cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5517/ccdc.csd.cc2fylrr
title: CCDC 2262775: Experimental Crystal Structure Determination

description: Related Article: Yi-Qi Tian, Wen-Lei Mu, Lin-Lin Wu, Xiao-Yi Yi, Jun Yan, Chao Liu|2023|Chemical Science|14|10212|doi:10.1039/D3SC02793G
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.cc2fylss
title: CCDC 2262776: Experimental Crystal Structure Determination

description: Related Article: Yi-Qi Tian, Wen-Lei Mu, Lin-Lin Wu, Xiao-Yi Yi, Jun Yan, Chao Liu|2023|Chemical Science|14|10212|doi:10.1039/D3SC02793G
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.cc2fyltt
title: CCDC 2262777: Experimental Crystal Structure Determination

description: Related Article: Yi-Qi Tian, Wen-Lei Mu, Lin-Lin Wu, Xiao-Yi Yi, Jun Yan, Chao Liu|2023|Chemical Science|14|10212|doi:10.1039/D3SC02793G
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: 10.48577/jpl.roxzclta
title: FPGA Implementation of an Adaptive Sweep Algorithm for Spacecraft Radios

description: Spacecraft communicate with the Deep Space Network (DSN) at a predetermined carrier frequency. However,signals are Doppler shifted away from the original transmittedfrequency due to the high orbital velocities of the spacecraft.This paper describes the implementation of an adaptive sweepalgorithm on a Xilinx Kintex-7 field-programmable gate array(FPGA). This algorithm estimates the carrier Doppler shift andcompensates for it to allow for coherent data demodulation. Thealgorithm is implemented in MATLAB’s Simulink, compliedto Verilog using HDL Coder, and run on the FPGA using theFPGA-in-the-loop Wizard. The FPGA implementation has beenvalidated in the presence of noise, by comparing the standarddeviation of the Doppler residuals at different signal-to-noiseratios, to values obtained via a theoretical analysis of the carriersynchronization loop. Results indicate excellent agreement andthus validate our implementation. We have also tested thealgorithm against flight data obtained from the Lunar Reconnaissance Orbiter (LRO) and the Deep Space Network (DSN),and proven that the algorithm can successfully acquire andtrack the carrier.
DOI: 10.5517/ccdc.csd.cc2g4qq2
title: CCDC 2268664: Experimental Crystal Structure Determination

description: Related Article: Mingwan Leng|2023|CSD Communication|||
description: Related Article: Mingwan Leng, Nandu Koripally, Junjie Huang, Aikaterini Vriza, Kyeong Yeon Lee, Xiaozhou Ji, Chenxuan Li, Megan Hays, Qing Tu, Kim Dunbar, Jie Xu, Tse Nga Ng, Lei Fang|2023|Materials Horizons|10|4354|doi:10.1039/D3MH00883E
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.cc2g4ryb
title: CCDC 2268702: Experimental Crystal Structure Determination

description: Related Article: Lee Hee-Seung, Jaewook Kim|2023|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.cc2g4rzc
title: CCDC 2268703: Experimental Crystal Structure Determination

description: Related Article: Hee-Seung, Lee, Kim, Jaewook|2023|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: 10.5281/zenodo.7590773
title: DeepD3 Datasets

description: In this repository, you will find: The DeepD3 benchmark dataset - a TIFF stack with an extent of 135.7 by 34.4 by 35.5 µm from the rat hippocampal CA1 region (94 x 94 x 500 nm voxel size) The dendrite tracings of the DeepD3 benchmark dataset by three human annotators U, V and W in SWC file format, traced with neutube The pixel-precise spine annotations of the DeepD3 benchmark dataset by three human annotators as *.mask files (compatible with pipra, but easy to open with any HDF5 reader) The human annotations and clusters for the DeepD3 benchmark dataset as CSV file A Jupyter notebook that shows how we assigned the human annotations to individual clusters that pertain to individual dendritic spines The DeepD3 training data as d3set file The DeepD3 validation data as d3set file (see paper for further information, basically HDF5 files)
DOI: 10.5281/zenodo.8428849
title: DeepD3 Datasets

