cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2d3b0w
title: CCDC 2207727: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Shelyganov, Mehdi Elsayed Moussa, Michael Seidl, Manfred Scheer|2023|Angew.Chem.,Int.Ed.|62|e202215650|doi:10.1002/anie.202215650
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d3b1x
title: CCDC 2207728: Experimental Crystal Structure Determination

description: Related Article: Pavel A. Shelyganov, Mehdi Elsayed Moussa, Michael Seidl, Manfred Scheer|2023|Angew.Chem.,Int.Ed.|62|e202215650|doi:10.1002/anie.202215650
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2d5nz7
title: CCDC 2209989: Experimental Crystal Structure Determination

description: Related Article: Kyoung Chul Park, Preecha Kittikhunnatham, Jaewoong Lim, Grace C. Thaggard, Yuan Liu, Corey R. Martin, Gabrielle A. Leith, Donald J. Toler, An T. Ta, Nancy Birkner, Ingrid Lehman-Andino, Alejandra Hernandez-Jimenez, Gregory Morrison, Jake W. Amoroso, Hans-Conrad zur Loye, Dave P. DiPrete, Mark D. Smith, Kyle S. Brinkman, Simon R. Phillpot, Natalia B. Shustova|2023|Angew.Chem.,Int.Ed.|62|e202216349|doi:10.1002/anie.202216349
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2djqc1
title: CCDC 2220603: Experimental Crystal Structure Determination

description: Related Article: Jianbo Song, Louis Frentzel-Beyme, Roman Pallach, Pascal Kolodzeiski, Athanasios Koutsianos, Wen-Long Xue, Rochus Schmid, Sebastian Henke|2023|J.Am.Chem.Soc.|145|9273|doi:10.1021/jacs.3c01933
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.5235535
title: Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets

description: This dataset accompanies the paper titled <em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br> <br> that appeared in the Transactions on Machine Learning Research<br> <br> https://openreview.net/forum?id=I4IkGmgFJz<br> <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> We make available two datasets. <strong>Raw-Microscopy:</strong> <strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels). <strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075) <strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views) <strong>Raw-Drone:</strong> <strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472. <strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075). <strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views). Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper. The code repository for this project can be found at https://github.com/aiaudit-org/raw2logit
DOI: 10.5281/zenodo.5235536
title: Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets

description: This dataset accompanies the paper titled <em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br> <br> that appeared in the Transactions on Machine Learning Research<br> <br> https://openreview.net/forum?id=I4IkGmgFJz<br> <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> We make available two datasets. <strong>Raw-Microscopy:</strong> <strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels). <strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075) <strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views) <strong>Raw-Drone:</strong> <strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472. <strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075). <strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views). Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper. The code repository for this project can be found at https://github.com/aiaudit-org/raw2logit
DOI: 10.5285/3ff7ae723d3542bab6525207a9092e2b
title: ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 06.2 data collection

description: Soil Moisture data (version 06.2) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets alongside ancillary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, AMSR2, SMOS, GPM and SMAP. The COMBINED product is generated from the Level 2 active and passive instruments. The homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2021-12-31 and the ACTIVE product covering 1991-08-05 to 2021-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Additional documentation and information relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product User Guide. The data set should be cited using all of the following references: 1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019 2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 If using the COMBINED break-adjusted product, the following should also be cited in addition to the above: 3. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., "Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record," in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896.
DOI: 10.5281/zenodo.7889277
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2009

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500 m spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500 m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2009. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2008</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2010</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2009 Spatial Resolution: 500 m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7889276
title: MUSES Fractional Vegetation Coverage (FVC) Monthly Global 500m SIN Grid in 2009

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global FVC product at 500 m spatial resolution and monthly temporal resolution. The MUSES FVC product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from the MUSES LAI product at 500 m resolution and other ancillary information using the complement to unity of the transmittance of light through the entire canopy in the nadir viewing direction (Xiao <em>et al</em>., 2016). The MUSES FVC values are physically consistent with the corresponding MUSES LAI values. The MUSES FVC product is spatially complete and temporally continuous. This dataset is the MUSES FVC product in 2009. <em>Please <strong>click here</strong> to download the MUSES FVC product <strong>in 2008</strong></em>, <em>and <strong>click here</strong> to download the MUSES FVC product <strong>in 2010</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2009 Spatial Resolution: 500 m Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.004 Valid Range: 0 – 250 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et a</em>l. (2016). Estimating the Fractional Vegetation Cover from GLASS Leaf Area Index Product. <em>Remote Sensing</em>, 8, 337. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7891978
title: PalliersTomo9_Heating

description: Sellf Heating curves (heating part) of porous specimen, tomo 9
DOI: 10.5281/zenodo.7891977
title: PalliersTomo9_Heating

description: Sellf Heating curves (heating part) of porous specimen, tomo 9
DOI: 10.5519/qd.qmwqx4uz
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (3576 records)
DOI: 10.34740/kaggle/dsv/5495819
title: Amharic News Corpus
DOI: 10.17037/data.00002130
title: Clinic attendance by sex and HIV status in rural South Africa - Data and Code

description: Research outputs associated with a study to determine the frequency of Primary Healthcare Clinic (PHC) attendance among HIV-negative men in a TB-prevalent setting. The collection is comprised of a tabular data set that contains routine information on clinic attendance in a demographic surveillance site, a STATA do-file to analyse primary clinic visits data, as well as supporting documentation. These files may be used to reproduce the results in Table 1, Table 2 and Table 3 in a paper titled "Can we find the missing men in clinics? Clinic attendance by sex and HIV status in rural South Africa".
DOI: 10.6084/m9.figshare.22664896.v1
title: data from Hulugou peat.xlsx

description: The resluts of elements was scanned by X-Ray Fluorescence (XRF) using the Geotek Multi-Sensor Core Loggers (MSCL) XRF core scanner. The grain size distributions of peat samples were determined by a laser-optical particle size analyzer Malvern Mastersizer-2000 and analyzed by parameterization End-member Modelling Analysis (EMA). We stacked a curve with EM1 and Ti components for the reconstructed dust input.
DOI: 10.48366/r586327
title: Ontology Learning from Text

description: In this ORKG research comparison, a selection of ontology learning methods from text are compared. The original data can be found in Appendix A of the article "Ontology learning methods from text – an extensive knowledge-based approach" by Jarosław Wątróbski. The article is linked as reference to this comparison.
DOI: 10.5281/zenodo.7890450
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2003

