cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.1235446
title: Georges Laplace's archaeological offprints collection

description: Table of the offrint collection held by the prehistoric archaeologist Georges Laplace (1918–2004) at the <em>Centre de palethnographie stratigraphique</em> in Arudy, France. This index has been generated from the Georges Laplace archives in the <em>Musée national de Préhistoire</em> (Les Eyzies-de-Tayac, France) and the offprint collection of the TRACES laboratory (Toulouse, France). Each line corresponds to one offprint, which is described by 16 values (columns): <strong>ART_lang</strong>: language of the offprint. <strong>ART_title</strong>: title of the offprint. <strong>ART_year</strong>: publication year of the offprint. <strong>ART_journal</strong>: journal title in which the offprint was published. <strong>ART_fullref</strong>: unstructured reference of the journal title, issue, and pages of the offprint. <strong>aut1</strong>: first author of the offprint. <strong>aut2</strong> <strong>aut3</strong> <strong>aut4</strong> <strong>aut5</strong> <strong>aut6</strong> <strong>aut7</strong> <strong>aut8</strong> <strong>aut9</strong> <strong>aut10</strong> <strong>source</strong>: whether the line comes from the TRACES laboratory collection or the MNP collection.
DOI: 10.5517/cc6ln7n
title: CCDC 196671: Experimental Crystal Structure Determination

description: Related Article: A.S.Antsyshkina, G.G.Sadikov, A.L.Poznyak, V.S.Sergienko|2002|Zh.Neorg.Khim.|47|43|
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.21227/sdad-1e56
title: Detecting Damaged Buildings on Post-Hurricane Satellite Imagery Based on Customized Convolutional Neural Networks

description: After a hurricane, damage assessment is critical to emergency managers and first responders so that resources can be planned and allocated appropriately. One way to gauge the damage extent is to detect and quantify the number of damaged buildings, which is traditionally done through driving around the affected area. This process can be labor intensive and time-consuming. In this paper, utilizing the availability and readiness of satellite imagery, we propose to improve the efficiency and accuracy of damage detection via image classification algorithms. From the building coordinates, we extract their aerial-view windows of appropriate size and classify whether a building is damaged or not. We demonstrate the result of our method in the case study of 2017 Hurricane Harvey.
DOI: 10.17632/w5k6hx7mbz.1
title: CHINARE2016 tracer data version 1

description: During the summer and fall of 2016, the 7th Chinese National Arctic Research Expedition (CHINARE2016) cruise was undertaken to the Bering Sea and the western Arctic Ocean with the R/V XUELONG (i.e., Snow Dragon) in July, August and September. The CFCs, SF6, temperature and salinity data were collected. This dataset will show all the data that has been corrected.
DOI: 10.17632/nx84xryt7b.1
title: Dataset on the moderating role of self-construal on the watching-eyes effect in prosociality

description: The data were composed of datasets from four experiments, a meta-analysis, and a subgroup analysis. The total sample size was 481 participants. There are six Excel workbooks of the datasets, each of which consists of two worksheets for database and statement, respectively (refer to the ZIP file in Appendix A). 
The first four sheets are for the four experiments, respectively. In the sheet for each experiment, each row represents a participant. It is important to note that the sheet also contains data for excluded participants, which are marked by gray shadow. Each column represents one of the experimental variables, consisting of age, gender, cues, self-construal, allocation amount (i.e., indicator of prosociality), perceived anonymity, etc. The last two sheets are for the meta-analysis and the subgroup analysis, respectively. The meta-analysis and the subgroup analysis used the same participants that were recruited in the analyses of the four prior experiments. For the meta-analysis (see “5 Meta-analysis” in Appendix A for database), the mean, standard deviation and sample size of each experiment were extracted respectively and organized into a single excel sheet for further calculation. The rows indicate the experiments and the columns indicate related summaries including the experiment number, sample size, mean and standard deviation for the experimental (eye) condition, sample size, and mean and standard deviation for the control condition. For the subgroup analysis (see “6 Subgroup analysis” in Appendix A), the participants of each experiment were further segmented into an independence subgroup and an interdependence subgroup according to the measurement or the manipulation of self-construal. The mean, standard deviation, and sample size were then extracted respectively and organized into a single excel sheet for further calculation. The rows indicate the subgroups and the columns indicate related summaries including subgroup number, sample size, mean and standard deviation for the experimental (eye) condition, sample size, mean and standard deviation for the control condition, and the subgroup assignment (i.e., 1 = independent self-construal; 2 = interdependent self-construal).
DOI: 10.17632/nx84xryt7b.2
title: Dataset on the moderating role of self-construal on the watching-eyes effect in prosociality

