cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7734161
title: Gridded Global Revisit Periods of Landsat, Sentinel-1, Sentinel-2 Satellites and their Combination

description: This is a global dataset of revisit periods of individual satellites and their combination based on a 0.5-degree resolution grid.<br> Revisit periods are defined as the time between two consecutive observations of a particular point on the surface, for the satellite missions Landsat, Sentinel-2 and Sentinel-1. The grid was created using ArcMap 10.8.1 and intersections of the grid were used to create points. For each individual point, average revisit times (i.e., to account for irregular revisits, downlink issues) were calculated for each individual satellite and the composite of the three satellites. Averaged revisit times for each of these points were calculated based on the number of image tiles that intersected a particular grid point with more than a 30-minute time difference between each other acquired between 01 Jan 2016 and 31 Dec 2020.<br> The following equation is used to calculate revisit periods: Average revisit time for a grid point = (Number of days between 01 Jan 2016 and 31 Dec 2020 (1827)) / (Total Number of Images captured) Only revisits occurring between 82.5 N and 55 S of land grid points are considered; Antarctica is omitted from analysis. For satellite missions that consist of two spacecraft orbiting simultaneously (Sentinel-1 A/B, and Sentinel-2 A/B), images acquired by both satellites were used in average revisit period calculation for a given grid point. Sum totals of image tiles of all three missions are used to calculate composite point-based revisit times.
description: This dataset was produced as part of the manuscript "A multi-sensor approach for increased measurements of floods and their societal impacts from space" which is currently in review.
DOI: 10.5281/zenodo.7699591
title: Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L4)

description: Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. File naming convention: 2001..2020 = time reference: period 2001-2020, QTP_DNN_Sm = Dataset ID, L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm), day1..day365/day366 = Date order within the year (January 1st - December 31st), pkl = Data storage format.
DOI: 10.5281/zenodo.7699572
title: Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L1)

description: Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. File naming convention: 2001..2020 = time reference: period 2001-2020, QTP_DNN_Sm = Dataset ID, L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm), day1..day365/day366 = Date order within the year (January 1st - December 31st), pkl = Data storage format.
DOI: 10.5281/zenodo.7699585
title: Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L3)

description: Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. File naming convention: 2001..2020 = time reference: period 2001-2020, QTP_DNN_Sm = Dataset ID, L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm), day1..day365/day366 = Date order within the year (January 1st - December 31st), pkl = Data storage format.
DOI: 10.5281/zenodo.7699578
title: Soil moisture in %(m3/m3) at 4 layer at 1000 m resolution in Qinghai-Tibet Plateau (QTP_DNN_Sm_L2)

description: Based on the deep neural network, using the SMAP and ERA5 datasets as the target data, and considering the elements of the water cycle process and environmental factors as predictor variables for training, a daily multi-layer soil moisture dataset with a resolution of 1000 meters from 2001 to 2020 was produced. The data set is stored as integer data, scale=100000. File naming convention: 2001..2020 = time reference: period 2001-2020, QTP_DNN_Sm = Dataset ID, L1..L4 = 4 layer soil depth (0-7cm, 7-28cm, 28-100cm, 100-289cm), day1..day365/day366 = Date order within the year (January 1st - December 31st), pkl = Data storage format.
DOI: 10.6084/m9.figshare.21976805
title: Structural dynamics in the evolution of SARS-CoV-2 spike glycoprotein - deuterium uptake plots

description: Deuterium uptake plots of HDX-MS data sets contained in "Structural dynamics in the evolution of SARS-CoV-2 spike glycoprotein" Valeria Calvaresi*, Antoni G. Wrobel*, Joanna Toporowska, Dietmar Hammerschmid, Katie J. Doores, Richard T. Bradshaw, Ricardo B. Parsons, Donald J. Benton, Chloë Roustan, Eamonn Reading, Michael H. Malim, Steven J. Gamblin, Argyris Politis* <br> Nature Communications, March 2023
DOI: 10.6084/m9.figshare.21976805.v1
title: Structural dynamics in the evolution of SARS-CoV-2 spike glycoprotein - deuterium uptake plots

