cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.5789351
title: LoGov Switzerland Interview Report n°3

description: The interviews have been realised in the framework of the H2020-MSCA-RISE-2018 project “LoGov - Local Government and the Changing Urban-Rural Interplay” as part of the implementation phase of the project. The interviews have been conducted with experts in the field of public administration, public law and political science, both researchers and practitioners, with the aim of widening the scope of the Country Report on Switzerland. To access the full transcription of this interview, the other interview reports on Switzerland, and to receive more information about the project, please visit: https://www.logov-rise.eu/. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 823961.
DOI: 10.5281/zenodo.7335646
title: Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other)

description: <strong>Description</strong> 1018 images and 1018 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 4 classes are 0=water, 1=whitewater, 2=sediment, 3=other These images and labels have been made using the Doodleverse software package, Doodler*. These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**. Some (473) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 4 classes. Imagery comes from the following 10 sand beach sites: Duck, NC, Hatteras NC, USA Santa Cruz CA, USA Galveston TX, USA Truc Vert,France Sunset State Beach CA, USA Torrey Pines CA, USA Narrabeen, NSW, Australia Elwha WA, USA Ventura region, CA, USA Klamath region, CA USA Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, and Blue bands only <strong>File descriptions</strong> classes.txt, a file containing the class names images.zip, a zipped folder containing the 3-band images of varying sizes and extents labels.zip, a zipped folder containing the 1-band label images overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (blue=0=water, red=1=whitewater, yellow=2=sediment, green=3=other) resized_images.zip, RGB images resized to 512x512x3 pixels resized_labels.zip, label images resized to 512x512 pixels <strong>References</strong> *Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler. **Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym ***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information
DOI: 10.25934/pr00007372.8
title: Available IPD datapackage for study 'A Phase III, Open-Label, Randomized Study of Atezolizumab in Combination With Bevacizumab Compared With Sorafenib in Patients With Untreated Locally Advanced or Metastatic Hepatocellular Carcinoma'

description: This study will evaluate the efficacy and safety of atezolizumab in combination with bevacizumab compared with sorafenib in participants with locally advanced or metastatic Hepatocellular Carcinoma (HCC) who have received no prior systemic treatment.
DOI: 10.15139/s3/zl4uf9
title: ZIP Code-Level Temperature Data, Contiguous US, 2000-2017

description: Files: ‘zip.temp.data_[year].rds’, where [year] is between 2000-2017 Data frame with arithmetic (.Mean) and population-weighted (.Wght) averages of mean/max/min temperature, dew point, relative humidity, and apparent temperature for 9,917 ZIP codes located in the urban cores of 120 metropolitan areas in the contiguous United States for 01/01/2000 to 12/31/2017. A data dictionary describing all variables included in the dataset can be found in: 'Data Dictionary.docx'
DOI: 10.6084/m9.figshare.21611079
title: map_0.083_age_bias_corrected_per_area_ndvi

description: map_0.083_age_bias_corrected_per_area_ndvi
DOI: 10.6084/m9.figshare.21611079.v1
title: map_0.083_age_bias_corrected_per_area_ndvi

description: map_0.083_age_bias_corrected_per_area_ndvi
DOI: 10.6084/m9.figshare.21611085
title: map_0.083_age_bias_corrected_per_area_nbr

description: map_0.083_age_bias_corrected_per_area_nbr
DOI: 10.6084/m9.figshare.21611085.v2
title: map_0.083_age_bias_corrected_per_area_nbr

description: map_0.083_age_bias_corrected_per_area_nbr
DOI: 10.6084/m9.figshare.21611085.v1
title: Untitled Item

description: map_0.083_age_bias_corrected_per_area_nbr
DOI: 10.5281/zenodo.7352135
title: Chiral surface superconductivity in half-Heusler semimetals