description: The data were composed of datasets from four experiments, a meta-analysis, and a subgroup analysis. The total sample size was 481 participants. There are six Excel workbooks of the datasets, each of which consists of two worksheets for database and statement, respectively (refer to the ZIP file in Appendix A). 
The first four sheets are for the four experiments, respectively. In the sheet for each experiment, each row represents a participant. It is important to note that the sheet also contains data for excluded participants, which are marked by gray shadow. Each column represents one of the experimental variables, consisting of age, gender, cues, self-construal, allocation amount (i.e., indicator of prosociality), perceived anonymity, etc. The last two sheets are for the meta-analysis and the subgroup analysis, respectively. The meta-analysis and the subgroup analysis used the same participants that were recruited in the analyses of the four prior experiments. For the meta-analysis (see “Meta_analysis” in Appendix A for database), the mean, standard deviation and sample size of each experiment were extracted respectively and organized into a single excel sheet for further calculation. The rows indicate the experiments and the columns indicate related summaries including the experiment number, sample size, mean and standard deviation for the experimental (eye) condition, sample size, and mean and standard deviation for the control condition. For the subgroup analysis (see “Subgroup_analysis” in Appendix A), the participants of each experiment were further segmented into an independence subgroup and an interdependence subgroup according to the measurement or the manipulation of self-construal. The mean, standard deviation, and sample size were then extracted respectively and organized into a single excel sheet for further calculation. The rows indicate the subgroups and the columns indicate related summaries including subgroup number, sample size, mean and standard deviation for the experimental (eye) condition, sample size, mean and standard deviation for the control condition, and the subgroup assignment (i.e., 1 = independent self-construal; 2 = interdependent self-construal).
DOI: 10.17632/mrk7355wtk.1
title: A Locus Coeruleus- dorsal CA1 dopaminergic circuit modulates memory linking. Chowdhury et al.

description: Main figures 1 to 7 and Supplemental figures 1 to 4, table 1: Excel sheets of raw values used to quantification
Please refer to the individual figure legends for further information
DOI: 10.17632/g6mmxc4pnb.1
title: Spring locations and descriptions in the Hikurangi Forearc (compiled by A. G. Reyes)

description: Spring locations and descriptions in the Hikurangi margin forearc compiled from the literature since 1877 and from fieldwork by A G Reyes since 1992
DOI: 10.17632/2r4jdxbrb2.1
title: Inhaled diesel exhaust particles result in microbiome-related systemic inflammation and altered cardiovascular disease biomarkers in C57Bl/6 male mice.

description: The gut microbiota plays a vital role in host homeostasis and is associated with inflammation and cardiovascular disease (CVD) risk. Exposure to particulate matter (PM) is a known mediator of inflammation and CVD and is reported to promote dysbiosis and decreased intestinal integrity. However, the role of inhaled traffic-generated PM on the gut microbiome and its corresponding systemic effects are not well-characterized. Thus, we investigated the hypothesis that exposure to inhaled diesel exhaust particles (DEP) alters the gut microbiome and promotes microbial-related inflammation and CVD biomarkers. 4-6-wk-old male C57Bl/6 mice on either a low-fat (LF) or high-fat (HF) diet were exposed via oropharyngeal aspiration to 35 μg DEP suspended in 35 μl saline or saline only (CON) 2x/week for 30 days. To determine whether probiotics could prevent diet or DEP exposure mediated alterations in the gut microbiome or systemic outcomes, a subset of animals on the HF diet was treated orally with 0.3 g/day of Winclove Ecologic Barrier probiotics throughout the study. Our results show that inhaled DEP exposure alters gut microbial profiles, including reducing Actinobacteria and expanding Verrucomicrobia and Proteobacteria. We observed increased circulating LPS, altered circulating cytokines, and CVD biomarkers in DEP-exposed and HF diet mice. Furthermore, probiotics attenuated the reduction of Actinobacteria and expansion of Proteobacteria and mitigated inflammatory and CVD responses in DEP exposed and/or HF diet mice. Key findings of this study are that inhaled DEP exposure altered gut microbial profiles that play a role in systemic inflammation and early CVD biomarkers that can be mitigated with probiotics.
DOI: 10.17632/rpy5fyt8c6.1
title: Andrade, Ekponon, Jeanneret JFE replication files