description: In this repository, you will find: The DeepD3 benchmark dataset - a TIFF stack with an extent of 135.7 by 34.4 by 35.5 µm from the rat hippocampal CA1 region (94 x 94 x 500 nm voxel size) The dendrite tracings of the DeepD3 benchmark dataset by three human annotators U, V and W in SWC file format, traced with neutube The pixel-precise spine annotations of the DeepD3 benchmark dataset by three human annotators as *.mask files (compatible with pipra, but easy to open with any HDF5 reader) The human annotations and clusters for the DeepD3 benchmark dataset as CSV file A Jupyter notebook that shows how we assigned the human annotations to individual clusters that pertain to individual dendritic spines The DeepD3 training data as d3set file The DeepD3 validation data as d3set file (see paper for further information, basically HDF5 files)
DOI: 10.5281/zenodo.7590772
title: DeepD3 Datasets

description: In this repository, you will find: The DeepD3 benchmark dataset - a TIFF stack with an extent of 135.7 by 34.4 by 35.5 µm from the rat hippocampal CA1 region (94 x 94 x 500 nm voxel size) The dendrite tracings of the DeepD3 benchmark dataset by three human annotators U, V and W in SWC file format, traced with neutube The pixel-precise spine annotations of the DeepD3 benchmark dataset by three human annotators as *.mask files (compatible with pipra, but easy to open with any HDF5 reader) The human annotations and clusters for the DeepD3 benchmark dataset as CSV file A Jupyter notebook that shows how we assigned the human annotations to individual clusters that pertain to individual dendritic spines The DeepD3 training data as d3set file The DeepD3 validation data as d3set file (see paper for further information, basically HDF5 files)
DOI: 10.5281/zenodo.8380702
title: Quasi-Static and Fatigue Testing Dataset for soft bone cements according to ASTM F2118

description: This Dataset features quasi-static and fatigue data of PMMA cements. All the tests were run on an MTS 858 Mini Bionix (MTS Systems Corporation, United States). In summary, this dataset contains: Videos captured for marker tracking used in a virtual extensometer (.mp4) Quasi-static testing data for the PMMA cements (.txt) Fatigue data for three different stress levels (5MPa, 7MPa, 9MPa) (.txt) An Excel sheet with corrected tensile properties (.xlsx) General Abbreviations: VS is the V-Steady Cement VSLA is the V-Steady Cement with 12%vol linoleic acid Abbreviations for Fatigue Data: B is the batch number S is the sample number
DOI: 10.5281/zenodo.8380701
title: Quasi-Static and Fatigue Testing Dataset for soft bone cements according to ASTM F2118

description: This Dataset features quasi-static and fatigue data of PMMA cements. All the tests were run on an MTS 858 Mini Bionix (MTS Systems Corporation, United States). In summary, this dataset contains: Videos captured for marker tracking used in a virtual extensometer (.mp4) Quasi-static testing data for the PMMA cements (.txt) Fatigue data for three different stress levels (5MPa, 7MPa, 9MPa) (.txt) An Excel sheet with corrected tensile properties (.xlsx) General Abbreviations: VS is the V-Steady Cement VSLA is the V-Steady Cement with 12%vol linoleic acid Abbreviations for Fatigue Data: B is the batch number S is the sample number
DOI: 10.5281/zenodo.8428565
title: Data associated with: Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones.

description: These are the soil organic matter fraction and environmental covariate data associated with the manuscript, " Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones." Please note that these data are currently for review only; our agreement with farmers currently limits the public availability of these data for any purposes other than review by assigned peer-reviewers. Following review, data will be shared following a reasonable request.
DOI: 10.5281/zenodo.8428564
title: Data associated with: Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones.

