cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.14870127.v1
title: Supplementary Materials for Serum metabolomics reveals dysregulation and diagnostic potential of oxylipins in tumor-induced osteomalacia

description: Excessive production of fibroblast growth factor 23 (FGF23) by tumor was considered as the main pathogenesis in tumor-induced osteomalacia (TIO). Despite its importance to comprehensive understanding of pathogenesis and diagnosis, the regulation of systemic metabolism in TIO remains unclear. By means of liquid chromatography-tandem mass spectrometry (LC-MS) based metabolomics, we analyzed the metabolic profile from 96 serum samples (32 initial diagnosis TIO patients, pairwise samples after tumor resection and 32 matched healthy control subjects). Metabolomic profiling revealed distinct alterations between TIO and HC cohort. Differential metabolites were screened and conducted to functional clustering and annotation. Significantly enriched pathway was found involved in arachidonic acid metabolism. A combination of 5 oxylipins, 4-HDoHE, leukotriene B4, 5-HETE, 17-HETE and 9,10,13-TriHOME, demonstrated a high sensitivity and specificity panel for TIO prediction screened by random forest (RF) algorithm (AUC=0.951, 95% confidence interval, CI 0.827-1). Supported vector machine (SVM) model and partial least-squares (PLS) model were conducted to validate the predictive capabilities of the diagnostic panel. In summary, for the first time, we provide the global profile of metabolomes and identify potential diagnostic biomarkers of TIO. The present work may offer novel insights into the pathogenesis of TIO.
DOI: 10.6084/m9.figshare.2445310.v4
title: Dataset for paper "Local cooling and warming effects of forest based on satellite data"

description: Updated on 29 June, 2021:Added data for monthly mean differences in LST, albedo, and ET between forest and open land at 0.5 degree. This data were used by "Meier, R., Davin, E. L., Lejeune, Q., Hauser, M., Li, Y., Martens, B., … Thiery, W. (2018). Evaluating and improving the Community Land Model’s sensitivity to land cover. Biogeosciences, 15(15), 4731–4757. https://doi.org/10.5194/bg-15-4731-2018" to evaluate CLM model.<br>File name: diff_lst_albedo_et_monthly_halfdeg.nc--------------------Updated on 28 August, 2017:Added data for annual snow frequency and rainfall at each forest and open land comparison sample location (data for Fig 4).File name: snow_rainfall_005degree.nc<br>--------------------Updated on 21 August, 2017:Uploaded data for the comparison samples of forest minus open land across the globe at 0.05 degree for daytime, nighttime, daily mean LST, annual albedo, and daily ET. These comparison samples can reproduce most of the results from the paper (Fig1, part of Fig3, and Fig4). They can be aggregated to 1-degree map or to derive the latitudinal pattern shown in the paper.<br>File name: delta_lst_albedo_et_005degree.nc<br>-------------------------------This is the dataset used to create Figure 1 to Figure 3 in Li et al 2015 (Local cooling and warming effects of forest based on satellite data, Nature Communications). <br>The dataset is saved as netcdf file, named as Fig1_NC.nc, Fig2_NC.nc, and Fig3_NC.nc. Figure 1 to 4 are also included in the dataset. <br><br>When using the data, please cite the original paper "Li, Y. et al. Local cooling and warming effects of forest based on satellite data. Nat. Commun. 6:6603, (2015)" and also cite or acknowledge the dataset on figshare (doi:10.6084/m9.figshare.2445310). <br>If you have any questions, please feel free to contact Yan Li. Email: yanli.geo@gmail.com <br>
DOI: 10.6084/m9.figshare.2445310.v3
title: Dataset for paper "Local cooling and warming effects of forest based on satellite data"