description: Matlab and Stata files for "Sovereign Risk Premia and Global Macroeconomic Conditions" (Journal of Financial Economics)
DOI: 10.17632/k6p2b7nmy2.1
title: A highly sensitive immunoassay for determination of immune response to SARS-CoV-2 in capillary blood samples

description: Data corresponding to the study
DOI: 10.34740/kaggle/dsv/4792305
title: Spotify Daily Top 50 Of 59 countries + Global
DOI: 10.34740/kaggle/dsv/4792320
title: Spotify Daily Top 50 Of 63 countries + Global
DOI: 10.6084/m9.figshare.21800865
title: All figures and tables.csv

description: The datasheet uploaded here consists of data that is used for generating the results.
DOI: 10.6084/m9.figshare.21800865.v1
title: All figures and tables.csv

description: The datasheet uploaded here consists of data that is used for generating the results.
DOI: 10.34740/kaggle/dsv/4792342
title: 🇺🇦 Ukraine Conflict Twitter Dataset
DOI: 10.34740/kaggle/dsv/4792356
title: Elon Musk Tweets
DOI: 10.6084/m9.figshare.21218402
title: Downscaled NTL Data

description: The downscaled 250m NTL data
DOI: 10.6084/m9.figshare.21218402.v2
title: Downscaled NTL Data

description: The downscaled 250m NTL data
DOI: 10.6084/m9.figshare.21218402.v1
title: Downscaled NTL Data

description: The downscaled 250m NTL data of YRD, China
DOI: 10.6084/m9.figshare.17694767
title: Survey Data

description: The data provide information about awareness of and interaction with physician rating websites in Austria.
DOI: 10.6084/m9.figshare.21800873
title: Raw Data: Structure-Property

description: This dataset provides raw data to reproduce the structure-property relationships.
DOI: 10.6084/m9.figshare.19336535.v2
title: Data recorded sheet for drought tolerance

description: Data is contain with the seedling and booting stage parameters which recorded during the research work.
DOI: 10.5281/zenodo.6997371
title: SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"

description: This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution". The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (SESMG) v0.4.0rc1 The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.
DOI: 10.5281/zenodo.6997372
title: SESMG scenario-files of the study "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution"

description: This dataset contains model scenario-files belonging to the publication "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution". The individual scenarios can be executed and evaluated with the "Spreadsheet Energy System Model Generator" (SESMG) v0.4.0rc1 The respective file names indicate to which model run mentioned in the main study the scenario-files belong. For model runs for which no sepparate scenario file exists, the scenario "reference.xlsx" with adjusted SESMG settings was used.
DOI: 10.6084/m9.figshare.21800906
title: Processed Data: Structure-Property Relationship

description: Data set provides the process data concerning the structure-property relationship.
DOI: 10.6084/m9.figshare.21800906.v1
title: Processed Data: Structure-Property Relationship

description: Data set provides the process data concerning the structure-property relationship.
DOI: 10.5281/zenodo.7496444
title: COVID-19 Impact on European Higher Education Survey Data

