cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4603812
title: Common Nightingale - Luscinia megarhynchos

description: Dataset provided by Eamonn Keogh Stored on Zenodo as backup for Stumpy Motif Discovery
DOI: 10.6084/m9.figshare.12762074
title: Investigating markers of reprogramming potential in somatic cell lines derived from matched donors

description: Data generated and analysed during the study "Investigating markers of reprogramming potential in somatic cell lines derived from matched donors," which includes data obtained and analysed using a Clopper-Pearson binomial assessment spreadsheet and SAS codes. Obtained data, codes, and outcomes have all been included.
DOI: 10.6084/m9.figshare.12762074.v1
title: Investigating markers of reprogramming potential in somatic cell lines derived from matched donors

description: Data generated and analysed during the study "Investigating markers of reprogramming potential in somatic cell lines derived from matched donors," which includes data obtained and analysed using a Clopper-Pearson binomial assessment spreadsheet and SAS codes. Obtained data, codes, and outcomes have all been included.
DOI: 10.5281/zenodo.4603825
title: Requirement-datasetN on Weight scales from stakeholders

description: The weight scales captured for each stakeholder's requirement.
DOI: 10.5281/zenodo.4603824
title: Requirement-datasetN on Weight scales from stakeholders

description: The weight scales captured for each stakeholder's requirement.
DOI: 10.6084/m9.figshare.12662540
title: Validation of reference genes for use in untreated bovine fibroblasts RT-qPCR dataset.xlsx

description: Source data generated and analyzed during a study evaluating reference genes for use in untreated bovine fibroblasts.
DOI: 10.6084/m9.figshare.12662540.v1
title: Validation of reference genes for use in untreated bovine fibroblasts RT-qPCR dataset.xlsx

description: Source data generated and analyzed during a study evaluating reference genes for use in untreated bovine fibroblasts.
DOI: 10.5281/zenodo.4603842
title: Requirement-dataset on Linguistic Scales from stakeholders

description: This dataset describes the ranking scales captured for each stakeholder on each requirement analyzed.
DOI: 10.5281/zenodo.4603841
title: Requirement-dataset on Linguistic Scales from stakeholders

description: This dataset describes the ranking scales captured for each stakeholder on each requirement analyzed.
DOI: 10.5281/zenodo.4603844
title: BabySlakh

description: <strong>Introduction</strong> BabySlakh is a tiny version of Slakh2100 (zenodo link) that is useful for debugging. It consists of the first 20 tracks of Slakh2100 (i.e., Track00001 through Track00020). All of the audio is in the wav format and has a sample rate of 16 kHz. BabySlakh is ready to go once it's unzipped. <strong>About Slakh</strong> The Synthesized Lakh (Slakh) Dataset is a dataset of multi-track audio and aligned MIDI for music source separation and multi-instrument automatic transcription. Individual MIDI tracks are synthesized from the Lakh MIDI Dataset v0.1 using professional-grade sample-based virtual instruments, and the resulting audio is mixed together to make musical mixtures. This release of Slakh, called Slakh2100, contains 2100 automatically mixed tracks and accompanying, aligned MIDI files, synthesized from 187 patches categorized into 34 classes. <strong>Citing Slakh</strong> If you use Slakh2100 or generate data using the same method we ask that you cite it using the following bibtex entry: <pre><code>@inproceedings{manilow2019cutting, title={Cutting Music Source Separation Some {Slakh}: A Dataset to Study the Impact of Training Data Quality and Quantity}, author={Manilow, Ethan and Wichern, Gordon and Seetharaman, Prem and Le Roux, Jonathan}, booktitle={Proc. IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA)}, year={2019}, organization={IEEE} }</code></pre>
DOI: 10.6084/m9.figshare.14214077
title: Database 1

description: Database of local advisory structures of a sample of one hundred Brazilian municipalities with more than 200 thousand inhabitants.<br>
DOI: 10.6084/m9.figshare.14214077.v1
title: Database 1

description: Database of local advisory structures of a sample of one hundred Brazilian municipalities with more than 200 thousand inhabitants.<br>
DOI: 10.5281/zenodo.4603863
title: Criminals from Russia, Interpol data