description: Deuterium uptake plots of HDX-MS data sets contained in "Structural dynamics in the evolution of SARS-CoV-2 spike glycoprotein" Valeria Calvaresi*, Antoni G. Wrobel*, Joanna Toporowska, Dietmar Hammerschmid, Katie J. Doores, Richard T. Bradshaw, Ricardo B. Parsons, Donald J. Benton, Chloë Roustan, Eamonn Reading, Michael H. Malim, Steven J. Gamblin, Argyris Politis* <br> Nature Communications, March 2023
DOI: 10.6084/m9.figshare.22271047
title: FLS2_CDS Sequences_Unaligned_With Psuedogenes

description: Nucleocide sequences of FLS2 homologs from Solanaceae Domestic and Wild Species of <em>Solanum</em> (tomato, potato, eggplant), <em>Capsicum</em>, <em>Nicotiana</em>, <em>Petunia</em>, and the Solanaceae outgroup <em>Ipomoea triloba </em>(morning glory). This file has pseudogenes and is unaligned. The FLS2 coding sequences were obtained using a TBLASTN search using the FLS2 from <em>Arabidopsis thaliana</em> against Sol Genomics Database, Ensembl, and the Eggplant Genomic Database.
DOI: 10.6084/m9.figshare.22271047.v1
title: FLS2_CDS Sequences_Unaligned_With Psuedogenes

description: Nucleocide sequences of FLS2 homologs from Solanaceae Domestic and Wild Species of <em>Solanum</em> (tomato, potato, eggplant), <em>Capsicum</em>, <em>Nicotiana</em>, <em>Petunia</em>, and the Solanaceae outgroup <em>Ipomoea triloba </em>(morning glory). This file has pseudogenes and is unaligned. The FLS2 coding sequences were obtained using a TBLASTN search using the FLS2 from <em>Arabidopsis thaliana</em> against Sol Genomics Database, Ensembl, and the Eggplant Genomic Database.
DOI: 10.6084/m9.figshare.22271626
title: FLS2_CDS Sequences_MUSCLE Alignment_Removed Pseudogenes

description: Muscle Aligned Nucleocide sequences of FLS2 homologs from Solanaceae Domestic and Wild Species of <em>Solanum</em> (tomato, potato, eggplant), <em>Capsicum</em>, <em>Nicotiana</em>, <em>Petunia</em>, and the Solanaceae outgroup <em>Ipomoea triloba </em>(morning glory). This file has pseudogenes removed and is aligned. The FLS2 coding sequences were obtained using a TBLASTN search using the FLS2 from <em>Arabidopsis thaliana</em> against Sol Genomics Database, Ensembl, and the Eggplant Genomic Database.
DOI: 10.25452/figshare.plus.22270450
title: THINGS-data: MRI protocols

description: Scanning protocoles of the MRI expriment. Details are split up across two PDF files: "S1.pdf" provides information about scanning parameters for all tasks sessions of one example subject. "Localizer_Session.pdf" provides information about the localizer session. <br> Part of THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in brain and behavior <br> See related materials in Collection at: https://doi.org/10.25452/figshare.plus.c.6161151
DOI: 10.5281/zenodo.7204071
title: Dataset for "The Orbital Eccentricity Distribution of Planets Orbiting M dwarfs"

description: Dataset for the submitted manuscript "The Orbital Eccentricity Distribution of Planets Orbiting M dwarfs" (Sagear & Ballard)
DOI: 10.5281/zenodo.7204072
title: Dataset for "The Orbital Eccentricity Distribution of Planets Orbiting M dwarfs"

description: Dataset for the submitted manuscript "The Orbital Eccentricity Distribution of Planets Orbiting M dwarfs" (Sagear & Ballard)
DOI: 10.5281/zenodo.7731019
title: Dataset for "The Orbital Eccentricity Distribution of Planets Orbiting M dwarfs"