description: These are the soil organic matter fraction and environmental covariate data associated with the manuscript, " Shifts in controls and abundance of particulate and mineral-associated organic matter fractions among subfield yield stability zones." Please note that these data are currently for review only; our agreement with farmers currently limits the public availability of these data for any purposes other than review by assigned peer-reviewers. Following review, data will be shared following a reasonable request.
DOI: 10.6084/m9.figshare.22775846
title: JMA data from 1964 to 2022

description: Japan Meteorological Agency (JMA) Research Vessels (OMMORV) data separated by year and saved in formats such as '.E'. There are various observation items in addition to nutrient data.
DOI: 10.6084/m9.figshare.22775846.v1
title: JMA data from 1964 to 2022

description: Japan Meteorological Agency (JMA) Research Vessels (OMMORV) data separated by year and saved in formats such as '.E'. There are various observation items in addition to nutrient data.
DOI: 10.6084/m9.figshare.22775849
title: NIFS data from 1964 to 2021

description: National Institute of Fisheries Science (NIFS) Serial Oceanographic Observation (SOO) data from Korea, separated by year and stored in '.csv' format
DOI: 10.6084/m9.figshare.22775852.v1
title: preprocessed data

description: Preprocessed observed nutrient, ETOPO depth, and coastline data(spatial polygons), saved in binary format '.RData' for direct use in R programming language.
DOI: 10.6084/m9.figshare.22775849.v1
title: NIFS data from 1964 to 2021

description: National Institute of Fisheries Science (NIFS) Serial Oceanographic Observation (SOO) data from Korea, separated by year and stored in '.csv' format
DOI: 10.6084/m9.figshare.22775852
title: preprocessed data

description: Preprocessed observed nutrient, ETOPO depth, and coastline data(spatial polygons), saved in binary format '.RData' for direct use in R programming language.
DOI: 10.6084/m9.figshare.22775855
title: source codes for spatiotemporal kriging

description: Code and subroutines for data collection, Kriging, and post-processing.
DOI: 10.6084/m9.figshare.22775855.v1
title: source codes for spatiotemporal kriging

description: Code and subroutines for data collection, Kriging, and post-processing.
DOI: 10.6084/m9.figshare.24131622
title: GLODAPv2.2022 product

description: To verify the nutrient data estimated by STK, we provide the GLODAPv2.2022 data, which was downloaded from https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0257247/ on September 13, 2023.
DOI: 10.6084/m9.figshare.22775990
title: Spatiotemporal Estimated Nutrients

description: The nutrient data reconstructed in grid format using Spatiotemporal Kriging. It is stored in '.csv' and '.RData' formats, and includes the validation results of the observation data using 10-fold cross-validation.
DOI: 10.6084/m9.figshare.22775990.v1
title: Spatiotemporal Estimated Nutrients

description: The nutrient data reconstructed in grid format using Spatiotemporal Kriging. It is stored in '.csv' and '.RData' formats, and includes the validation results of the observation data using 10-fold cross-validation.
DOI: 10.6084/m9.figshare.24131622.v1
title: GLODAPv2.2022 product

description: To verify the nutrient data estimated by STK, we provide the GLODAPv2.2022 data, which was downloaded from https://www.ncei.noaa.gov/data/oceans/ncei/ocads/data/0257247/ on September 13, 2023.
DOI: 10.21223/k5kuhl/xfuzry
title: 03_Datadictionary.txt

description: :unav
DOI: 10.21223/k5kuhl/3yl2sh
title: PTYL200510_LAVICT_Screened.xls

description: :unav
DOI: 10.21223/k5kuhl/mnnikm
title: 01_Fieldbook_PTYL200510_LAVICT_Screened.txt

description: :unav
DOI: 10.21223/k5kuhl/7jawzk
title: 02_Material_list.txt

description: :unav
DOI: 10.21223/k5kuhl
title: Dataset for: Screened potato clones in Huancayo, Junin 2005

