figshare.13961432.v1
title: Contemporary MaxEnt Model Inputs for TX Fire Occurrence Model

description: These are the model inputs for the Maxent SDM used to generate fire occurrence prediction maps. When running the Maxent model, we used 135,000 background points, and used 20% of the occurrence points for model validation.
DOI: 10.21227/ckk0-jc60
title: Hardware-Design-Graph dataset

description: This dataset includes the data used in our two research papers. GNN4TJ and GNN4IP.
DOI: 10.6084/m9.figshare.13834712
title: Exploring researcher preference for the version of record - anonymised data

description: Anonymised data from Springer Nature's new white paper, <i>Exploring researcher preference for the version of recor</i>d.
DOI: 10.6084/m9.figshare.13834712.v2
title: Exploring researcher preference for the version of record - anonymised data

description: Anonymised data from Springer Nature's new white paper, <i>Exploring researcher preference for the version of recor</i>d.
DOI: 10.5066/p9btb41w
title: Data release for Climatically driven displacement on the Eglington fault, Las Vegas, Nevada, USA

description: The Eglington fault is one of several intrabasinal faults in the Las Vegas Valley, Nevada, USA, and is the only one recognized as a source for significant earthquakes. Its broad warp displaces Late Pleistocene spring deposits of the Las Vegas Formation, which record hydrologic fluctuations that occurred in response to millennial- and submillennial-scale climate oscillations throughout the late Quaternary. The sediments allow us to constrain the timing of displacement on the Eglington fault and identify hydrologic changes that are temporally coincident with that event. The fault deforms deposits that represent widespread marshes that filled the valley between ca. 31.7 and 27.6 ka. These marshes desiccated abruptly in response to warming and groundwater lowering during Dansgaard-Oeschger (D-O) events 4 and 3, resulting in the formation of a pervasive, hard carbonate cap by 27.0 ka. Vertical offset by as much as 4.2 m occurred after the cap hardened, and most likely after younger marshes desiccated irreversibly due to a sudden depression of the water table during D-O event 2, beginning at 23.3 ka. The timing of displacement is further constrained to before 19.5 ka as evidenced by undeformed spring deposits that are inset into the incised topography of the warp. Coulomb stress calculations validate the hypothesis that the substantial groundwater decline during D-O event 2 unclamped the fault through unloading of vertical stress of the water column. The synchroneity of this abrupt hydrologic change and displacement of the Eglington fault suggests that climatically modulated tectonics operated in the Las Vegas Valley during the late Quaternary.
DOI: 10.5281/zenodo.4562915
title: Gene family data from the PhyloGenes (release version 3.0, phylogenes.org)

description: The data files were generated from the PhyloGenes 3.0 release (see release notes here). About the two zip files: 1. phyloXML.zip PhyloGenes gene family trees in PhyloXML format, one file per family (e.g. &lt;family_ID&gt;.xml). The following information is provided for each node of a tree:<br> 1) leaf node:<br> branch length<br> name &lt;gene_id&gt;<br> taxonomy scientific_name<br> sequence accession &lt;UniProt ID&gt; 2) non-leaf node:<br> branch length<br> events &lt;duplication or speciation&gt; <br> 2. CSV.zip Functional information of family members in CSV format, one file per family (e.g. &lt;family_ID&gt;.csv). A CSV file includes the following columns:<br> Uniprot ID<br> Gene &lt;Gene name. If none then Gene ID&gt;<br> Gene ID<br> Gene name<br> Organism<br> Subfamily name The columns displayed after 'Subfamily name', if any, are GO annotations. Each column is a GO molecular function or biological process term that is annotated to at least one member of the gene family AND the annotation is supported by an experimental evidence (indicated by 'EXP') or phylogenetic inference (indicated by 'IBA'). A '0' indicates absence of either annotations.
DOI: 10.5281/zenodo.4562914
title: Gene family data from the PhyloGenes (release version 3.0, phylogenes.org)

description: The data files were generated from the PhyloGenes 3.0 release (see release notes here). About the two zip files: 1. phyloXML.zip PhyloGenes gene family trees in PhyloXML format, one file per family (e.g. &lt;family_ID&gt;.xml). The following information is provided for each node of a tree:<br> 1) leaf node:<br> branch length<br> name &lt;gene_id&gt;<br> taxonomy scientific_name<br> sequence accession &lt;UniProt ID&gt; 2) non-leaf node:<br> branch length<br> events &lt;duplication or speciation&gt; <br> 2. CSV.zip Functional information of family members in CSV format, one file per family (e.g. &lt;family_ID&gt;.csv). A CSV file includes the following columns:<br> Uniprot ID<br> Gene &lt;Gene name. If none then Gene ID&gt;<br> Gene ID<br> Gene name<br> Organism<br> Subfamily name The columns displayed after 'Subfamily name', if any, are GO annotations. Each column is a GO molecular function or biological process term that is annotated to at least one member of the gene family AND the annotation is supported by an experimental evidence (indicated by 'EXP') or phylogenetic inference (indicated by 'IBA'). A '0' indicates absence of either annotations.
DOI: 10.34863/41cn-4361
title: Datasets: Deep Neural Net Instance Segmentation of Optical Floating Zone Melts

description: Image and JSON labeled polygon training and test sets for deep-learning models. GoodMelts, fastBottom, and fastTop archives contain dataset for each of the named instance classes described in the associated paper (DOI pending). 10x_crossVal archive contains the training data split into the 10 k-fold, cross validation sets used in the study. TestSet are a reserved set of labeled images used for model evaluation, but not used in training.

