cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.3959126
title: Datasets for the study "Hydro-economic analysis of infrastructure investment options within the Water-Energy-Food nexus in the Niger River basin"

description: This is the dataset used in the study "Hydro-economic analysis of infrastructure investment options within the Water-Energy-Food nexus in the Niger River basin". This study has been conducted within the Department of Environmental Engineering at the Technical University of Denmark as a Master Thesis project. The dataset describes the water-energy-food nexus of the Niger River basin used as input to the open source single objective hydro-economic optimization model WHAT-IF. Note that the Energy Module is partly filled, but has not been used in the study (except the hydropower sheet). More information on data organization here. The model is documented in this publication. The code is available on Github.
description: {"references": ["International Food Policy Research Institute, \"Spatially-Disaggregated Crop Production Statistics Data in Africa South of the Saharan for 2017\" (2020). https://doi.org/10.7910/DVN/FSSKBW, Harvard Dataverse, V1.", "Ghiggi, G., Humphrey, V., Seneviratne, S. I. and Gudmundsson, L. 2019. \"GRUN: an observation-based global gridded runoff dataset from 1902 to 2014\", Earth Syst. Sci. Data, 11, 1655\u20131674, https://doi.org/10.5194/essd-11-1655-2019.", "Harris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. 2014. \"Updated high-resolution grids of monthly climatic observations - the CRU TS3.10 Dataset\". International Journal of Climatology 34, 623-642 doi:10.1002/joc.3711.", "IRENA, \"WEST AFRICAN POWER POOL: Planning and Prospects for Renewable Energy\" (2013). URL: https://www.irena.org/documentdownloads/publications/wapp.pdf.", "Cervigni, R., Liden, R., Neumann, J.E. and Strzepek, K.M. 2015. \"Enhancing the Climate Resilience of Africa's Infrastructure : The Power and Water Sectors.\" Africa Development Forum;. Washington, DC: World Bank. \u00a9 World Bank. https://openknowledge.worldbank.org/handle/10986/21875 License: CC BY 3.0 IGO.", "UNEP Energy Country Profiles (2017). URL (Mali): https://www.unenvironment.org/resources/report/energy-profile-mali.", "FAO: FAOSTAT, [online] Available from: http://www.fao.org/faostat/en/ (Accessed 24 July 2020), 2020.", "JRC, 'Irrigation and irrigated agriculture potential in the Sahel: The case of the Niger river basin\" (2019). DOI: 10.2760/725906"]}
DOI: 10.5281/zenodo.3959089
title: ZeroCostDL4Mic - DeepSTORM training and example dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - DeepSTORM training and example dataset (see our Wiki for details) <strong>Data type</strong>: Time-series of simulated, randomly distributed single-molecule localization (SMLM) data (Training dataset). Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin (Example dataset). <strong>Microscopy data type</strong>: Simulated SMLM and experimental dSTORM super-resolution microscopy data. The experimental dataset was temporally binned in groups of 4 (BIN4) or 10 (BIN10) consecutive time frames in order to simulate higher density of emitters. <strong>Microscope</strong>: Nikon N-STORM <strong>Cell type</strong>: Glial cell in a culture of rat hippocampal neurons, fixed and stained with phalloidin-Alexa Fluor 647. <strong>File format</strong>: .tif (16-bit or 32-bit) <strong>Image size</strong>: 64x64 px (160 nm / pixel), 20 frames (Training dataset), 256 x 256 px (160 nm / pixel), 14,975 frames (Example dataset) <strong>Author(s)</strong>: Christophe Leterrier<sup>1,2</sup>, Romain F. Laine<sup>3,4,5</sup> <strong>Contact email</strong>: christophe.leterrier@univ-amu.fr, r.laine@ucl.ac.uk <strong>Affiliation(s)</strong>: 1) Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France 2) ORCID: 0000-0002-2957-2032 3) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK 4) The Francis Crick Institute, London, UK 5) ORCID: 0000-0002-2151-4487 <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: C.L. acknowledges CNRS ATIP-AO2016. R.F.L. acknowledges a MRC Skills development fellowship (MR/T027924/1).
DOI: 10.5281/zenodo.3959088
title: ZeroCostDL4Mic - DeepSTORM training and example dataset

