cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.6023185
title: Datasets generated and/or analysed for paper entitled: "Oxysterol 7α,25OHC synthesising enzymes, CH25H and CYP7B1, are upregulated in the blood-brain barrier during inflammation"

description: Images collected and datasets generated and/or analyzed for a paper entitled: "Oxysterol 7α,25OHC synthesising enzymes, CH25H and CYP7B1, are upregulated in the blood-brain barrier during inflammation"
DOI: 10.5281/zenodo.6023184
title: Datasets generated and/or analysed for paper entitled: "Oxysterol 7α,25OHC synthesising enzymes, CH25H and CYP7B1, are upregulated in the blood-brain barrier during inflammation"

description: Images collected and datasets generated and/or analyzed for a paper entitled: "Oxysterol 7α,25OHC synthesising enzymes, CH25H and CYP7B1, are upregulated in the blood-brain barrier during inflammation"
DOI: 10.5281/zenodo.6008097
title: DeepFish dataset

description: Image bank of fish trays collected in the Campello fish market by artisanal fishing belonging to the DeepFish project. JSON files are prepared to be used with the program "Django Labeller", but it can be converted to any format, f.e. "COCO". Indeed, the file "data_fish.json" contains the previous information with the images filtered in this COCO format. Each JSON file is composed by an object containing the name of the image and the labels appearing in it. Inside each label we will have: Type of label. It can be a size (total, diameter of the eye...), tray or fish specie. Class of the label. It means the concrete specie, measurement or tray depending on the type of label. Semantic segmentation represented by one or multiple regions in case of occlusions. Represented by an array of coordinates in the image (x and y). Object_id: Identifier of the label, unique in the entire dataset. Father_object_id: In case this is not the main object (The label with the segmentation of the specie). It will point to the id of that main specie which it belongs. It means, if this is the total size, it will point to the fish sized like that.
description: This project is developed with the collaboration of the Biodiversity Foundation of the Ministry for Ecological Transition and the Demographic Challenge of Spain, through the Pleamar Program, co-financed by the FEMP.
DOI: 10.5281/zenodo.5977869
title: DeepFish dataset

description: Image bank of fish trays collected in the Campello fish market by artisanal fishing belonging to the DeepFish project. JSON files are prepared to be used with the program "Django Labeller", but it can be converted to any format, f.e. "COCO". Indeed, the file "data_fish.json" contains the previous information with the images filtered in this COCO format. Each JSON file is composed by an object containing the name of the image and the labels appearing in it. Inside each label we will have: Type of label. It can be a size (total, diameter of the eye...), tray or fish specie. Class of the label. It means the concrete specie, measurement or tray depending on the type of label. Semantic segmentation represented by one or multiple regions in case of occlusions. Represented by an array of coordinates in the image (x and y). Object_id: Identifier of the label, unique in the entire dataset. Father_object_id: In case this is not the main object (The label with the segmentation of the specie). It will point to the id of that main specie which it belongs. It means, if this is the total size, it will point to the fish sized like that.
description: This project is developed with the collaboration of the Biodiversity Foundation of the Ministry for Ecological Transition and the Demographic Challenge of Spain, through the Pleamar Program, co-financed by the FEMP.
DOI: 10.48364/isimip.263794
title: ISIMIP3a lakes input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>This dataset contains the lake area fraction (percentage of lakes in a grid cell) and the lake mask (total lake surface area in a grid cell, average surface area of lakes in grid cell) for the ISIMIP3a simulation round. It accounts for growing lake area fraction due to reservoir construction.
description: The data are derived from the HydroLAKES v1.0 (Messager et al., 2016) and GRanD v1.3 (Lehner et al., 2011) databases and rasterized using the polygon_to_cellareafraction tool (Vanderkelen 2021, available on GitHub), and mapped to 0.5 degrees resolution. Time steps represent increasing reservoirs in their construction year, as given by GRanD.
DOI: 10.48364/isimip.263794.1
title: ISIMIP3a lakes input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>This dataset contains the lake area fraction (percentage of lakes in a grid cell) and the lake mask (total lake surface area in a grid cell, average surface area of lakes in grid cell) for the ISIMIP3a simulation round. It accounts for growing lake area fraction due to reservoir construction.<br>Version 1.1 of this dataset adds the 1901soc scenario with fixed year-1901 direct human influences.
description: The data are derived from the HydroLAKES v1.0 (Messager et al., 2016) and GRanD v1.3 (Lehner et al., 2011) databases and rasterized using the polygon_to_cellareafraction tool (Vanderkelen 2021, available on GitHub), and mapped to 0.5 degrees resolution. Time steps represent increasing reservoirs in their construction year, as given by GRanD.
DOI: 10.48364/isimip.571261
title: ISIMIP3a landuse input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>This data set provides annual maps of various land use categories. In particular it gives annual maps of both rainfed and irrigated areas of total croplands, of 5 crop categories (C3 annual, C3 perennial, C4 annual, C4 perennial and C3 nitrogen fixing crops) and of 15 crop types/categories (C3 annual disaggregated into: rapeseed, rice, temperate cereals, temperate roots, tropical roots, sunflower, others C3 annual; C3 perennial: (no further disaggregation); C3 nitrogen-fixing disaggregated into: groundnut, pulses, soybean, others C3 nitrogen-fixing; C4 annual disaggregated into: maize, tropical cereals; C4 perennial: sugarcane). Furthermore, maps of pastures, managed rangelands and urban areas are provided.<br>Data is available for the time period: historical (1860-2018).
description: The land use data here originates from the historic LUH2 data set (Hurtt et al, 2020, see also https://luh.umd.edu), which in turn is based on HYDE 3.2 (Klein-Goldewijk, 2016), and the Monfreda data set (Monfreda et al, 2008). All variables except for the 15 crops variables were obtained by interpolation from LUH2. The 15 crop variables distribute the coarser 5 crop types according to the Monfreda data set, which gives spatial distribution of 175 different crops in the year 2000, thereby obtaining the 15 crop types maps.
DOI: 10.48364/isimip.759077
title: ISIMIP3a N-deposition input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>This dataset contains gridded NHx and NOy deposition simulated by NCAR Chemistry-Climate Model Initiative (CCMI) during 1860-2014 at monthly time step.
description: Nitrogen deposition data was interpolated to 0.5° by 0.5° by the nearest grid. Data from 2015 to 2018 are taken from 2014 as it is assumed to be same (Tian et al. 2018).
DOI: 10.48364/isimip.311496
title: ISIMIP3a N-fertilizer input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>We provide the nitrogen fertilization rate per our five canonical crop types, i.e. for both annual and perennial C3 and C4 crops, as well as for nitrogen fixing crops. In particular, this describes the application of industrial, mineral nitrogen. The data are provided on an annual times step on the land surface of a global 0.5° × 0.5° grid. The units are kilograms per year and per hectare.<br>Data is available for the time period: historical (1860-2018).
description: The fertilization rates are based on the harmonized land use dataset by Hurtt, G. et al. (2020, see also https://luh.umd.edu). They used different sources, please read their manuscript for of further information.
DOI: 10.48364/isimip.482888
title: ISIMIP3a wood harvesting input data