description: Updated on 28 August, 2017:Added data for annual snow frequency and rainfall at each forest and open land comparison sample location (data for Fig 4).File name: snow_rainfall_005degree.nc<br>--------------------Updated on 21 August, 2017:Uploaded data for the comparison samples of forest minus open land across the globe at 0.05 degree for daytime, nighttime, daily mean LST, annual albedo, and daily ET. These comparison samples can reproduce most of the results from the paper (Fig1, part of Fig3, and Fig4). They can be aggregated to 1-degree map or to derive the latitudinal pattern shown in the paper.<br>File name: delta_lst_albedo_et_005degree.nc<br>-------------------------------This is the dataset used to create Figure 1 to Figure 3 in Li et al 2015 (Local cooling and warming effects of forest based on satellite data, Nature Communications). <br>The dataset is saved as netcdf file, named as Fig1_NC.nc, Fig2_NC.nc, and Fig3_NC.nc. Figure 1 to 4 are also included in the dataset. <br><br>When using the data, please cite the original paper "Li, Y. et al. Local cooling and warming effects of forest based on satellite data. Nat. Commun. 6:6603, (2015)" and also cite or acknowledge the dataset on figshare (doi:10.6084/m9.figshare.2445310). <br>If you have any questions, please feel free to contact Yan Li. Email: yanli.geo@gmail.com <br>
DOI: 10.6084/m9.figshare.14411975.v1
title: Te Waihora Groundwater Chemistry

description: A dataset of 40 hydrochemistry samples taken in groundwater and surface water around Te Waihora/Lake Ellesmere, a coastal lagoon in New Zealand. Samples were analysed for major ions, stable water isotopes, nutrients and trace metals.
DOI: 10.6084/m9.figshare.14458896.v2
title: JEDI Billion Molecules against Covid-19: compounds synthesized

description: On May 4th 2020 a GrandChallenge entitled "Billion molecules against Covid-19" was launched by the Joint European Disruptive Initiative (JEDI). Teams from all over the world provided lists of compounds with suspected inhibitory properties versus specific SARS-CoV-2 and human host protein targets. Here we disclose 878 compounds, which were selected by the 20 top-ranked teams ('finalists'). Some finalists teams did not have compounds produced, because they were either unstable or synthetically unfeasible. The compounds were synthesized thanks to JEDI fundraising, and are in the process of being tested in biophysical assays, cell-based assays, etc. Purity data for each compound by LC-MS (liquid chromatography mass spectrometry) is provided. The data file contains SMILES information, molecular weight, purity, and solubility information (in DMSO dimethylsulfoxide). The compounds should be active against either one of the following SARS-CoV-2 protein targets: nsp3, nsp5, nucleocapsid (N), Spike (S), or RdRp (Nsp12), or against human host target TMPRSS2. Authors are listed in random order. Some co-authors are members of the Scientific Committee or organizing team of the GrandChallenge. All work has been performed with an open science mindset, and all data will be shared in the public domain. A publication is in preparation to disclose the experimental results. In version 1 of this data set it is likely that some co-author data is missing, which will be corrected in following versions. The data has been made public by the program manager of the GrandChallenge, Prof. Thomas M. Hermans. Author contributions are also available in spreadsheet format. Please contact covid19@jedi.foundation for more information, or check https://www.jedi.foundation/<br><br><br><em><strong>About JEDI </strong>- The Joint European Disruptive Initiative (JEDI) seeks to bring Europe in a leadership position in breakthrough technologies by launching Scientific and Technology GrandChallenges to push the frontiers of innovation. </em><em>It is the European advanced research projects agency (ARPA) with a radical method based on excellence, speed, high expectations, bold risk-taking & no geographical return </em><em>to solve major societal issues in environment, healthcare, digital, education, oceans & space. Driven by humanistic values, JEDI is a not-for-profit organization financed by foundations, governments, corporates and powered by the contribution of 4.100 technology and scientific leaders from academia, corporations and deeptech startups in 29 countries in Europe.</em><br>
DOI: 10.6084/m9.figshare.3761562.v1907
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1906
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.7581473.v1
title: Datasets for "Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States"

