cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc11xcv5
title: CCDC 980587: Experimental Crystal Structure Determination

description: Related Article: Zhi-Jun Jia,  Hiroshi Takayama,  Yushi Futamura,  Harumi Aono,  Jonathan O. Bauer,  Carsten Strohmann,  Andrey P. Antonchick,  Hiroyuki Osada,  and Herbert Waldmann|2018|J.Org.Chem.|83|7033|doi:10.1021/acs.joc.7b03202
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.cc1q8x1y
title: CCDC 1587480: Experimental Crystal Structure Determination

description: Related Article: Dawid Lichosyt, Sylwia Wasiłek, Paweł Dydio, Janusz Jurczak|2018|Chem.-Eur.J.|24|11683|doi:10.1002/chem.201801460
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.cc1qb67h
title: CCDC 1588726: Experimental Crystal Structure Determination

description: Related Article: S. Sivaraman, A. Agilandeswari, C. Balakrishnan, R.M. Sockalingam, S.P. Meenakshisundaram, R. Markkandan|2018|J.Mol.Struct.|1173|385|doi:10.1016/j.molstruc.2018.06.095
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.cc2086n5
title: CCDC 1854936: Experimental Crystal Structure Determination

description: Related Article: Bin Zhang|2018|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.cc2086q7
title: CCDC 1854938: Experimental Crystal Structure Determination

description: Related Article: Rong-Lu Bi, Shi-Kuang Zhu, Qin-Hua Ling, Guang-Zhao Zhou, Xiang-Yi Liu|2014|Yunnan Huagong|41|4|
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.cczsw1n
title: CCDC 917632: Experimental Crystal Structure Determination

description: Related Article: Chen Chen, Chang He, Ying-Yu Zhu, Xiao-Tong He, Dan-Li Hong, Yang-Hui Luo, Bai-Wang Sun|2018|Polyhedron|153|64|doi:10.1016/j.poly.2018.06.043
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: 10.15139/s3/avuf00
title: Ukraine Strengthening Tuberculosis Control Project Impact Evaluation – Baseline data for the Social Support study, 2011, 2012
DOI: 10.5281/zenodo.1308731
title: Synthetic Images Of Fluorescent Spots And Ground Truth Data

description: Synthetical images of fluorescent spots and ground truth data created with the simcep software.
DOI: 10.5281/zenodo.1308732
title: Synthetic Images Of Fluorescent Spots And Ground Truth Data

description: Synthetical images of fluorescent spots and ground truth data created with the simcep software.
DOI: 10.5517/ccdc.csd.cc1z0jj1
title: CCDC 1817763: Experimental Crystal Structure Determination

description: Related Article: Nan-Nan Xu, Li-Wen Qian, Zhao-Qi Li, Guo-Qing Bian, Qin-Yu Zhu, Jie Dai|2018|Inorg.Chem.|57|9153|doi:10.1021/acs.inorgchem.8b01168
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.cc1z0jk2
title: CCDC 1817764: Experimental Crystal Structure Determination

description: Related Article: Nan-Nan Xu, Li-Wen Qian, Zhao-Qi Li, Guo-Qing Bian, Qin-Yu Zhu, Jie Dai|2018|Inorg.Chem.|57|9153|doi:10.1021/acs.inorgchem.8b01168
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.cc1z1qwm
title: CCDC 1818921: Experimental Crystal Structure Determination

description: Related Article: Aleksandra Ziółkowska, Natalia Szynkiewicz, Aleksandra Wiśniewska, Jerzy Pikies, Łukasz Ponikiewski|2018|Dalton Trans.|47|9733|doi:10.1039/C8DT01833B
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.cc1z1qxn
title: CCDC 1818922: Experimental Crystal Structure Determination

description: Related Article: Aleksandra Ziółkowska, Natalia Szynkiewicz, Aleksandra Wiśniewska, Jerzy Pikies, Łukasz Ponikiewski|2018|Dalton Trans.|47|9733|doi:10.1039/C8DT01833B
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: 10.58064/s168-8a12
title: FB_AD_PAL