description: Criminals from Russia who are on the international wanted list of Interpol. Data as of February 4, 2021.
DOI: 10.5281/zenodo.4603862
title: Criminals from Russia, Interpol data

description: Criminals from Russia who are on the international wanted list of Interpol. Data as of February 4, 2021.
DOI: 10.6084/m9.figshare.14214131
title: raw_mapped_bam_from_Galaxy

description: Mapped reads (BAM files) on the UA159 genome. Data published in Werlang et. al. Nature Microbiology 2021https://doi.org/10.1038/s41564-021-00876-1<br><br>Generated using Galaxy: usegalaxy.org<br>Steps used to generate the files from raw sequencing data. This represents Step 0 of the full analysis pipeline available at https://github.com/cwerlang/Smutans-MUC5B-RNASeq<br>0. The raw bam files are available from the Gene Expression Omnibus under accession number GSE163258<br>1. Load the 'data/external/GCF_000007465.2_ASM746v2_genomic.fna' file and raw bam files into Galaxy2. Map with BWA (Burrows-Wheeler Aligner). Options: unpaired single end short reads3. Download the .bam files into the "raw_mapped_bam_from_Galaxy" folder4. Rename the files to 'xxxx.mapped.bam'5. Fill in the 'data/raw_mapped_bam_from_Galaxy/file_list_mapped_bam.csv' file with the new filenames<br>
DOI: 10.6084/m9.figshare.14214131.v1
title: raw_mapped_bam_from_Galaxy

description: Mapped reads (BAM files) on the UA159 genome. Data published in Werlang et. al. Nature Microbiology 2021https://doi.org/10.1038/s41564-021-00876-1<br><br>Generated using Galaxy: usegalaxy.org<br>Steps used to generate the files from raw sequencing data. This represents Step 0 of the full analysis pipeline available at https://github.com/cwerlang/Smutans-MUC5B-RNASeq<br>0. The raw bam files are available from the Gene Expression Omnibus under accession number GSE163258<br>1. Load the 'data/external/GCF_000007465.2_ASM746v2_genomic.fna' file and raw bam files into Galaxy2. Map with BWA (Burrows-Wheeler Aligner). Options: unpaired single end short reads3. Download the .bam files into the "raw_mapped_bam_from_Galaxy" folder4. Rename the files to 'xxxx.mapped.bam'5. Fill in the 'data/raw_mapped_bam_from_Galaxy/file_list_mapped_bam.csv' file with the new filenames<br>
DOI: 10.5281/zenodo.4603893
title: MEANtools: example data

description: Example data for using MEANtools.
DOI: 10.5281/zenodo.4603892
title: MEANtools: example data

description: Example data for using MEANtools.
DOI: 10.5281/zenodo.4603902
title: MEANtools: RetroRules enzyme annotations

description: RetroRules-pfam annotations necessary for running MEANtools.
DOI: 10.5281/zenodo.4603901
title: MEANtools: RetroRules enzyme annotations

description: RetroRules-pfam annotations necessary for running MEANtools.
DOI: 10.25403/upresearchdata.14186003
title: The perceived impact of transformational leadership on unionized worker productivity

description: Data comprises of the interview schedules for each participants as well as the combined excel sheet where the information was collated and counted.
DOI: 10.25403/upresearchdata.14186003.v1
title: The perceived impact of transformational leadership on unionized worker productivity

description: Data comprises of the interview schedules for each participants as well as the combined excel sheet where the information was collated and counted.
DOI: 10.6084/m9.figshare.13789354.v2
title: Dataset of geochemistry, P-T data and Re-Os isotopic compositions of peridotites from Arctic Canada

description: The dataset provides the whole-rock and mineral major and trace contents for peridotites from Arctic Canada as well as their whole-rock Re-Os isotopic compositions and P-T data.
DOI: 10.5281/zenodo.4603912
title: ISR3D other inputs file for UQ tutorial paper

description: This is the input data for ISR3D simulation with a very small vessel size and stents size designed for the uncertainty quantification tutorial with MUSCLE3.
DOI: 10.5281/zenodo.4603911
title: ISR3D other inputs file for UQ tutorial paper