description: An screening of 199 clones was conducted in Huancayo, Junin in 2005
DOI: 10.21231/nvzc-mf25
title: Assessment of N2O5 heterogeneous reaction mechanisms in the Community Multiscale Air Quality (CMAQ) model
DOI: 10.7281/t1/6jmkig/ph89hr
title: 1. README_WeatherData_Stettz2019.txt

description: Locations and periods of record for the four weather stations used in the Stettz et al. (2019) PLOS ONE publication
DOI: 10.7281/t1/6jmkig/ewj5eb
title: 2. Debre_Markos_temperature_and_solar_radiation.zip

description: Data from the Debre Markos weather station used in Stettz et al. (2019) PLOS ONE publication
DOI: 10.7281/t1/6jmkig/jh8mf2
title: 3. Enebi_temperature_and_solar_radiation.zip

description: Data from the Enebi weather station used in Stettz et al. (2019) PLOS ONE publication
DOI: 10.7281/t1/6jmkig/oa61wp
title: 5. Yejubie_temperature_and_solar_radiation.zip

description: Data from the Yejubie weather station used in Stettz et al. (2019) PLOS ONE publication
DOI: 10.7281/t1/6jmkig/xveuqh
title: 4. Kurar_temperature_and_solar_radiation.zip

description: Data from the Kurar weather station used in Stettz et al. (2019) PLOS ONE publication
DOI: 10.5281/zenodo.7057741
title: Toloka Visual Question Answering Dataset

description: Our dataset consists of the images associated with textual questions. One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in Context dataset, MS COCO. All data labeling was performed on the Toloka crowdsourcing platform, https://toloka.ai/. Our dataset has 45,199 instances split among three subsets: <strong>train</strong> (38,990 instances), <strong>public test</strong> (1,705 instances), and <strong>private test</strong> (4,504 instances). The entire train dataset will be available for everyone since the start of the challenge. The public test dataset will be available since the evaluation phase of the competition, but without any ground truth labels. The private test dataset will not be available until the challenge ends. The datasets will be provided as files in the comma-separated values (CSV) format containing the following columns. <strong>Column</strong> <strong>Type</strong> <strong>Description</strong> image string URL of an image on a public content delivery network width integer image width height integer image height left integer bounding box coordinate: left top integer bounding box coordinate: top right integer bounding box coordinate: right bottom integer bounding box coordinate: bottom question string question in English This upload also contains a ZIP file with the images from MS COCO.
DOI: 10.5281/zenodo.7113781
title: Toloka Visual Question Answering Dataset

description: Our dataset consists of the images associated with textual questions. One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in Context dataset, MS COCO. All data labeling was performed on the Toloka crowdsourcing platform, https://toloka.ai/. Our dataset has 45,199 instances split among three subsets: <strong>train</strong> (38,990 instances), <strong>public test</strong> (1,705 instances), and <strong>private test</strong> (4,504 instances). The entire train dataset will be available for everyone since the start of the challenge. The public test dataset will be available since the evaluation phase of the competition, but without any ground truth labels. The private test dataset will not be available until the challenge ends. The datasets will be provided as files in the comma-separated values (CSV) format containing the following columns. <strong>Column</strong> <strong>Type</strong> <strong>Description</strong> image string URL of an image on a public content delivery network width integer image width height integer image height left integer bounding box coordinate: left top integer bounding box coordinate: top right integer bounding box coordinate: right bottom integer bounding box coordinate: bottom question string question in English This upload also contains a ZIP file with the images from MS COCO.
DOI: 10.5281/zenodo.7057740
title: Toloka Visual Question Answering Dataset