description: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a consistent set of climate impact data across sectors and scales. It also provides a unique opportunity for considering interactions between climate change impacts across sectors through consistent scenarios.<br>The ISIMIP3a part of the third simulation round is dedicated to i) impact model evaluation and improvement and ii) detection and attribution of observed impacts according to the framework of IPCC AR5 Working Group II Chapter 18. To this end all simulations are driven by observed socio-economic information combined with either observed (factual) climate data or a detrended (counterfactual) version of the observed climate allowing for the generation of a “no climate change” baseline.<br>This dataset presents historical annual country-level wood harvesting data, covering the period 1850-2017. It is an adaptation of the gridded wood harvesting data provided by the LUH2 v2h Harmonization Data Set (Hurtt et al., 2011), to allow its use as input for the ISIMIP biome model simulations. The gridded LUH2 v2h data set reconstructs forest areas differently than other forest reconstructions used by the ISIMIP vegetation models. Providing the data as annual aggregated values per country, and not as a gridded dataset, allows ISIMIP modellers to use the same dataset but their own model specific rules to include harvesting.
description: The data is based on the LUH2 v2h Harmonization Data Set (see Hurtt et al. 2011; see also https://luh.umd.edu). Interpolated to a 0.5° grid using first-order conservative remapping and calculated over a fractional country mask (https://gitlab.pik-potsdam.de/isipedia/countrymasks/-/blob/master/) derived from ASAP-GAUL (https://data.europa.eu/euodp/data/dataset/jrc-10112-10004).
DOI: 10.34740/kaggle/dsv/3161729
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.5884428
title: Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells data