description: Paired Associates Learning (PAL) task for 3 Alzheimer's mouse models (3xTG-AD, APP/PS1 and 5xFAD). Male and female mice from the 3 mouse lines and their wild-type controls were evaluated longitudinally (3-6, 11-13 months of age) for learning and memory deficits.
DOI: 10.58064/99nm-cr55
title: FB_AD_PD

description: Pairwise visual discrimination (PD or PVD) task for 3 Alzheimer's mouse models (3xTG-AD, APP/PS1 and 5xFAD). Male and female mice from the 3 mouse lines and their wild-type controls were evaluated longitudinally (3-6, 7-10 and 11-13 months of age) to access discrimination learning and cognitive flexibility.
DOI: 10.5281/zenodo.7582145
title: An Agnostic Benchmark for Optical Remote Sensing Image Super-Resolution

description: In remote sensing, image super-resolution (ISR) is a technique used to create high-resolution (HR) images from low-resolution (R) satellite images, giving a more detailed view of the Earth’s surface. However, with the constant development and introduction of new ISR algorithms, it can be challenging to stay updated on the latest advancements and evaluate their performance objectively. To address this issue, we introduce SRcheck, a Python package that provides an easy-to-use interface for comparing and benchmarking various ISR methods. SRcheck includes a range of datasets that consist of high-resolution and low-resolution image pairs, as well as a set of quantitative metrics for evaluating the performance of SISR algorithms.
description: NAIP HR masked pixel has been changed.
DOI: 10.5281/zenodo.7582175
title: 2D NMR Data

description: 2D NMR Data
DOI: 10.5281/zenodo.7582174
title: 2D NMR Data

description: 2D NMR Data
DOI: 10.58064/b58t-8d46
title: DP_AD_5Choice

description: 5-Choice Serial Reaction Time (5-CSRT) task for 3 Alzheimer's mouse models (3xTG-AD, APP/PS1 and 5xFAD). Male and female mice from the 3 mouse lines and their wild-type controls were evaluated longitudinally (3-6, 7-10 and 11-13 months of age) for attention deficits.
DOI: 10.58064/cb0j-em17
title: DP_AD_PAL

description: Pairwise Associates Learning (PAL) task for 3 Alzheimer's mouse models (3xTG-AD, APP/PS1 and 5xFAD). Male and female mice from the 3 mouse lines and their wild-type controls were evaluated longitudinally (3-6, 11-13 months of age) for learning and memory deficits.
DOI: 10.58064/r7yk-yb13
title: DP_AD_PD

description: Pairwise visual discrimination (PD or PVD) task for 3 Alzheimer's mouse models (3xTG-AD, APP/PS1 and 5xFAD). Male and female mice from the 3 mouse lines and their wild-type controls were evaluated longitudinally (3-6, 7-10 and 11-13 months of age) to access discrimination learning and cognitive flexibility.
DOI: 10.6084/m9.figshare.21973346.v1
title: Food security and Land use changes Household Data.sav

description: Land use changes, spurred by human growth, are still shaping biodiversity worldwide. However, land ownership rights and land use changes have not been sufficiently studied. This study examined land ownership rights and agricultural land usage in Mwatate Sub County, Taita County, Kenya. The study sampled 301 stratified disproportionate and simple random households using a cross sectional design. Data entry and analysis used SPSS 23.0. The Neo-Malthusian hypothesis is utilized to analyze the study results, which demonstrate higher population growth and increased anthropogenic forces on agricultural land. This was due to unsustainable land use practices such deforestation, tree logging, high fuelwood use, clearing bushes for human settlement and cultivation, traditional farming methods, and reliance on agriculture for livelihood. A 95%-confidence Chi-square investigation found a strong link between agricultural land use changes and land ownership rights. Agriculture as a primary sustenance led to deforestation, inefficient farming methods, and bush clearing for cultivation. The study suggests giving households the skills to diversify their income to reduce their dependence on agricultural land use. Land use policy and community education about environmental protection's benefits for agriculture are needed.
DOI: 10.6084/m9.figshare.21973346
title: Food security and Land use changes Household Data.sav