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1 km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2003. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2002</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2004</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2003 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.5281/zenodo.7890449
title: MUSES Leaf Area Index (LAI) Monthly Global 1km SIN Grid in 2003

description: The MUltiscale Satellite remotE Sensing (MUSES) product suite includes products with different spatial and temporal resolutions for parameters such as Normalized Difference Vegetation Index (NDVI), Near-Infrared Reflectance of Vegetation (NIRv), Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Fractional Vegetation Coverage (FVC), Gross Primary Production (GPP), Net Primary Production (NPP). For more information about the MUSES products, please refer to this website (https://muses.bnu.edu.cn/). This dataset is the MUSES global LAI product at 1 km spatial resolution and monthly temporal resolution. The MUSES LAI product is provided on a Sinusoidal grid and spans from 2000 to 2019 (continuously updated). It was generated from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product using general regression neural networks (GRNNs) (Xiao <em>et al</em>., 2014; Xiao <em>et al.</em>, 2016). The MUSES LAI product is spatially complete and temporally continuous. This dataset is the MUSES LAI product in 2003. <em>Please <strong>click here</strong> to download the MUSES LAI product <strong>in 2002</strong></em>, <em>and <strong>click here</strong> to download the MUSES LAI product <strong>in 2004</strong></em>. <strong>Dataset Characteristics:</strong> Spatial Coverage: Global Temporal Coverage: 2003 Spatial Resolution: 1 km Temporal Resolution: 1 month Projection: Sinusoidal Data Format: HDF Scale: 0.01 Valid Range: 0 – 1000 <strong>Citation </strong>(Please cite this paper whenever these data are used)<strong>:</strong> Xiao Zhiqiang, <em>et al</em>. (2014). Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 52, 209-223. Xiao Zhiqiang, <em>et al</em>. (2016). Long-time-series global land surface satellite leaf area index product derived from MODIS and AVHRR surface reflectance. <em>IEEE Transactions on Geoscience and Remote Sensing</em>, 54, 5301-5318. Xiao Zhiqiang, Jinling Song, Hua Yang, Rui Sun and Juan Li. (2022). A 250 m resolution global leaf area index product derived from MODIS surface reflectance data. <em>International Journal of Remote Sensing</em>, 43(4), 1199-1225. Xiao Zhiqiang, <em>et al</em>. (2017). Evaluation of four long time-series global leaf area index products. <em>Agricultural and Forest Meteorology</em>, 246, 218-230. If you have any questions, please contact Prof. Zhiqiang Xiao (zhqxiao@bnu.edu.cn).
DOI: 10.48366/r586326
title: Knowledge extraction and graph based representation

description: Highlights proposed models for semantic knowledge extraction using NLP techniques and information representation
DOI: 10.5281/zenodo.7851915
title: Dataset and Code for "How Trump Triumphed: Multi-Candidate Primaries with Buffoons"

description: This is code and data for the paper “How Trump Triumphed: Multi-Candidate Primaries with Buffoons.” by Micael Castanheira, Steffen Huck, Johannes Leutgeb, and Andrew Schotter, forthcoming in the European Economic Review.
description: Forthcoming int he European Economic Review. Also see the latest working paper version Castanheira, Micael, Steffen Huck, Johannes Leutgeb, and Andrew Schotter. "How Trump Triumphed: Multi-Candidate Primaries with Buffoons." Working Paper. Discussion Papers. Berlin: WZB, September 2022. https://bibliothek.wzb.eu/pdf/2022/ii20-307r.pdf. and the github repository for the experimental code. https://github.com/jleutgeb/buffoon
DOI: 10.5281/zenodo.7851914
title: Dataset and Code for "How Trump Triumphed: Multi-Candidate Primaries with Buffoons"

description: This is code and data for the paper “How Trump Triumphed: Multi-Candidate Primaries with Buffoons.” by Micael Castanheira, Steffen Huck, Johannes Leutgeb, and Andrew Schotter, forthcoming in the European Economic Review.
description: Forthcoming int he European Economic Review. Also see the latest working paper version Castanheira, Micael, Steffen Huck, Johannes Leutgeb, and Andrew Schotter. "How Trump Triumphed: Multi-Candidate Primaries with Buffoons." Working Paper. Discussion Papers. Berlin: WZB, September 2022. https://bibliothek.wzb.eu/pdf/2022/ii20-307r.pdf. and the github repository for the experimental code. https://github.com/jleutgeb/buffoon
DOI: 10.5519/qd.dn2qobto
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (2866 records)
DOI: 10.48366/r586328
title: Ontology Learning from Text

description: In this ORKG research comparison, a selection of ontology learning methods from text are compared. The original data can be found in Appendix A of the article "Ontology learning methods from text – an extensive knowledge-based approach" by Jarosław Wątróbski. The article is linked as reference to this comparison.