description: <strong>Name</strong>: ZeroCostDL4Mic - DeepSTORM training and example dataset (see our Wiki for details) <strong>Data type</strong>: Time-series of simulated, randomly distributed single-molecule localization (SMLM) data (Training dataset). Experimental time-series dSTORM acquisition of Glial cells stained with phalloidin for actin (Example dataset). <strong>Microscopy data type</strong>: Simulated SMLM and experimental dSTORM super-resolution microscopy data. The experimental dataset was temporally binned in groups of 4 (BIN4) or 10 (BIN10) consecutive time frames in order to simulate higher density of emitters. <strong>Microscope</strong>: Nikon N-STORM <strong>Cell type</strong>: Glial cell in a culture of rat hippocampal neurons, fixed and stained with phalloidin-Alexa Fluor 647. <strong>File format</strong>: .tif (16-bit or 32-bit) <strong>Image size</strong>: 64x64 px (160 nm / pixel), 20 frames (Training dataset), 256 x 256 px (160 nm / pixel), 14,975 frames (Example dataset) <strong>Author(s)</strong>: Christophe Leterrier<sup>1,2</sup>, Romain F. Laine<sup>3,4,5</sup> <strong>Contact email</strong>: christophe.leterrier@univ-amu.fr, r.laine@ucl.ac.uk <strong>Affiliation(s)</strong>: 1) Aix Marseille Université, CNRS, INP UMR7051, NeuroCyto, Marseille, France 2) ORCID: 0000-0002-2957-2032 3) MRC-Laboratory for Molecular Cell Biology. University College London, London, UK 4) The Francis Crick Institute, London, UK 5) ORCID: 0000-0002-2151-4487 <strong>Associated publications</strong>: Unpublished <strong>Funding bodies</strong>: C.L. acknowledges CNRS ATIP-AO2016. R.F.L. acknowledges a MRC Skills development fellowship (MR/T027924/1).
DOI: 10.5281/zenodo.3959130
title: RCTs and non-RCT studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and prepreints from MedRxiv, we identified a number of RCTs and non-randomized studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.
DOI: 10.5281/zenodo.3959132
title: Observational studies on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and preprints from MedRxiv, we identified a number of observational studies on preventive measures and treatments for Covid-19 that we have included in our systematic review. This file is updated regularly.
DOI: 10.5281/zenodo.3959133
title: Protocols for RCTs on preventive measures and treatments for Covid-19

description: In the course of our PubMed searches and preprints from MedRxiv, we identified a number of protocols for RCTs on preventive measures and treatments for Covid-19. This file is updated regularly.
DOI: 10.21227/j5n7-yz53
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.5281/zenodo.3959134
title: List of studies on Covid-19 identified in MedRxiv and excluded from our systematic review

description: In the course of our MedRxiv searches we identified and screened the title and abstract of a number of studies on Covid-19 that were finally excluded from our systematic review. This file will be updated regularly.
DOI: 10.21227/1x7k-x985
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.21227/f17c-fj20
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.5281/zenodo.3959136
title: FatNav

description: FatNav 3D GRE with a 2D GRE as a host sequence. Original data and split into host and navigator with modified header. Navigator header updated.
DOI: 10.5281/zenodo.3938927
title: FatNav

description: FatNav 3D GRE with a 2D GRE as a host sequence. Original data and split into host and navigator with modified header. Navigator header updated.
DOI: 10.21227/9p0w-1y78
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.21227/wj9r-kp82
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.21227/68kr-0y45
title: MiniFrance