description: Land use changes, spurred by human growth, are still shaping biodiversity worldwide. However, land ownership rights and land use changes have not been sufficiently studied. This study examined land ownership rights and agricultural land usage in Mwatate Sub County, Taita County, Kenya. The study sampled 301 stratified disproportionate and simple random households using a cross sectional design. Data entry and analysis used SPSS 23.0. The Neo-Malthusian hypothesis is utilized to analyze the study results, which demonstrate higher population growth and increased anthropogenic forces on agricultural land. This was due to unsustainable land use practices such deforestation, tree logging, high fuelwood use, clearing bushes for human settlement and cultivation, traditional farming methods, and reliance on agriculture for livelihood. A 95%-confidence Chi-square investigation found a strong link between agricultural land use changes and land ownership rights. Agriculture as a primary sustenance led to deforestation, inefficient farming methods, and bush clearing for cultivation. The study suggests giving households the skills to diversify their income to reduce their dependence on agricultural land use. Land use policy and community education about environmental protection's benefits for agriculture are needed.
DOI: 10.6084/m9.figshare.21973346.v2
title: Food security and Land use changes Household Data.sav

description: Land use changes, spurred by human growth, are still shaping biodiversity worldwide. However, land ownership rights and land use changes have not been sufficiently studied. This study examined land ownership rights and agricultural land usage in Mwatate Sub County, Taita County, Kenya. The study sampled 301 stratified disproportionate and simple random households using a cross sectional design. Data entry and analysis used SPSS 23.0. The Neo-Malthusian hypothesis is utilized to analyze the study results, which demonstrate higher population growth and increased anthropogenic forces on agricultural land. This was due to unsustainable land use practices such deforestation, tree logging, high fuelwood use, clearing bushes for human settlement and cultivation, traditional farming methods, and reliance on agriculture for livelihood. A 95%-confidence Chi-square investigation found a strong link between agricultural land use changes and land ownership rights. Agriculture as a primary sustenance led to deforestation, inefficient farming methods, and bush clearing for cultivation. The study suggests giving households the skills to diversify their income to reduce their dependence on agricultural land use. Land use policy and community education about environmental protection's benefits for agriculture are needed.
DOI: 10.5281/zenodo.7582191
title: BestBuy JSON dataset

description: Dataset used in several JsonPath engine (see JSurfer, JSonski and Rsonpath)
DOI: 10.58064/qek4-ft08
title: OK_En1CreVAChTfx_PVD

description: Pairwise visual discrimination (PD or PVD) task for a brainstem cholinergic-deficient mouse line. Male mice and their wild-type controls were evaluated at 11-13 months of age to assess discrimination learning and cognitive flexibility.
DOI: 10.58064/q115-ns87
title: OK_En1CreVAChTfx_PAL

description: Pairwise Associate Learning (PAL) task for a brainstem cholinergic-deficient mouse line. Male mice and their wild-type controls were evaluated at 11-13 months of age for learning and memory deficits.
DOI: 10.5281/zenodo.7582201
title: Google Map JSON data file

description: A data file used by several JSON engine.
DOI: 10.6084/m9.figshare.21973496
title: Climate 2021 prediction - Prophet

description: This dataset contains the prediction of the 2021 climate statistics based on center Africa.
DOI: 10.6084/m9.figshare.21973496.v1
title: Climate 2021 prediction - Prophet

description: This dataset contains the prediction of the 2021 climate statistics based on center Africa.
DOI: 10.58064/52xx-2y73
title: OK_En1CreVAChTfx_5Choice

description: 5-Choice Serial Reaction Time (5-CSRT) Task for a brainstem cholinergic-deficient mouse line. Male mice and their wild-type controls were evaluated at 7-10 months of age for attention deficits.
DOI: 10.58064/zsc8-sy91
title: FB_PD_M83(A53T)HOMOZYGOUS_PVD