description: Dataset for the submitted manuscript "The Orbital Eccentricity Distribution of Planets Orbiting M dwarfs" (Sagear & Ballard)
DOI: 10.25345/c5j09wf0g
title: MassIVE MSV000091486 - GNPS - Dataset Creation from GNPS Molcular Networking - 5b36068b96524bda86d9db721f3a8379
title: Untargeted metabolomics of deep sea corals collected in Southern California Borderlands including some shallow water species and standards
DOI: 10.5281/zenodo.7733730
title: Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations

description: Supplementary dataset for manuscript "Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations".
DOI: 10.5281/zenodo.7733729
title: Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations

description: Supplementary dataset for manuscript "Effect of Single-Residue Mutations on CTCF Binding to DNA: Insights from Molecular Dynamics Simulations".
DOI: 10.6073/pasta/9b5abafd602c097c793dabcd9f5e0bb9
title: PAR data for the Green Lake 4 buoy, 2018 - ongoing.

description: High-resolution water quality data are fundamental to observing rapid ecological responses to meteorology, climate, and other disturbance events. Here we describe the deployment of a single buoy line with multiple sensors at fixed-depths from a subsurface float in the water-column of Green Lake 4 (GL4). Sensors on the buoy collect data in both summer and winter, thereby providing valuable insights into lake characteristics beyond our standard sampling period, including key transitional periods such as ice formation and ice break-up.
DOI: 10.5517/ccdc.csd.cc1lsxy7
title: CCDC 1483720: Experimental Crystal Structure Determination

description: Related Article: Mohsen M. Mostafa, Hamed M. El-Shora, Haitham K. Elemam, Waleed H. Al-Assy|2023|J.Mol.Struct.|1279|134963|doi:10.1016/j.molstruc.2023.134963
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.cc255kjg
title: CCDC 2001345: Experimental Crystal Structure Determination

description: Related Article: Minhui Yu, Tao Jin, Xiaohua Wang, Haohu Li, Decai Ji, Jinzhong Yao, Heyang Zeng, Senlei Shi, Kaimeng Xu, Lianpeng Zhang|2023|RSC Advances|13|6210|doi:10.1039/D3RA00030C
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: 10.6073/pasta/5e659c5f72a20ac3af7090cf2cba59d5
title: Chlorophyll-a data for the Green Lake 4 buoy, 2018 - ongoing.

description: High-resolution water quality data are fundamental to observing rapid ecological responses to meteorology, climate, and other disturbance events. Here we describe the deployment of a single buoy line with multiple sensors at fixed-depths from a subsurface float in the water-column of Green Lake 4 (GL4). Sensors on the buoy collect data in both summer and winter, thereby providing valuable insights into lake characteristics beyond our standard sampling period, including key transitional periods such as ice formation and ice break-up.
DOI: 10.5517/ccdc.csd.cc2bcyh5
title: CCDC 2156437: Experimental Crystal Structure Determination

description: Related Article: Bo Yang, Zhong-Duo Yang, Ke-Zhen Ma, Wen-Lin Guo, Tian-Hao Zhang, Tian-Kun Zhao|2023|Phytochemistry|209|113613|doi:10.1016/j.phytochem.2023.113613
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.cc2d0nrv
title: CCDC 2205177: Experimental Crystal Structure Determination

description: Related Article: Jing-Yan Xian, Xue-Xian Xie, Zi-Yuan Huang, Yi-Lin Liu, Hai-Yan Song, Zheng-Qiang Chen, Yong-Cong Ou, Sheng-Run Zheng|2023|Cryst.Growth Des.|23|1448|doi:10.1021/acs.cgd.2c01023
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: 10.34740/kaggle/dsv/5166611
title: Discover Dhaka City's Hospitals: Google Maps Data
DOI: 10.5281/zenodo.7732595
title: Exposing the CSI - S1

description: This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.
DOI: 10.5281/zenodo.7732594
title: Exposing the CSI - S1