description: This repository is for the datasets and codes for “Excessive rainfall leads to maize yield loss of a comparable magnitude to extreme drought in the United States” (Li et al. 2019 GCB). The datasets include maize yield, climate data, maize yield loss from crop insurance data in the US, etc. The datasets can reproduce the results of the paper and they can also be reused to explore other topics. Please cite Li et al (2019) or acknowledge this dataset (DOI: 10.6084/m9.figshare.7581473) when using it.
DOI: 10.6084/m9.figshare.14899329.v4
title: Data For Figures

description: Data for validation denoising method
DOI: 10.6084/m9.figshare.14899329.v3
title: Data For Figures

description: Data for validation denoising method
DOI: 10.48370/ofd/5fse5y/xzol9n
title: Golovinomyces inulae (U. Braun & H. D. Shin F-3242.tif

description: :unav
DOI: 10.6084/m9.figshare.14904783.v1
title: DEGs reanalysis of GSE113941

description: 281 DEGs are identified by analyzing GSE113941 dataset.
DOI: 10.48370/ofd/5fse5y
title: Golovinomyces inulae (U. Braun H. D. Shin, 3242

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.14904807.v2
title: String interactions of DEGs

description: String interactions of DEGs reanalyzed from GSE113941.
DOI: 10.6084/m9.figshare.14904807.v1
title: String interactions of DEGs

description: String interactions of DEGs reanalyzed from GSE113941.
DOI: 10.6084/m9.figshare.14905515.v1
title: Table 34. Research methods used in DC (% of Quantitative articles to Total articles per Journal Ranking)

description: Research methods used in DC (% of Quantitative articles to Total articles per Journal Ranking)<b></b>
DOI: 10.6084/m9.figshare.3761562.v1911
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1910
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.14910522.v3
title: WIND Uydu Verileri.xlsx

description: 1995-2019 yılları arasında WIND uydusunun ölçmüş olduğu şoklara ait yoğunluk, hız ve şok tiplerine ait tablo.
DOI: 10.6084/m9.figshare.14910522.v2
title: WIND Uydu Verileri.xlsx

description: 1995-2019 yılları arasında WIND uydusunun ölçmüş olduğu şoklara ait yoğunluk, hız ve şok tiplerine ait tablo.
DOI: 10.48370/ofd/1ezz1w/ms6u5h
title: Golovinomyces macrocarpus (Speer) U. Braun F-2430.tif

description: :unav
DOI: 10.48370/ofd/1ezz1w
title: Golovinomyces macrocarpus (Speer) U. Braun, 2430

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.14921256.v2
title: Nature-placemaking at GUI in Singapore: Survey Data

description: Survey data collected from members at Ground-Up Initiative (GUI) in Singapore regarding their engagement and perceptions about nature-placemaking activities held at GUI.
DOI: 10.48370/ofd/xouudr/hsxqho
title: Golovinomyces macrocarpus (Speer) U. Braun F-0432.tif

description: :unav
DOI: 10.48370/ofd/xouudr
title: Golovinomyces macrocarpus (Speer) U. Braun, 432

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.14914605.v1
title: Chloroplast short-read alignment of 50 broomcorn millet accessions

description: Chloroplast short-read alignment of 50 broomcorn millet accession against the reference sequence NC_029732.1 (NC_029732.1 is identical with KU343177.1)
DOI: 10.5281/zenodo.5946832
title: GIS data

description: G
DOI: 10.5281/zenodo.5946831
title: GIS data

description: G
DOI: 10.6084/m9.figshare.14260736.v3
title: Norfish 21 Scottish Cod Fishery 1468–179