description: This dataset consists of the results of the first European-Union-wide survey on the potential long-term impacts of COVID-19 on higher education (Huth, M. & Cominola, A., 2022 forthcoming), evaluating over 800 responses from students and faculty members of higher education institutions located in 17 different European countries. The data consists of variables related to the actual pre-pandemic, pandemic and intended future frequency of use of various educational tools and formats, as well as students and faculty members' attitude toward retaining digital teaching formats and media post-pandemic. The data is shared in SPSS file format (.sav) and as a .CSV file. A detailed item bank label overview is attached in excel format. The attitude related items consists of Technology Acceptance Model (TAM) construct variables following items used by Rizun et al. (2021) and Vladova et al. (2021). Following guidelines (Hair et al. 2019; Henseler, Ringle, and Sarstedt 2015) to establish discriminant validity before analysing the TAM variables in a structural equation model, the objective had to be disregarded. The data might still yield interesting descriptive insights nonetheless, which is why they are published here along with the variables used in the forthcoming publication by Huth and Cominola (2022).
DOI: 10.5281/zenodo.7496443
title: COVID-19 Impact on European Higher Education Survey Data

description: This dataset consists of the results of the first European-Union-wide survey on the potential long-term impacts of COVID-19 on higher education (Huth, M. & Cominola, A., 2022 forthcoming), evaluating over 800 responses from students and faculty members of higher education institutions located in 17 different European countries. The data consists of variables related to the actual pre-pandemic, pandemic and intended future frequency of use of various educational tools and formats, as well as students and faculty members' attitude toward retaining digital teaching formats and media post-pandemic. The data is shared in SPSS file format (.sav) and as a .CSV file. A detailed item bank label overview is attached in excel format. The attitude related items consists of Technology Acceptance Model (TAM) construct variables following items used by Rizun et al. (2021) and Vladova et al. (2021). Following guidelines (Hair et al. 2019; Henseler, Ringle, and Sarstedt 2015) to establish discriminant validity before analysing the TAM variables in a structural equation model, the objective had to be disregarded. The data might still yield interesting descriptive insights nonetheless, which is why they are published here along with the variables used in the forthcoming publication by Huth and Cominola (2022).
DOI: 10.5281/zenodo.7245548
title: celegans scRNA data set objects

description: Combined and converted scRNA data from (Packer and Zhu et al. 2019), see a detailed description of the study here: https://www.science.org/doi/full/10.1126/science.aax1971 Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126954 converted into Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package. If you use this data, please cite Packer and Zhu et al. 2019.
description: {"references": ["Packer, J.S., Zhu, Q., Huynh, C., Sivaramakrishnan, P., Preston, E., Dueck, H., Stefanik, D., Tan, K., Trapnell, C., Kim, J. and Waterston, R.H., 2019. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science, 365(6459), p.eaax1971.", "Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. and Regev, A., 2015. Spatial reconstruction of single-cell gene expression data. Nature biotechnology, 33(5), pp.495-502.", "Wolf, F.A., Angerer, P. and Theis, F.J., 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), pp.1-5."]}
DOI: 10.5281/zenodo.7496490
title: celegans scRNA data set objects

description: Combined and converted scRNA data from (Packer and Zhu et al. 2019), see a detailed description of the study here: https://www.science.org/doi/full/10.1126/science.aax1971 Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126954 converted into Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package. If you use this data, please cite Packer and Zhu et al. 2019.
description: {"references": ["Packer, J.S., Zhu, Q., Huynh, C., Sivaramakrishnan, P., Preston, E., Dueck, H., Stefanik, D., Tan, K., Trapnell, C., Kim, J. and Waterston, R.H., 2019. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science, 365(6459), p.eaax1971.", "Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. and Regev, A., 2015. Spatial reconstruction of single-cell gene expression data. Nature biotechnology, 33(5), pp.495-502.", "Wolf, F.A., Angerer, P. and Theis, F.J., 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), pp.1-5."]}
DOI: 10.5281/zenodo.7245547
title: celegans scRNA data set objects