description: This dataset has been used to study a CSI-based activity-recognition framework in the paper "Exposing the CSI: A Systematic Investigation of CSI-based Wi-Fi Sensing Capabilities and Limitations". The dataset is split into several archives, one for each experiment considered in the paper. A description of all the experiments can be found in the paper, together with a map of the lab reporting the location of all the nodes and target positions.
DOI: 10.5281/zenodo.7734425
title: Goat milk microbiota of Dutch farms

description: Microbiota composition of raw milk in relation to Dutch goat farm management. After amplicon sequencing we analyzed the taxa at phylum and genus levels. On ten farms our results indicated that the number of microbiota composition of the milk, directly after milking was variable among farms and not related to the farm management system. The milk microbiota showed a farm-specific fingerprint. Repeated sampling of a farm showed that this fingerprint changed over time. The lactic acid producing bacteria showed a similar pattern. Variable microbiota richness amount and diversity of microorganisms were present in different farming systems. We concluded that farm-specific management and sampling moment were the major determining factors for the milk microbiota composition.
description: This dataset is already pre-processed and can be directly loaded in R using the phyloseq package. Note this data is not rarefied.
DOI: 10.5281/zenodo.7734424
title: Goat milk microbiota of Dutch farms

description: Microbiota composition of raw milk in relation to Dutch goat farm management. After amplicon sequencing we analyzed the taxa at phylum and genus levels. On ten farms our results indicated that the number of microbiota composition of the milk, directly after milking was variable among farms and not related to the farm management system. The milk microbiota showed a farm-specific fingerprint. Repeated sampling of a farm showed that this fingerprint changed over time. The lactic acid producing bacteria showed a similar pattern. Variable microbiota richness amount and diversity of microorganisms were present in different farming systems. We concluded that farm-specific management and sampling moment were the major determining factors for the milk microbiota composition.
description: This dataset is already pre-processed and can be directly loaded in R using the phyloseq package. Note this data is not rarefied.
DOI: 10.3886/e185861v1
title: HES Interviews Dataset

description: This dataset includes semi-structured interviews conducted to explore people's experiences with a Covid-19 tracking app. The interviews specifically aimed to investigate what motivates users to download and use the app, whether they find the app useful, how they feel about data privacy and whether using the app leads to a change in behavior.<br>
DOI: 10.6073/pasta/295644787ee8a1c00edd6f48dde75c4e
title: ANA01 Weekly, seasonal and annual measurement of precipitation volume and chemistry collected as part of the National Atmospheric Deposition Program at Konza Prairie

description: Data set contains results of chemical analysis of wetfall samples collected on Konza Prairie. Analysis is done by the Central Analytical Lab (CAL), Champaign, IL as part of the National Atmospheric Deposition Program (NADP). NADP data products available on the NADP/NTN web site (nadp.slh.wisc.edu/data/NTN/) include: Annual Data Summaries, Semiannual Data Reports, Annual and Seasonal Averages, Monthly Averages, and Weekly data. Konza Prairie LTER archives and provides the weekly data in electronic form before May 2019.
DOI: 10.5281/zenodo.7649143
title: SuperDARN Grid data in netCDF format (2012-Aug)

description: 2012-Aug SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7649144
title: SuperDARN Grid data in netCDF format (2012-Aug)

description: 2012-Aug SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7649174
title: SuperDARN Grid data in netCDF format (2012-Jul)

description: 2012-Jul SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.7649175
title: SuperDARN Grid data in netCDF format (2012-Jul)

description: 2012-Jul SuperDARN radar data in netCDF format. These files were produced using versions 3.0 of the public FitACF and make_grid algorithms, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.The RST is available here: https://github.com/SuperDARN/rstThe research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation. Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications. A brief statement on how to acknowledge use of SuperDARN data is provided below.Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication. A listing of radars and PIs with contact information can be found here: (SuperDARN Radar Overview)<strong>Recommended form of acknowledgement for the use of SuperDARN data:</strong>'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'