description: This is the input data for ISR3D simulation with a very small vessel size and stents size designed for the uncertainty quantification tutorial with MUSCLE3.
DOI: 10.25345/c5v22q
title: MassIVE MSV000087048 - Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability
title: Interlaboratory Comparison of Untargeted Mass Spectrometry Data Uncovers Underlying Causes for Variability
DOI: 10.6084/m9.figshare.14213807.v1
title: Network activity traces

description: Dataset and code for Sukenik et al. 2021<br>Ca-imaging of network activity in cultures with 0-100% of inhibitory neurons. Raw activity traces, processing scripts.
DOI: 10.6084/m9.figshare.14213807
title: Network activity traces

description: Dataset and code for Sukenik et al. 2021<br>Ca-imaging of network activity in cultures with 0-100% of inhibitory neurons. Raw activity traces, processing scripts.
DOI: 10.6084/m9.figshare.14208188
title: Patch clamp summaries

description: Data from Sukenik et al. 2021Summary of patch clamp recordings.<br>
DOI: 10.6084/m9.figshare.14208188.v1
title: Patch clamp summaries

description: Data from Sukenik et al. 2021Summary of patch clamp recordings.<br>
DOI: 10.6084/m9.figshare.13557809
title: Bilateral Government Event Sequences Extracted From the GDELT Database for the Period 19 June 2018 to 20 June 2019

description: Data files for the COINE 2020 Paper - "Mining International Political Norms from the GDELT Database".This file contains bilateral government event sequences extracted from the GDELT database for the period 19 June 2018 to 20 June 2019<br><br>Use numpy.load() to load up the file<br><br>Code repository can be found here - https://bitbucket.org/SuraviMsc/suravi-msc-python/src/master/<br>
DOI: 10.6084/m9.figshare.13557809.v4
title: Bilateral Government Event Sequences Extracted From the GDELT Database for the Period 19 June 2018 to 20 June 2019

description: Data files for the COINE 2020 Paper - "Mining International Political Norms from the GDELT Database".This file contains bilateral government event sequences extracted from the GDELT database for the period 19 June 2018 to 20 June 2019<br><br><br>Code repository can be found here - https://bitbucket.org/SuraviMsc/suravi-msc-python/src/master/<br>
DOI: 10.6084/m9.figshare.14214209.v1
title: New Zealand stepwise regression data for modelling dithionite-P

description: National dataset used to create an equation to model the size of dithionite-P and WEP pools across New Zealand
DOI: 10.6084/m9.figshare.14214209
title: New Zealand stepwise regression data for modelling dithionite-P

description: National dataset used to create an equation to model the size of dithionite-P and WEP pools across New Zealand
DOI: 10.6084/m9.figshare.14214209.v3
title: New Zealand stepwise regression data for modelling dithionite-P

description: National dataset used to create an equation to model the size of dithionite-P and WEP pools across New Zealand
DOI: 10.6084/m9.figshare.14214209.v2
title: New Zealand stepwise regression data for modelling dithionite-P

description: National dataset used to create an equation to model the size of dithionite-P and WEP pools across New Zealand
DOI: 10.7284/139678
title: Anemometer data collected during research cruise KM1609 using a RM Young 85106 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139855
title: Multibeam Echosounder data collected during research cruise KM1609 using a Kongsberg EM122 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.7284/139856
title: Multibeam Echosounder data collected during research cruise KM1609 using a Kongsberg EM122 instrument system onboard the platform RV Kilo Moana

description: Original (unprocessed) data collected during a Field_expedition
DOI: 10.6084/m9.figshare.14214218
title: Irish national dataset to create model for dithionite-P and WEP

description: Soil parameters used to create an equation using stepwise regression analysis. The equation from this data was used to create a map that could identify areas in the landscape with large pools of anoxic WEP and dithionite-P.
DOI: 10.6084/m9.figshare.14214218.v1
title: Irish national dataset to create model for dithionite-P and WEP

description: Soil parameters used to create an equation using stepwise regression analysis. The equation from this data was used to create a map that could identify areas in the landscape with large pools of anoxic WEP and dithionite-P.
DOI: 10.6084/m9.figshare.14214239
title: Anoxic and oxic water extractable phosphorus