description: Input and relevant output files for the VASP first-principles calculations on LuPtBi half-Heusler semimetal shown in the manuscript.
DOI: 10.5281/zenodo.7352134
title: Chiral surface superconductivity in half-Heusler semimetals

description: Input and relevant output files for the VASP first-principles calculations on LuPtBi half-Heusler semimetal shown in the manuscript.
DOI: 10.6073/pasta/b444229adfc34dfcf049e3d302036d81
title: North Temperate Lakes LTER: High Frequency Water Temperature Data - Lake Mendota Pier 2006 - 2008

description: Water temperature was measured on the pier at 1 and 2 m water depth at a frequency of 1 minute.
DOI: 10.25402/cer.21610950
title: Systematic review of outcomes and patient heterogeneity in relapsed or refractory diffuse large B-cell lymphoma: supplementary tables

description: Aim: To evaluate trials of systemic therapies in transplant-ineligible or -experienced, relapsed/refractory diffuse large-B cell lymphoma and the impact of patient characteristics on overall response rate (ORR). Patients & methods: Systematically reviewed multiple databases through 22 July 2021. Analyzed variations in patient characteristics and their relationship with ORR across trials. Results: Among 17 included trials, key patient characteristics varied substantially: primary refractory (0–69%), refractory to last line of therapy (LOT) (12–100%), ≥2 prior LOTs (14–100%), ≥3 prior LOTs (0–64%), IPI ≥3 (23–73%), tumor stage III/IV (50–90%) and median age (56–74 years). ORRs varied substantially (25–83%), correlating with these characteristics. Conclusion: Differences in patient characteristics significantly contribute to the variability in ORR across these trials and should be considered when contextualizing efficacy data.
DOI: 10.25402/cer.21610950.v1
title: Systematic review of outcomes and patient heterogeneity in relapsed or refractory diffuse large B-cell lymphoma: supplementary tables

description: Aim: To evaluate trials of systemic therapies in transplant-ineligible or -experienced, relapsed/refractory diffuse large-B cell lymphoma and the impact of patient characteristics on overall response rate (ORR). Patients & methods: Systematically reviewed multiple databases through 22 July 2021. Analyzed variations in patient characteristics and their relationship with ORR across trials. Results: Among 17 included trials, key patient characteristics varied substantially: primary refractory (0–69%), refractory to last line of therapy (LOT) (12–100%), ≥2 prior LOTs (14–100%), ≥3 prior LOTs (0–64%), IPI ≥3 (23–73%), tumor stage III/IV (50–90%) and median age (56–74 years). ORRs varied substantially (25–83%), correlating with these characteristics. Conclusion: Differences in patient characteristics significantly contribute to the variability in ORR across these trials and should be considered when contextualizing efficacy data.
DOI: 10.5517/ccdc.csd.cc13r78c
title: CCDC 1035222: Experimental Crystal Structure Determination

description: Related Article: Davide Lionetti, Sandy Suseno, Emily Y. Tsui, Luo Lu, Troy A. Stich, Kurtis M. Carsch, Robert J. Nielsen, William A. Goddard, R. David Britt, Theodor Agapie|2019|Inorg.Chem.|58|2336|doi:10.1021/acs.inorgchem.8b02701
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.cc2087sb
title: CCDC 1854971: Experimental Crystal Structure Determination

description: Related Article: Jamie Hicks, Akseli Mansikkamäki, Petra Vasko, Jose M. Goicoechea,  Simon Aldridge|2019|Nature Chemistry|11|237|doi:10.1038/s41557-018-0198-1
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.cc2087vd
title: CCDC 1854973: Experimental Crystal Structure Determination

description: Related Article: Jamie Hicks, Akseli Mansikkamäki, Petra Vasko, Jose M. Goicoechea,  Simon Aldridge|2019|Nature Chemistry|11|237|doi:10.1038/s41557-018-0198-1
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.cc2087wf
title: CCDC 1854974: Experimental Crystal Structure Determination