description: Animal models have provided many insights into ocular development and disease, but they remain suboptimal for understanding human oculogenesis. Eye development requires spatiotemporal gene expression patterns and disease phenotypes can differ significantly between humans and animal models, with patient-associated mutations causing embryonic lethality reported in some animal models. The emergence of human induced pluripotent stem cell (hiPSC) technology has provided a new resource for dissecting the complex nature of early eye morphogenesis through the generation of three-dimensional (3D) cellular models. By using patient-specific hiPSCs to generate <em>in vitro </em>optic vesicle-like models, we can enhance the understanding of early developmental eye disorders and provide a pre-clinical platform for disease modelling and therapeutics testing. A major challenge of <em>in vitro </em>optic vesicle generation is the low efficiency of differentiation in 3D cultures. To address this, we adapted a previously published protocol of retinal organoid differentiation to improve embryoid body formation using a microwell plate. Established morphology, upregulated transcript levels of known early eye-field transcription factors and protein expression of standard retinal progenitor markers confirmed the optic vesicle/presumptive optic cup identity of <em>in vitro </em>models between day 20 and 50 of culture. This adapted protocol is relevant to researchers seeking a physiologically relevant model of early human ocular development and disease with a view to replacing animal models.
DOI: 10.5517/cc14hdvw
title: CCDC 1057498: Experimental Crystal Structure Determination

description: Related Article: Tadeusz M. Muziol, Joanna Wisniewska|2015|CSD Communication|||
description: Related Article: Joanna Wiśniewska, Marzena Fandzloch, Tadeusz Muzioł, Iwona Łakomska|2019|Inorg.Chem.Commun.|109|107567|doi:10.1016/j.inoche.2019.107567
DOI: 10.5517/ccdc.csd.cc20kr7l
title: CCDC 1864099: Experimental Crystal Structure Determination

description: Related Article: Meng-Ya Zhu|2018|CSD Communication|||
description: Related Article: Meng-Ya Zhu, Le-Xi Zhang, Jing Yin, Jing-Jing Chen, Li-Jian Bie|2019|Inorg.Chem.Commun.|109|107562|doi:10.1016/j.inoche.2019.107562
DOI: 10.5517/ccdc.csd.cc20xxtp
title: CCDC 1874843: Experimental Crystal Structure Determination

description: Related Article: Mari E. Slater-Parry, James P. Durrant, Joshua M. Howells, Mateusz B. Pitak, Peter N. Horton, Wim T. Klooster, Simon J. Coles, Helen M. O'Connor, Euan K. Brechin, Anne-Laure Barra, Leigh F. Jones|2019|Dalton Trans.|48|1477|doi:10.1039/C8DT04229B
DOI: 10.5517/ccdc.csd.cc20xxwr
title: CCDC 1874845: Experimental Crystal Structure Determination

description: Related Article: Mari E. Slater-Parry, James P. Durrant, Joshua M. Howells, Mateusz B. Pitak, Peter N. Horton, Wim T. Klooster, Simon J. Coles, Helen M. O'Connor, Euan K. Brechin, Anne-Laure Barra, Leigh F. Jones|2019|Dalton Trans.|48|1477|doi:10.1039/C8DT04229B
DOI: 10.5517/ccdc.csd.cc20xxxs
title: CCDC 1874846: Experimental Crystal Structure Determination

description: Related Article: Mari E. Slater-Parry, James P. Durrant, Joshua M. Howells, Mateusz B. Pitak, Peter N. Horton, Wim T. Klooster, Simon J. Coles, Helen M. O'Connor, Euan K. Brechin, Anne-Laure Barra, Leigh F. Jones|2019|Dalton Trans.|48|1477|doi:10.1039/C8DT04229B
DOI: 10.5517/ccdc.csd.cc20xxvq
title: CCDC 1874844: Experimental Crystal Structure Determination

description: Related Article: Mari E. Slater-Parry, James P. Durrant, Joshua M. Howells, Mateusz B. Pitak, Peter N. Horton, Wim T. Klooster, Simon J. Coles, Helen M. O'Connor, Euan K. Brechin, Anne-Laure Barra, Leigh F. Jones|2019|Dalton Trans.|48|1477|doi:10.1039/C8DT04229B
DOI: 10.5517/ccdc.csd.cc232y6c
title: CCDC 1939242: Experimental Crystal Structure Determination

description: Related Article: Liaw Chia-Ching|2021|CSD Communication|||
DOI: 10.6084/m9.figshare.19145771
title: Dataset of Information Behavior Research

description: The dataset encompasses 2 files, responses from participants, and PLS analysis using Warp PLS.
DOI: 10.6084/m9.figshare.19145771.v1
title: Dataset of Information Behavior Research

description: The dataset encompasses 2 files, responses from participants, and PLS analysis using Warp PLS.
DOI: 10.5281/zenodo.5553796
title: Annotated timeseries from yeast cell lifespans - DetecDiv (id03)