description: Pairwise Visual discrimination task (PD or PVD) for alpha synuclein M83 Parkinson's mouse model (HOMOZYGOUS)
DOI: 10.58064/wtnk-tz18
title: FB_PD_5choice M83(A53T)Homozygous

description: 5 choice - M83 Parkinson's mouse model of age (Homozygous_Males).
DOI: 10.25505/fiz.icsd.cc2f3mkr
title: CSD 2237814: Experimental Crystal Structure Determination

description: Related Article: Samir MATAR|2023|ICSD Communication|||
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.25505/fiz.icsd.cc2f47tn
title: CSD 2238411: Experimental Crystal Structure Determination

description: Related Article: Ibrahim Naeem;  Mohamed Gobara;  Sherif Elbasuney|2023|ICSD Communication|||
description: An entry from the Inorganic Crystal Structure Database, the world’s repository for inorganic crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the joint CCDC and FIZ Karlsruhe Access Structures service and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.3761562.v2473
title: Online Labour Index: Measuring the Online Gig Economy for Policy and Research

description: Data repository for the data underlying the Online Labour Index. See http://ilabour.oii.ox.ac.uk online-labour-index/ for details.
DOI: 10.58064/0qtj-be81
title: OK_D2CreVGLUT3fx_PVD

description: Pairwise visual discrimination (PD or PVD) task for a mouse line unable to secrete glutamate from cholinergic interneurons. Male mice and their wild-type controls were evaluated at 4-6 months of age to assess discrimination learning and cognitive flexibility.
DOI: 10.34740/kaggle/dsv/4916177
title: IMDB Movies - All Categories
DOI: 10.34740/kaggle/ds/2851101
title: Company Filings - SEC Data Made Usable (10K & 10Q)
DOI: 10.5281/zenodo.7582278
title: Datos curados del INE y Ayuntamiento de Madrid

description: Dataset resultante de la limpieza, curación y combinación de los datos del INE y el Ayuntamiento de Madrid del 2016 hasta el 2019. Incluye información sobre la renta media por persona, el nivel de formación educativa de los residentes mayores de 25 años y el número de centros educativos en cada uno de los 21 distritos del Ayuntamiento de Madrid durante los años 2016-2019. El nivel de formación tiene 6 niveles: No sabe leer ni escribirBachiller elementalFormación profesionalTitulados mediosEstudios universitariosNivel de estudios no consta.Los centros educativos se clasifican también en 6 niveles:Escuelas infantiles municipalesEscuelas infantiles públicas de la Comunidad Autónoma de MadridEscuelas infantiles privadasColegios públicos infantil/primariaInstitutos públicos de educación secundariaColegios privados infantil/primaria.Los datos de renta fueron proporcionados por el INE, mientras que los datos sobre la formación y los centros educativos fueron solicitados al portal de datos abiertos del Ayuntamiento de Madrid. Los datos fueron prepreocesados y curados utilizando herramientas de software libre como NumPy y pandas en Python versión 3.10.6 dando como resultado el dataset (.xlsx) que puede encontrar aquí. El archivo de Python y los dataset originales utilizados para generar este recurso digital los puede encontrar en https://doi.org/10.5281/zenodo.7581810
DOI: 10.5281/zenodo.7582277
title: Datos curados del INE y Ayuntamiento de Madrid