description: Related Article: Jamie Hicks, Akseli Mansikkamäki, Petra Vasko, Jose M. Goicoechea,  Simon Aldridge|2019|Nature Chemistry|11|237|doi:10.1038/s41557-018-0198-1
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.cc2087tc
title: CCDC 1854972: Experimental Crystal Structure Determination

description: Related Article: Jamie Hicks, Akseli Mansikkamäki, Petra Vasko, Jose M. Goicoechea,  Simon Aldridge|2019|Nature Chemistry|11|237|doi:10.1038/s41557-018-0198-1
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.cc20ndcg
title: CCDC 1866645: Experimental Crystal Structure Determination

description: Related Article: Jun-ichi Sasaki, Makoto Kobayashi, Yûsuke Ibe, Hiroshi Nishinob|2020|CSD Communication|||
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.cc20nddh
title: CCDC 1866646: Experimental Crystal Structure Determination

description: Related Article: Jun-ichi Sasaki, Makoto Kobayashi, Yûsuke Ibe, Hiroshi Nishinob|2020|CSD Communication|||
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.cc20ndfj
title: CCDC 1866647: Experimental Crystal Structure Determination

description: Related Article: Jun-ichi Sasaki, Makoto Kobayashi, Yûsuke Ibe, Hiroshi Nishinob|2020|CSD Communication|||
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.cc217rcf
title: CCDC 1884284: Experimental Crystal Structure Determination

description: Related Article: Rajesh Kumar, K. Osahon Ogbeide, Mujeeb-Ur-Rehman, Bodunde Owolabi, Abiodun Falodun, M. Iqbal Choudhary, Sammer Yousuf|2019|Acta Crystallogr.,Sect.E:Cryst.Commun.|75|119|doi:10.1107/S2056989018017498
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.cc21y4kq
title: CCDC 1904843: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Monica Vasiliu, Ian Colliard, R. Lee Ayscue, Sae Young Han, Jeffery A. Bertke, May Nyman, David A. Dixon, Karah E. Knope|2019|Inorg.Chem.|58|10871|doi:10.1021/acs.inorgchem.9b01238
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.cc21y4lr
title: CCDC 1904844: Experimental Crystal Structure Determination

description: Related Article: Jennifer N. Wacker, Monica Vasiliu, Ian Colliard, R. Lee Ayscue, Sae Young Han, Jeffery A. Bertke, May Nyman, David A. Dixon, Karah E. Knope|2019|Inorg.Chem.|58|10871|doi:10.1021/acs.inorgchem.9b01238
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.cc21z3ms
title: CCDC 1905775: Experimental Crystal Structure Determination

description: Related Article: Maurice G. Sorolla, Xiqu Wang, Tatyana Makarenko, Allan J. Jacobson|2019|Inorg.Chem.|58|9935|doi:10.1021/acs.inorgchem.9b01097
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.cc21z3nt
title: CCDC 1905776: Experimental Crystal Structure Determination

description: Related Article: Maurice G. Sorolla, Xiqu Wang, Tatyana Makarenko, Allan J. Jacobson|2019|Inorg.Chem.|58|9935|doi:10.1021/acs.inorgchem.9b01097
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.cc26l5ll
title: CCDC 2043259: Experimental Crystal Structure Determination

description: Related Article: Jörg Wagler, Robert Gericke|2020|CSD Communication|||
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: 10.6084/m9.figshare.19142627
title: Meteorology dataset at subbasions

description: Temperature (T), precipitation (P) and potential evaporation (PET)) at subbasin scales.
DOI: 10.6084/m9.figshare.19158467
title: Code for extracting climate data

description: Code for extracting climate data of each subbasin from the Climatic Research Unit at the University of East Anglia (http://www.cru.uea.ac.uk/) and the Global Precipitation Climatology Centre (https://climatedataguide.ucar.edu/climate-data/gpcc-global-precipitation-climatology-centre).
DOI: 10.6084/m9.figshare.19158467.v1
title: Code for extracting climate data