description: This dataset has been generated by manual annotation from yeast cells lifespans using the DetecDiv software (see below) for detecting cell-cycle slowdown. It has 1 file containing : Input data: 200 timeseries of classes "1. unbudded", "2. small", "3. large", "4. dead", "5. empty", "6. clog" which are outputs from the doi.org/10.5281/zenodo.5553862 network Groundtruth: 200 timeseries of classes "1. no cell-cycle slowdown", "2. cell-cycle slowdown", which have been annotated manually. It is related to the trained network doi.org/10.5281/zenodo.5553829 from the software DetecDiv: github.com/gcharvin/DetecDiv biorxiv.org/content/10.1101/2021.10.05.463175v1 <strong>Data type</strong>: Vector timeseries (.mat) (250xF with F the number of frames, between 700 and 1000). <strong>File format</strong>: .mat <strong>Author(s)</strong>: Théo, ASPERT <strong>Contact email</strong>: theo.aspert@gmail.com <strong>Affiliation</strong>: IGBMC, Université de Strasbourg <strong>Funding bodies</strong>: This work was supported by the Agence Nationale pour la Recherche, the grant ANR-10-LABX-0030-INRT, a French State fund managed by the Agence Nationale de la Recherche under the frame program Investissements d'Avenir ANR-10-IDEX-0002-02.
description: {"references": ["DetecDiv, a deep-learning platform for automated cell division tracking and replicative lifespan analysis Theo Aspert, Didier Hentsch, Gilles Charvin bioRxiv 2021.10.05.463175; doi: https://doi.org/10.1101/2021.10.05.463175", "ASPERT Th\u00e9o. (2021). Trained network for cell-cycle slowdown detection - DetecDiv (id03) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.5553829"]}
DOI: 10.6084/m9.figshare.19145777
title: Raw data -PDB

description: This study screened the genetic variations of PDB patients in Chinese population.WES, BioinformaticsThe present study was permitted by Ethics Committee and informed consents were obtained from all patients.<br>
DOI: 10.5281/zenodo.6024726
title: Data set for "Token-Level Multilingual Epidemic Dataset for Event Extraction"

description: This is the data for the TPDL 2021 paper "Token-Level Multilingual Epidemic Dataset for Event Extraction". If you use this resource, please cite the paper: <pre><code>@inproceedings{mutuvi2021dataset, title = "Token-level Multilingual Epidemic Dataset for Event Extraction", author = {Mutuvi, Stephen and Boros, Emanuela and Doucet, Antoine, and Lejeune, Gaël and Jatowt, Adam and Odeo, Moses}, booktitle = "Proceedings of the 25th International Conference on Theory and Practice of Digital Libraries, September 13–17, 2021, TPDL 2021", year = "2021", location = "Online" }</code></pre> This work has been supported by the European Union Horizon 2020 research and innovation programme under grants 825153 (Embeddia) and 770299 (NewsEye).
DOI: 10.5281/zenodo.6024725
title: Data set for "Token-Level Multilingual Epidemic Dataset for Event Extraction"

description: This is the data for the TPDL 2021 paper "Token-Level Multilingual Epidemic Dataset for Event Extraction". If you use this resource, please cite the paper: <pre><code>@inproceedings{mutuvi2021dataset, title = "Token-level Multilingual Epidemic Dataset for Event Extraction", author = {Mutuvi, Stephen and Boros, Emanuela and Doucet, Antoine, and Lejeune, Gaël and Jatowt, Adam and Odeo, Moses}, booktitle = "Proceedings of the 25th International Conference on Theory and Practice of Digital Libraries, September 13–17, 2021, TPDL 2021", year = "2021", location = "Online" }</code></pre> This work has been supported by the European Union Horizon 2020 research and innovation programme under grants 825153 (Embeddia) and 770299 (NewsEye).
DOI: 10.5281/zenodo.5918637
title: Snow Spotter Canopy-Snow Interception Dataset #1

description: This dataset was used in a manuscript titled "<em>Evaluating multiple canopy-snow unloading parameterizations in SUMMA with time-lapse photography characterized by citizen scientists</em>" submitted to AGU WRR by the corresponding first-author, Cassie A. Lumbrazo (lumbraca@uw.edu). <br> An excerpt from the manuscript is provided below. Please see the published manuscript for the full dataset summary and the dataset ReadMe file for information on the dataset structure. Time-lapse images from high-resolution digital cameras were collected from the PhenoCam Network (Milliman et al., 2018), the 2017 NASA SnowEx Field Campaign (Currier et al., 2019; Kim et al., 2017), and the Olympic Mountain Experiment (OLYMPEX) ground validation campaign (Currier et al., 2017; Houze et al., 2017; Lundquist et al., 2018). These images were then uploaded to a citizen science platform called Zooniverse (<em>Zooniverse.org</em>) and classified by thousands of volunteers as part of the <em>Snow Spotter</em> project. The goal of Snow Spotter was to mobilize citizen scientists to collect information about canopy interception. A total of 6,700 volunteer citizen scientists responded to questions about 13,600 images from sites across the United States to create this first canopy-snow interception Snow Spotter dataset.