Item
DOI: 10.15139/s3/5rrlpz
title: New Bern, NC_2020 (Frankie Zito MP)

description: Frankie Zito MP, Spring 2020 New Bern, North Carolina
DOI: 10.5281/zenodo.4144319
title: Paper-author bipartite graph from Semantic Scholar

description: Paper-author bipartite graph created from Semantic Scholar's Open Research Corpus, version 2018-05-03. Vertices are papers (39,219,709 of them) in one part and authors (12,862,455 of them) in the other. A paper is connected to all its co-authors, and an author is connected to all the papers they wrote, leading to 139,268,795 edges. A citation count (the number of times the paper was cited) is available for each paper (from 0 to 37,230 citations per paper).
DOI: 10.5281/zenodo.4144318
title: Paper-author bipartite graph from Semantic Scholar

description: Paper-author bipartite graph created from Semantic Scholar's Open Research Corpus, version 2018-05-03. Vertices are papers (39,219,709 of them) in one part and authors (12,862,455 of them) in the other. A paper is connected to all its co-authors, and an author is connected to all the papers they wrote, leading to 139,268,795 edges. A citation count (the number of times the paper was cited) is available for each paper (from 0 to 37,230 citations per paper).
DOI: 10.25345/c5kz3k
title: MassIVE MSV000086954 - GNPS - Corynebacterium aurimucosum
title: Culture of Corynebacterium aurimucosum NCBI:txid169292 DSM-44532 on diluted BHI enriched with chemical compounds under aerobic conditions. Data was acquired in positive ion mode.
DOI: 10.15139/s3/frb2zc
title: When do mice start to see?

description: Quest - ATL Science Festival
DOI: 10.25345/c5g80m
title: MassIVE MSV000086955 - GNPS - Rothia dentocariosa LC-MS
title: Culture of Rothia dentocariosa NCBI:txid2047 DSM-43762 on diluted BHI enriched with chemical compounds under aerobic conditions. Data was acquired in positive ion mode.
DOI: 10.5066/p96psobq
title: Spatial variation in population dynamics of Northern Great Plains piping plovers, 2014-2019
DOI: 10.25810/c862-0e81.34
title: US Television Coverage of Climate Change or Global Warming, 2000-2021 - January 2021

description:         The Media and Climate Change Observatory Data monitors 120 sources (across newspapers, radio and TV) in 54 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.
DOI: 10.25345/c5bf84
title: MassIVE MSV000086956 - GNPS - Pseudomonas fragi LC-MS
title: Culture of Pseudomonas fragi NCBI:txid296 DSM-3456 on diluted BHI enriched with chemical compounds under aerobic conditions. Data was acquired in positive ion mode.
DOI: 10.6084/m9.figshare.14119910
title: Kansas_River_continuous_WQ_data.RData

description: 15-minute water quality and quantity measurements, including chl-a, nitrate (SUNA), turbidity, temperature and discharge, at 3 sites down the mainstem of the Kansas River (gage ID's: "06887500",06888990, "06892350", "06892518"). File is an R "list" object, where each element is a wide table per site. For each site, columns are parameters, and rows are 15 minute time intervals (i.e., wide format.) Data used in manuscript under review at ecological applications: "Simple metric for predicting the timing of river phytoplankton blooms".
DOI: 10.6084/m9.figshare.14119910.v1
title: Kansas_River_continuous_WQ_data.RData

description: 15-minute water quality and quantity measurements, including chl-a, nitrate (SUNA), turbidity, temperature and discharge, at 3 sites down the mainstem of the Kansas River (gage ID's: "06887500",06888990, "06892350", "06892518"). File is an R "list" object, where each element is a wide table per site. For each site, columns are parameters, and rows are 15 minute time intervals (i.e., wide format.) Data used in manuscript under review at ecological applications: "Simple metric for predicting the timing of river phytoplankton blooms".
DOI: 10.25810/4c3b-b819.34
title: World Radio Coverage of Climate Change or Global Warming, 2000-2021 - January 2021

description:         The Media and Climate Change Observatory Data monitors 120 sources (across newspapers, radio and TV) in 54 countries in seven different regions around the world. Data is assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases via the University of Colorado libraries. More information may be found at: http://mecco.colorado.edu.