description: Combined and converted scRNA data from (Packer and Zhu et al. 2019), see a detailed description of the study here: https://www.science.org/doi/full/10.1126/science.aax1971 Data were downloaded from https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126954 converted into Seurat object and converted into loom and AnnData (h5ad) files to be able to analyse with e.g. python scanpy package. If you use this data, please cite Packer and Zhu et al. 2019.
description: {"references": ["Packer, J.S., Zhu, Q., Huynh, C., Sivaramakrishnan, P., Preston, E., Dueck, H., Stefanik, D., Tan, K., Trapnell, C., Kim, J. and Waterston, R.H., 2019. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science, 365(6459), p.eaax1971.", "Satija, R., Farrell, J.A., Gennert, D., Schier, A.F. and Regev, A., 2015. Spatial reconstruction of single-cell gene expression data. Nature biotechnology, 33(5), pp.495-502.", "Wolf, F.A., Angerer, P. and Theis, F.J., 2018. SCANPY: large-scale single-cell gene expression data analysis. Genome biology, 19(1), pp.1-5."]}
DOI: 10.34740/kaggle/dsv/4792654
title: YouTube Daily
DOI: 10.5281/zenodo.7496458
title: EbrahimBidokh/NewTest: ReleaseTest

description: empty
DOI: 10.5281/zenodo.7496457
title: EbrahimBidokh/NewTest: ReleaseTest

description: empty
DOI: 10.34740/kaggle/dsv/4792721
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.6084/m9.figshare.20744107.v1
title: Qinling slab tearing-Supplementary Tables.xlsx

description: The dataset includes five supplementary tables for manuscript titled "Evidence of Late Triassic vertical slab tearing in the Qinling Orogen (central China) from multiproxy geochemical and isotopic imaging" submitted to <strong>Journal of Geophysical Research: Solid Earth</strong>.
DOI: 10.34740/kaggle/dsv/4792829
title: 2021 World Population (updated daily)
DOI: 10.34740/kaggle/dsv/4792873
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.21800965
title: G20P24_G3P2_upload.xls

description: The data about 24-bus, 20-node and 2-bus, 3-node systems
DOI: 10.6084/m9.figshare.21800965.v1
title: G20P24_G3P2_upload.xls

description: The data about 24-bus, 20-node and 2-bus, 3-node systems
DOI: 10.5281/zenodo.7494877
title: Mediterranean Marine Heatwaves (MHWs) as detected from ESA CCI SST 0.05°x0.05° covering 1982-2021

description: Daily records of Mediterranean Marine Heatwaves (MHW) resulting from detection applied to the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) satellite product on a regular 0.05°x0.05° grid, covering the period 01/01/1982-31/12/2021. The MHW detection has been carried out via Hobday's method (Hobday et al. 2016). The <strong>mhw_original</strong> field provides the intensity of anomaly [°C] of MHW events detected on the original SST data, while the <strong>mhw_detrended</strong> field describes the intensity of anomaly [°C] of MHW events detected on detrended SST data, via X-11 seasonal adjustment procedure. The production of this dataset has been sustained with the support of the European Space Agency (ESA) "deteCtion and threAts of maRinE Heat waves" project (CAREHeat; grant number: 4000137121/21/I-DT). References:<br> Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C., ... & Wernberg, T. (2016). <br> A hierarchical approach to defining marine heatwaves. Progress in Oceanography 141:227–238, https://doi.org/10.1016/j.pocean.2015.12.014.
DOI: 10.5281/zenodo.7494876
title: Mediterranean Marine Heatwaves (MHWs) as detected from ESA CCI SST 0.05°x0.05° covering 1982-2021

description: Daily records of Mediterranean Marine Heatwaves (MHW) resulting from detection applied to the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature (SST) satellite product on a regular 0.05°x0.05° grid, covering the period 01/01/1982-31/12/2021. The MHW detection has been carried out via Hobday's method (Hobday et al. 2016). The <strong>mhw_original</strong> field provides the intensity of anomaly [°C] of MHW events detected on the original SST data, while the <strong>mhw_detrended</strong> field describes the intensity of anomaly [°C] of MHW events detected on detrended SST data, via X-11 seasonal adjustment procedure. The production of this dataset has been sustained with the support of the European Space Agency (ESA) "deteCtion and threAts of maRinE Heat waves" project (CAREHeat; grant number: 4000137121/21/I-DT). References:<br> Hobday, A. J., Alexander, L. V., Perkins, S. E., Smale, D. A., Straub, S. C., Oliver, E. C., ... & Wernberg, T. (2016). <br> A hierarchical approach to defining marine heatwaves. Progress in Oceanography 141:227–238, https://doi.org/10.1016/j.pocean.2015.12.014.