description: Code for extracting climate data of each subbasin from the Climatic Research Unit at the University of East Anglia (http://www.cru.uea.ac.uk/) and the Global Precipitation Climatology Centre (https://climatedataguide.ucar.edu/climate-data/gpcc-global-precipitation-climatology-centre).
DOI: 10.5281/zenodo.7351726
title: Scaled and Translated Image Recognition (STIR) Source Data

description: While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size \(s \in [17,64]\), each randomly placed in a \(64 \times 64\) pixel image. <strong>Original Source Data</strong> <code>dota/</code> (from DOTA v1.5 Google Drive website) <code>train/</code> <code>DOTA-v1.5_train.zip</code> <strong>not</strong> unzipped <code>part1.zip</code> <strong>not</strong> unzipped <code>part2.zip</code> <strong>not</strong> unzipped <code>part3.zip</code> <strong>not</strong> unzipped <code>val/</code> <code>DOTA-v1.5_val.zip</code> <strong>not</strong> unzipped <code>part1.zip</code> <strong>not</strong> unzipped <code>fontawesome/</code> (from Font Awesome 5.15.3 "Free for Desktop") <code>svgs/</code> unzipped from archive <code>mapillary/</code> (from Mapillary Traffic Sign Dataset) <code>mtsd_v2_fully_annotated</code> unzipped from archive <code>train.0.zip</code> <strong>not</strong> unzipped <code>train.1.zip</code> <strong>not</strong> unzipped <code>train.2.zip</code> <strong>not</strong> unzipped <code>val.zip</code> <strong>not</strong> unzipped <code>mnist/</code> (from Yann LeCun website) <code>t10k-images-idx3-ubyte.gz</code> <code>t10k-labels-idx1-ubyte.gz</code> <code>train-images-idx3-ubyte.gz</code> <code>train-labels-idx1-ubyte.gz</code> <strong>License and Attribution</strong> When using the original source data for your own research, please respect the individual licenses. For attribution in papers, we recommend the following citations which introduce the respective datasets. D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998. C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020. G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.
DOI: 10.5281/zenodo.7351725
title: Scaled and Translated Image Recognition (STIR) Source Data

description: While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size \(s \in [17,64]\), each randomly placed in a \(64 \times 64\) pixel image. <strong>Original Source Data</strong> <code>dota/</code> (from DOTA v1.5 Google Drive website) <code>train/</code> <code>DOTA-v1.5_train.zip</code> <strong>not</strong> unzipped <code>part1.zip</code> <strong>not</strong> unzipped <code>part2.zip</code> <strong>not</strong> unzipped <code>part3.zip</code> <strong>not</strong> unzipped <code>val/</code> <code>DOTA-v1.5_val.zip</code> <strong>not</strong> unzipped <code>part1.zip</code> <strong>not</strong> unzipped <code>fontawesome/</code> (from Font Awesome 5.15.3 "Free for Desktop") <code>svgs/</code> unzipped from archive <code>mapillary/</code> (from Mapillary Traffic Sign Dataset) <code>mtsd_v2_fully_annotated</code> unzipped from archive <code>train.0.zip</code> <strong>not</strong> unzipped <code>train.1.zip</code> <strong>not</strong> unzipped <code>train.2.zip</code> <strong>not</strong> unzipped <code>val.zip</code> <strong>not</strong> unzipped <code>mnist/</code> (from Yann LeCun website) <code>t10k-images-idx3-ubyte.gz</code> <code>t10k-labels-idx1-ubyte.gz</code> <code>train-images-idx3-ubyte.gz</code> <code>train-labels-idx1-ubyte.gz</code> <strong>License and Attribution</strong> When using the original source data for your own research, please respect the individual licenses. For attribution in papers, we recommend the following citations which introduce the respective datasets. D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998. C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020. G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.