description: The Dataset
DOI: 10.21227/vy1v-0153
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.21227/b18v-cs21
title: MiniFrance

description: The DatasetWe introduce a novel large-scale dataset for semi-supervised semantic segmentation in Earth Observation, the MiniFrance suite. MiniFrance has several unprecedented properties: it is large-scale, containing over 2000 very high resolution aerial images,; it is varied, covering 16 conurbations in France, with various climates, different landscapes, and urban as well as countryside scenes; and it is challenging, considering land use classes with high-level semantics. Nevertheless, the most distinctive quality of MiniFrance is being the only dataset in the field especially designed for semi-supervised learning: it contains labeled and unlabeled images in its training partition, which reproduces a life-like scenario.
DOI: 10.25592/dgs.corpus-3.0-type-16809
title: HÄUTEN5^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, 3. Release)
title: PEELING-OR-SKINNING5^ (MY DGS – annotated. Public Corpus of German Sign Language, 3rd release)

description: Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Diese DOI identifiziert die Version 3.0 des Datensatzes.
description: A type from the annotation of MYDGS including all its tokens in MYDGS: Das Öffentliche DGS-Korpus besteht aus mehr als 50 Stunden Video aus dem Projekt DGS-Korpus , die zusammen mit Annotationen für Forschungszwecke zur Verfügung gestellt werden. Die Datenerhebung im Projekt fand im Zeitraum 2010-2012 in ganz Deutschland statt. Das öffentliche Korpus besteht aus Gesprächen jeweils zweier Informantinnen oder Informanten in Deutscher Gebärdensprache. Das Korpus umfasst 330 Informantinnen und Informanten in vier verschiedenen Altersgruppen (ab 18 Jahren) aus 13 verschiedenen Regionen. Der Schwerpunkt der Sammlung liegt auf Transkripten von Diskussionen und Berichten über das Leben Gehörloser sowie persönliche Erfahrungen, sie enthält aber auch Beispiele anderer Erhebungsformate wie Nacherzählungen.<br>Transkripte werden im iLex- und ELAN-Format sowie als SRT-Untertiteldateien (zum Import etwa in MaxQDA) zur Verfügung gestellt. Die zugehörigen MP4-Videodaten sind durchgängig im Format 360p50. This DOI identifies the version 3.0 of the dataset.
DOI: 10.25592/dgs.corpus-3.0-type-16811
title: BUNT1^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, 3. Release)
title: COLOURFUL1^ (MY DGS – annotated. Public Corpus of German Sign Language, 3rd release)

description: Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Diese DOI identifiziert die Version 3.0 des Datensatzes.
description: A type from the annotation of MYDGS including all its tokens in MYDGS: Das Öffentliche DGS-Korpus besteht aus mehr als 50 Stunden Video aus dem Projekt DGS-Korpus , die zusammen mit Annotationen für Forschungszwecke zur Verfügung gestellt werden. Die Datenerhebung im Projekt fand im Zeitraum 2010-2012 in ganz Deutschland statt. Das öffentliche Korpus besteht aus Gesprächen jeweils zweier Informantinnen oder Informanten in Deutscher Gebärdensprache. Das Korpus umfasst 330 Informantinnen und Informanten in vier verschiedenen Altersgruppen (ab 18 Jahren) aus 13 verschiedenen Regionen. Der Schwerpunkt der Sammlung liegt auf Transkripten von Diskussionen und Berichten über das Leben Gehörloser sowie persönliche Erfahrungen, sie enthält aber auch Beispiele anderer Erhebungsformate wie Nacherzählungen.<br>Transkripte werden im iLex- und ELAN-Format sowie als SRT-Untertiteldateien (zum Import etwa in MaxQDA) zur Verfügung gestellt. Die zugehörigen MP4-Videodaten sind durchgängig im Format 360p50. This DOI identifies the version 3.0 of the dataset.
DOI: 10.25592/dgs.corpus-3.0-type-16813
title: TROTZDEM1^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, 3. Release)
title: NEVERTHELESS1^ (MY DGS – annotated. Public Corpus of German Sign Language, 3rd release)