description: Data indicating the difference between soil WEP when extracted in anoxic and oxic conditions. Soil parameters were used to create an equation using stepwise regression analysis. The equation from this data was used to create a map that could identify areas in the landscape with large pools of anoxic WEP and dithionite-P.
DOI: 10.6084/m9.figshare.14214239.v1
title: Anoxic and oxic water extractable phosphorus

description: Data indicating the difference between soil WEP when extracted in anoxic and oxic conditions. Soil parameters were used to create an equation using stepwise regression analysis. The equation from this data was used to create a map that could identify areas in the landscape with large pools of anoxic WEP and dithionite-P.
DOI: 10.6084/m9.figshare.14214233
title: BATS Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214245
title: CARIACO Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214248
title: FOT Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214254
title: HOT Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214254.v1
title: HOT Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214248.v1
title: FOT Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214245.v1
title: CARIACO Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214233.v1
title: BATS Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214257
title: KEO Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.6084/m9.figshare.14214257.v1
title: KEO Data

description: Data Sets, code and figures used in the analyses associated with manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series"
DOI: 10.25919/xfc9-1x76
title: MongeNet - Training data

description: Triangle Dataset to train MongeNet model.
DOI: 10.6084/m9.figshare.14214251.v1
title: Additional file: Assessment of organizational policies in a retail store based on a simulation model.

description: Additional file. Input data. Article: Assessment of organizational policies in a retail store based on a simulation model.
DOI: 10.6084/m9.figshare.14214251
title: Additional file: Assessment of organizational policies in a retail store based on a simulation model.

description: Additional file. Input data. Article: Assessment of organizational policies in a retail store based on a simulation model.
DOI: 10.6084/m9.figshare.14214251.v2
title: Additional file: Assessment of organizational policies in a retail store based on a simulation model.

description: Additional file. Input data. Article: Assessment of organizational policies in a retail store based on a simulation model.
DOI: 10.6084/m9.figshare.14214272
title: MUNIDA Data

description: Data, code and figures from analyses part of manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series".
DOI: 10.6084/m9.figshare.14214278
title: PAPA Data

description: Data, code and figures as apart of analyses used in manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series".
DOI: 10.6084/m9.figshare.13166612
title: Malawi merg BL MD

description: A longitudinal research design was applied with a survey at baseline and a follow-up survey administered to the same households after intervention delivery. The data collection took place in April, May, and June 2016 and 2017. The quantitative follow-up survey interviewed 638 households.
DOI: 10.6084/m9.figshare.13166612.v2
title: Malawi merg BL MD

description: A longitudinal research design was applied with a survey at baseline and a follow-up survey administered to the same households after intervention delivery. The data collection took place in April, May, and June 2016 and 2017. The quantitative follow-up survey interviewed 638 households.
DOI: 10.6084/m9.figshare.13166612.v1
title: Malawi merg BL MD

description: A longitudinal research design was applied with a survey at baseline and a follow-up survey administered to the same households after intervention delivery. The data collection took place in April, May, and June 2016 and 2017. The quantitative follow-up survey interviewed 638 households.
DOI: 10.17632/n47cnzgw66.1
title: Data for: Late Paleozoic Gondwanide deformation in the central Andes: Insights from RSCM thermometry and thermal modeling, southern Bolivia

description: Supplementary text and data set. Text provides a detailed explanation of methods and thermal model parameters. Supplementary data set provides individual spot data for each RSCM analysis.
DOI: 10.17632/knchyfjhpm.1
title: Data for: New insights into the High Agri Valley deep structure revealed by magnetotelluric imaging and seismic tomography (southern Apennine, Italy)
DOI: 10.6084/m9.figshare.14214281
title: Analyses and Figures

description: Data, code and figures used in analyses for manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series".
DOI: 10.6084/m9.figshare.3761562.v1805
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: 10.5281/zenodo.4603700
title: Global reconstruction of historical ocean heat storage and transport

description: Reconstruction of 1870-2018 ocean heat content (OHC_GF_1870_2018.nc) and thermosteric sea level (ThSL_GF_1870_2018.nc) using Green's functions. The method is described in Zanna et al., PNAS, 2019 This dataset is an update from the original published version with changes described here.
DOI: 10.5281/zenodo.4603699
title: Global reconstruction of historical ocean heat storage and transport