description: Dataset resultante de la limpieza, curación y combinación de los datos del INE y el Ayuntamiento de Madrid del 2016 hasta el 2019. Incluye información sobre la renta media por persona, el nivel de formación educativa de los residentes mayores de 25 años y el número de centros educativos en cada uno de los 21 distritos del Ayuntamiento de Madrid durante los años 2016-2019. El nivel de formación tiene 6 niveles: No sabe leer ni escribirBachiller elementalFormación profesionalTitulados mediosEstudios universitariosNivel de estudios no consta.Los centros educativos se clasifican también en 6 niveles:Escuelas infantiles municipalesEscuelas infantiles públicas de la Comunidad Autónoma de MadridEscuelas infantiles privadasColegios públicos infantil/primariaInstitutos públicos de educación secundariaColegios privados infantil/primaria.Los datos de renta fueron proporcionados por el INE, mientras que los datos sobre la formación y los centros educativos fueron solicitados al portal de datos abiertos del Ayuntamiento de Madrid. Los datos fueron prepreocesados y curados utilizando herramientas de software libre como NumPy y pandas en Python versión 3.10.6 dando como resultado el dataset (.xlsx) que puede encontrar aquí. El archivo de Python y los dataset originales utilizados para generar este recurso digital los puede encontrar en https://doi.org/10.5281/zenodo.7581810
DOI: 10.5281/zenodo.7581928
title: Datasets originales proporcionados por el INE y Ayuntamiento de Madrid

description: Incluye información sobre la renta media por persona, el nivel de formación educativa de los residentes mayores de 25 años y el número de centros educativos en cada uno de los 21 distritos del Ayuntamiento de Madrid durante los años 2016-2019. El nivel de formación tiene 6 niveles: No sabe leer ni escribir Bachiller elemental Formación profesional Titulados medios Estudios universitarios Nivel de estudios no consta. Los centros educativos se clasifican también en 6 niveles: Escuelas infantiles municipales Escuelas infantiles públicas de la Comunidad Autónoma de Madrid Escuelas infantiles privadas Colegios públicos infantil/primaria Institutos públicos de educación secundaria Colegios privados infantil/primaria. Los datos de renta fueron proporcionados por el INE, mientras que los datos sobre la formación y los centros educativos fueron solicitados al portal de datos abiertos del Ayuntamiento de Madrid.
DOI: 10.5281/zenodo.7581927
title: Datasets originales proporcionados por el INE y Ayuntamiento de Madrid

description: Incluye información sobre la renta media por persona, el nivel de formación educativa de los residentes mayores de 25 años y el número de centros educativos en cada uno de los 21 distritos del Ayuntamiento de Madrid durante los años 2016-2019. El nivel de formación tiene 6 niveles: No sabe leer ni escribir Bachiller elemental Formación profesional Titulados medios Estudios universitarios Nivel de estudios no consta. Los centros educativos se clasifican también en 6 niveles: Escuelas infantiles municipales Escuelas infantiles públicas de la Comunidad Autónoma de Madrid Escuelas infantiles privadas Colegios públicos infantil/primaria Institutos públicos de educación secundaria Colegios privados infantil/primaria. Los datos de renta fueron proporcionados por el INE, mientras que los datos sobre la formación y los centros educativos fueron solicitados al portal de datos abiertos del Ayuntamiento de Madrid.
DOI: 10.34740/kaggle/dsv/4916371
title: US counties COVID 19 dataset
DOI: 10.5281/zenodo.7582318
title: [Raw data] Media Coverage of 3D Visual Tools Used in Urban Participatory Planning

description: Raw information on the articles used for the publication: Media Coverage of 3D Visual Tools Used in Urban Participatory Planning
DOI: 10.5281/zenodo.7582317
title: [Raw data] Media Coverage of 3D Visual Tools Used in Urban Participatory Planning

description: Raw information on the articles used for the publication: Media Coverage of 3D Visual Tools Used in Urban Participatory Planning
DOI: 10.6084/m9.figshare.21973529
title: Benchmark Architectures and Running Logs for RANUM

description: 1. The GRIST benchmark (in both original program format and ONNX format, model_zoo.zip) for numerical defects in deep neural networks. 2. The running logs (results.zip) for RANUM and GRIST tool. See detailed documentation at https://github.com/llylly/RANUM.
DOI: 10.25919/fw8v-mh76
title: Parkes pulsar observations with undefined project IDs for 2022OCTS_14

description: Parkes pulsar observations with undefined project IDs for 2022OCTS. This collection contains observations from project ID PX092.