description: Our dataset consists of the images associated with textual questions. One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in Context dataset, MS COCO. All data labeling was performed on the Toloka crowdsourcing platform, https://toloka.ai/. Our dataset has 45,199 instances split among three subsets: <strong>train</strong> (38,990 instances), <strong>public test</strong> (1,705 instances), and <strong>private test</strong> (4,504 instances). The entire train dataset was available for everyone since the start of the challenge. The public test dataset was available since the evaluation phase of the competition, but without any ground truth labels. After the end of the competition, public and private sets were released. The datasets will be provided as files in the comma-separated values (CSV) format containing the following columns. <strong>Column</strong> <strong>Type</strong> <strong>Description</strong> image string URL of an image on a public content delivery network width integer image width height integer image height left integer bounding box coordinate: left top integer bounding box coordinate: top right integer bounding box coordinate: right bottom integer bounding box coordinate: bottom question string question in English This upload also contains a ZIP file with the images from MS COCO.
DOI: 10.5281/zenodo.7570356
title: Toloka Visual Question Answering Dataset

description: Our dataset consists of the images associated with textual questions. One entry (instance) in our dataset is a question-image pair labeled with the ground truth coordinates of a bounding box containing the visual answer to the given question. The images were obtained from a CC BY-licensed subset of the Microsoft Common Objects in Context dataset, MS COCO. All data labeling was performed on the Toloka crowdsourcing platform, https://toloka.ai/. Our dataset has 45,199 instances split among three subsets: <strong>train</strong> (38,990 instances), <strong>public test</strong> (1,705 instances), and <strong>private test</strong> (4,504 instances). The entire train dataset was available for everyone since the start of the challenge. The public test dataset was available since the evaluation phase of the competition, but without any ground truth labels. After the end of the competition, public and private sets were released. The datasets will be provided as files in the comma-separated values (CSV) format containing the following columns. <strong>Column</strong> <strong>Type</strong> <strong>Description</strong> image string URL of an image on a public content delivery network width integer image width height integer image height left integer bounding box coordinate: left top integer bounding box coordinate: top right integer bounding box coordinate: right bottom integer bounding box coordinate: bottom question string question in English This upload also contains a ZIP file with the images from MS COCO.
DOI: 10.5281/zenodo.8428525
title: Data and code for "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification"

description: It contains the code and the data to reproduce the figures in the paper "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification" published in arXiv: https://arxiv.org/abs/2309.09976
DOI: 10.5281/zenodo.8428524
title: Data and code for "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification"

description: It contains the code and the data to reproduce the figures in the paper "Des-q: a quantum algorithm to construct and efficiently retrain decision trees for regression and binary classification" published in arXiv: https://arxiv.org/abs/2309.09976
DOI: 10.6084/m9.figshare.22096859
title: README

description: Contains a brief description of the data as well as usage notes
DOI: 10.6084/m9.figshare.22096859.v1
title: README

description: Contains a brief description of the data as well as usage notes
DOI: 10.6084/m9.figshare.22101932
title: Data Quality Analysis Code

description: Data Quality Analysis Code
DOI: 10.6084/m9.figshare.22101932.v1
title: Data Quality Analysis Code

description: Data Quality Analysis Code
DOI: 10.6084/m9.figshare.23643138
title: BIDS compliant (ASTaUND)

description: It contains BIDS format data files.
DOI: 10.6084/m9.figshare.23643138.v1
title: BIDS compliant (ASTaUND)

description: It contains BIDS format data files.
DOI: 10.6084/m9.figshare.23643336.v1
title: Raw fNIRS (ASTaUND)

description: It contains raw fNIRS dataset along with csvfiles of metadata.
DOI: 10.6084/m9.figshare.23643336
title: Raw fNIRS (ASTaUND)

description: It contains raw fNIRS dataset along with csvfiles of metadata.
DOI: 10.26093/cds/vizier.35830122
title: Variability behaviour of CoRoT M-giant stars

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'The variability behaviour of CoRoT M-giant stars.' (bibcode: 2015A&A...583A.122F)
DOI: 10.26093/cds/vizier.36470106
title: PSZSPT, joint Planck and SPT-SZ cluster catalog

description: VizieR online Data Catalogue associated with article published in journal Astronomy & Astrophysics with title 'PSZSPT: a joint Planck and SPT-SZ cluster catalog.' (bibcode: 2021A&A...647A.106M)