description: Reconstruction of 1870-2018 ocean heat content (OHC_GF_1870_2018.nc) and thermosteric sea level (ThSL_GF_1870_2018.nc) using Green's functions. The method is described in Zanna et al., PNAS, 2019 This dataset is an update from the original published version with changes described here.
DOI: 10.34740/kaggle/dsv/2023564
title: US counties COVID 19 dataset
DOI: 10.6084/m9.figshare.14214293
title: Lobbying in EU

description: Lobbying data from LobbyFacts.EU
DOI: 10.6084/m9.figshare.14214293.v1
title: Lobbying in EU

description: Lobbying data from LobbyFacts.EU
DOI: 10.6084/m9.figshare.14214281.v1
title: Analyses and Figures

description: Data, code and figures used in analyses for manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series".
DOI: 10.6084/m9.figshare.14214278.v1
title: PAPA Data

description: Data, code and figures as apart of analyses used in manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series".
DOI: 10.6084/m9.figshare.14214272.v1
title: MUNIDA Data

description: Data, code and figures from analyses part of manuscript titled "A Comparative Assessment of Gap-filling Techniques for Ocean Carbon Time Series".
DOI: 10.25592/uhhfdm.8960
title: Krisenbewältigung in der ersten Welle der Coronavirus-Pandemie am Beispiel von Einrichtungen der Migrationsberatung in Hamburg

description: Interviewtranskripte und Analysedaten für die Masterarbeit "Krisenbewältigung in der ersten Welle der Coronavirus-Pandemie am Beispiel von Einrichtungen der Migrationsberatung in Hamburg". Readme: Die Aufnahmen wurden direkt nach der Einholung der Einwilligung gestartet und mit dem Ende des Telefonats beendet. In einem Fall des Präsenzinterviews wurde die Aufnahme nach dem Ende des Interviews beendet. Die Aufnahmen wurden vollständig transkribiert, zunächst mithilfe vom VR-Tool "TRINT", und anschließend manuell im MAXQDA Pro 2020. Die Interviews fanden per Telefon auf Deutsch statt. In den Transkriptionen wird überwiegend Hochdeutsch verwendet, die dialektale Besonderheiten im Satzbau oder Aussprache wurden nicht berücksichtigt. An vielen Stellen musste für eine bessere Lesbarkeit die Wortfolge geändert werden. Diese Stellen sind in PDF-Versionen blau gekennzeichnet. Ebenso für die bessere Lesbarkeit mussten wenige Wörter verändert oder hinzugefügt werden. Diese sind grün gekennzeichnet. Andere Farben stellen semantische Codes dar: gelb – technische/Online-Ausstattung rot – Sprache lila – Existenzsicherung türkis – Zeit (Veränderungen im Zeitplan, Arbeitszeiten etc.) In den Transkriptionen wurden außerdem folgende Kennzeichnungen verwendet: ( ) – unverständlich (vermutlich) – vermuteter Wortlaut ((lacht)) – nichtsprachliche Ereignisse [anonymisiert] – anonymisierte Namen oder Bezeichnungen LAUT – betonte Wörter weil- – abgebrochene Sequenzen (wurde mit aufgeschrieben, wenn ggfs. wichtig für die Auswertung) (.) – kurze Pause (Pause 3 Sek.) – Pause oder Unterbrechung mit Angabe der Dauer Das zip-Archiv beinhaltet: Tabelle mit Cross-Analyse von Interviews (manuell erstellt); Ergebnisse maschineller Analyse – Ergebnisse der Wortsuche, visualisierte Portraits sowie alle codierte Segmente; Transkripte der Interviews in HTML (nur Text) und PDF (mit farblicher Codierung). Originalaufnahmen wurden am 11. März 2021 im Datenrepositorium der Universität Hamburg gespeichert und können dort jederzeit auf Anfrage abgerufen werden.
DOI: 10.6084/m9.figshare.14151563.v1
title: 210113-Mapping DB Digital Transformation Media literacy.xlsx

description: The data set includes the systematic mapping literature review of digital transformation and media literacy in higher education institutions from 2015 to 2020.The searched was carried out in Scopus and Web of Science data bases.This research is part of a doctoral thesis on Universities' Digital Transformation.