description: Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Diese DOI identifiziert die Version 3.0 des Datensatzes.
description: A type from the annotation of MYDGS including all its tokens in MYDGS: Das Öffentliche DGS-Korpus besteht aus mehr als 50 Stunden Video aus dem Projekt DGS-Korpus , die zusammen mit Annotationen für Forschungszwecke zur Verfügung gestellt werden. Die Datenerhebung im Projekt fand im Zeitraum 2010-2012 in ganz Deutschland statt. Das öffentliche Korpus besteht aus Gesprächen jeweils zweier Informantinnen oder Informanten in Deutscher Gebärdensprache. Das Korpus umfasst 330 Informantinnen und Informanten in vier verschiedenen Altersgruppen (ab 18 Jahren) aus 13 verschiedenen Regionen. Der Schwerpunkt der Sammlung liegt auf Transkripten von Diskussionen und Berichten über das Leben Gehörloser sowie persönliche Erfahrungen, sie enthält aber auch Beispiele anderer Erhebungsformate wie Nacherzählungen.<br>Transkripte werden im iLex- und ELAN-Format sowie als SRT-Untertiteldateien (zum Import etwa in MaxQDA) zur Verfügung gestellt. Die zugehörigen MP4-Videodaten sind durchgängig im Format 360p50. This DOI identifies the version 3.0 of the dataset.
DOI: 10.25592/dgs.corpus-3.0-type-16814
title: GLEICH4^ (MEINE DGS – annotiert. Öffentliches Korpus der Deutschen Gebärdensprache, 3. Release)
title: EQUAL4^ (MY DGS – annotated. Public Corpus of German Sign Language, 3rd release)

description: Ein Type aus der Annotation von MEINEDGS mit allen seinen Tokens aus MEINEDGS: The Public DGS Corpus consists of more than 50 hours of video data from the DGS-Korpus project made available together with annotations for research purposes. In this project, data were collected all across Germany in the timeframe 2010-2012. The public corpus shows 330 informants in 4 different age groups (from 18 years on) from 13 different regions. All parts are conversations between two informants in German Sign Language (DGS). The majority of transcripts included cover discussions and reports on Deaf life and personal experiences, although there are examples of other tasks such as story retellings as well.<br>Transcripts are made available in iLex and ELAN format as well as in SRT subtitles format that can be imported in MaxQDA and other analysis tools, together with the mp4 video files in 360p50. Diese DOI identifiziert die Version 3.0 des Datensatzes.
description: A type from the annotation of MYDGS including all its tokens in MYDGS: Das Öffentliche DGS-Korpus besteht aus mehr als 50 Stunden Video aus dem Projekt DGS-Korpus , die zusammen mit Annotationen für Forschungszwecke zur Verfügung gestellt werden. Die Datenerhebung im Projekt fand im Zeitraum 2010-2012 in ganz Deutschland statt. Das öffentliche Korpus besteht aus Gesprächen jeweils zweier Informantinnen oder Informanten in Deutscher Gebärdensprache. Das Korpus umfasst 330 Informantinnen und Informanten in vier verschiedenen Altersgruppen (ab 18 Jahren) aus 13 verschiedenen Regionen. Der Schwerpunkt der Sammlung liegt auf Transkripten von Diskussionen und Berichten über das Leben Gehörloser sowie persönliche Erfahrungen, sie enthält aber auch Beispiele anderer Erhebungsformate wie Nacherzählungen.<br>Transkripte werden im iLex- und ELAN-Format sowie als SRT-Untertiteldateien (zum Import etwa in MaxQDA) zur Verfügung gestellt. Die zugehörigen MP4-Videodaten sind durchgängig im Format 360p50. This DOI identifies the version 3.0 of the dataset.