description: The Scottish Cod Fishery was focused largely in the North Sea along the eastern Scottish coastline, but included some activity along the western coast of Scotland into the Atlantic and the northern parts of the Irish Sea. The overall effort was small in scale compared with the huge industrial activities of the French and English cod fishers who, while highly active in Newfoundland and off the Grand Banks from c.1500 onwards, were also engaged in the North Sea and beyond to Iceland and the Norwegian Sea. Nevertheless, cod fishing accounted for a large proportion of Scotland’s fishing effort which severally included herring and salmon as major species for extraction, and a host of other species in lesser numbers.
DOI: 10.6084/m9.figshare.14260736.v2
title: Norfish Scottish Cod Fishery 1468–179

description: The Scottish Cod Fishery was focused largely in the North Sea along the eastern Scottish coastline, but included some activity along the western coast of Scotland into the Atlantic and the northern parts of the Irish Sea. The overall effort was small in scale compared with the huge industrial activities of the French and English cod fishers who, while highly active in Newfoundland and off the Grand Banks from c.1500 onwards, were also engaged in the North Sea and beyond to Iceland and the Norwegian Sea. Nevertheless, cod fishing accounted for a large proportion of Scotland’s fishing effort which severally included herring and salmon as major species for extraction, and a host of other species in lesser numbers.
DOI: 10.48370/ofd/9dyowr/op0fsw
title: Golovinomyces macrocarpus (Speer) U. Braun F-0428.tif

description: :unav
DOI: 10.48370/ofd/9dyowr
title: Golovinomyces macrocarpus (Speer) U. Braun, 428

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.3761562.v1914
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1913
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.12651074.v11
title: Extended Data for A rooted phylogeny resolves early bacterial evolution

description: Extended Data for A rooted phylogeny resolves early bacterial evolution
DOI: 10.6084/m9.figshare.12651074.v10
title: Extended Data for A rooted phylogeny resolves early bacterial evolution

description: Extended Data for A rooted phylogeny resolves early bacterial evolution
DOI: 10.6084/m9.figshare.12271292.v16
title: Arrival catalog of coronal mass ejections observed with STEREO Heliospheric Imagers (HELCATS ARRCAT 2.0)

description: ARRIVAL CATALOGUE 2.0 <br>In this ARRival CATalog (ARRCAT), the arrivals of solar coronal mass ejections that were tracked in the STEREO heliospheric imagers in the HELCATS project are calculated.<br>ARRCAT lists modeled arrivals of CMEs at various spacecraft and planets, between April 2007 - June 2021, based on the HIGeoCAT catalog of CMEs established at RAL Space, UK (R. A. Harrison, J. A. Davies, D. Barnes). <br>This is version 2.0, released 2020-May-13, updated 2021-Jul-9.<br>https://doi.org/10.6084/m9.figshare.12271292<br>It is based on this HIGeoCAT version: https://www.helcats-fp7.eu/catalogues/data/HCME_WP3_V06.vot using the SSEF30 model.<br>The catalog is available as python pandas dataframe (pickle), python numpy arrays (pickle, as recarray and structured array),npy (numpy, no pickle), json, csv, xlsx, txt, hdf5, html at https://helioforecast.space/arrcathttps://www.helcats-fp7.eu<br>Number of events in ARRCAT: 1658Targets: Earth_L1, STEREO-A, STEREO-B, Solar Orbiter, Parker Solar Probe (PSP), Bepi Colombo, Ulysses, Venus, Mercury, Mars.<br>For further description, see header file.<br>source code: arrcat.ipynb inhttps://github.com/cmoestl/heliocats<br>Rules: If results are produced with this catalog for peer-reviewed scientific publications,please contact christian.moestl@oeaw.ac.at for co-authorships.<br>
DOI: 10.6084/m9.figshare.12271292.v15
title: Arrival catalog of coronal mass ejections observed with STEREO Heliospheric Imagers (HELCATS ARRCAT 2.0)

description: ARRIVAL CATALOGUE 2.0 <br>In this ARRival CATalog (ARRCAT), the arrivals of solar coronal mass ejections that were tracked in the STEREO heliospheric imagers in the HELCATS project are calculated.<br>ARRCAT lists modeled arrivals of CMEs at various spacecraft and planets, between April 2007 - March 2021, based on the HIGeoCAT catalog of CMEs established at RAL Space, UK (R. A. Harrison, J. A. Davies, D. Barnes). <br>This is version 2.0, released 2020-May-13, updated 2021-Apr-23.<br>https://doi.org/10.6084/m9.figshare.12271292<br>It is based on this HIGeoCAT version: https://www.helcats-fp7.eu/catalogues/data/HCME_WP3_V06.vot using the SSEF30 model.<br>The catalog is available as python pandas dataframe (pickle), python numpy arrays (pickle, as recarray and structured array),npy (numpy, no pickle), json, csv, xlsx, txt, hdf5, html at https://helioforecast.space/arrcathttps://www.helcats-fp7.eu<br>Number of events in ARRCAT: 1631Targets: Earth_L1, STEREO-A, STEREO-B, Solar Orbiter, Parker Solar Probe (PSP), Bepi Colombo, Ulysses, Venus, Mercury, Mars.<br>For further description, see header file.<br>source code: arrcat.ipynb inhttps://github.com/cmoestl/heliocats<br>Rules: If results are produced with this catalog for peer-reviewed scientific publications,please contact christian.moestl@oeaw.ac.at for co-authorships.<br>
DOI: 10.48370/ofd/xjvwtq/ttnopb
title: Golovinomyces magnicellulatus (U. Braun) Heluta F-0543.tif

description: :unav
DOI: 10.48370/ofd/xjvwtq
title: Golovinomyces magnicellulatus (U. Braun) Heluta, 543

description: Fungarium Mikromycetes
DOI: 10.6084/m9.figshare.12130980.v102
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.12130980.v101
title: MOOD Maps of Google community mobility change during the COVID-19 outbreak

description: The MOOD project (MOnitoring Outbreak events for Disease surveillance in a data science context. H2020) has geo-referenced the data Google has published as a series of PDF files presenting reports on national and subnational human mobility levels relative to a baseline data of late January 2020. The details and the PDF files can be found at https://www.google.com/covid19/mobility/.<br>More detail on these files can be found at https://www.moodspatialdata.com/humanmobilityforcovid19 <br>The first set of data were released on April 2 2020 and have been revised weekly since then. The maps now utilise the CSV data released by Google. Please note that the maps figures use a mean of the previous three days, while the Google PDFs use a single days data so there will be differences between values in our maps when compare to the Google PDFs.<br>The authors have extracted the majority of these data into a series of excel spreadsheets. Each worksheet provides the data for % change in numbers of records at various types of location categories illustrated by: retail and recreation, grocery and pharmacy, parks and beaches, transit stations, workplaces and residential (columns f to K). A second set of columns calculates the difference of each value from the mean values for each category (columns L to P) Columns A to E contain geographical details. Column Q contains the names used to link to a mapping file.There are separate worksheets for the date of the data from each dated release (e.g. 2903, 0504 etc.) and separate worksheets calculating the changes between specific dates.<br>A second spreadsheet has been added calculating the 3 day moving mean of each day from the 15th of February. Each day is referenced by the Gregorian calendar day count. So day 48 = Feb 17th.<br>The maps (for EU & Global) display these data. We provide 600 dpi jpegs of the Global (“WD”) and European (“EU”) mapped values at the latest date available, for each of the mobility categories: retail and recreation (“retrec”) , grocery and pharmacy (“grocphar”) , parks (“parks”) , transit stations (“transit”), residential (“resid”) and workplaces (“work”). We also provide maps of the changes from the previous week (“ch”).<br>All data extracting and subsequent processing have been carried out by ERGO (Environmental Research Group Oxford, c/o Dept Zoology, University of Oxford) on behalf of the MOOD H2020 project. Data will be periodically updated. Additional maps can be obtained on request to the authors.
DOI: 10.6084/m9.figshare.3761562.v1916
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.6084/m9.figshare.3761562.v1915
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.