cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6073/pasta/907dd1c7df45cec4fffdff6c890cfbb3
title: Herbaceous vegetation size and cover data in grasshopper survey quadrats at the Jornada Basin LTER site, 1984 to 1985

description: This dataset contains annual herbaceous plant data collected in association with grasshopper surveys during 1984 and 1985 as part of the Jornada Basin LTER program. Annual herbaceous plant data was collected from Jornada grasshopper plots on the east bajada of the Dona Ana Mts. Three plots were situated on the bajada parallel to and 200 m south of the LTER-I transect. Two additional sets of three plots were located 5 km and 10 km south of the LTER transect. Each plot is composed of two 50 m belt transects, each divided into ten 5-meter-squared quadrats. Annual plants were measured in the northeast 1 square meter of each quadrat. All individuals of each species were counted and measured. A mean diameter, height, and the total number of individuals per 1-square-meter quadrat were recorded. Measurements were taken in May, July, and September of 1984, and in May and September of 1985. This dataset consists of the date of collection, plot number, transect number, quadrat number, plant species code, mean plant diameter, and mean plant height. This dataset is complete.
DOI: 10.25377/sussex.17112080
title: Stereo event- and frame-based benchmark dataset for scene understanding

description: This is a proof-of-concept labelled dataset for training semantic segmentation and pose estimation vision systems with neuromorphic event-based vision<br>Recorded data directories take the form<b> ./data/[prop]/[sample #]</b>. Prop meshes are in <b>./props/[prop]</b> in STL format. Each data directory contains three HDF5 files containing segmented RGB frames, segmented DVS events and prop pose information. For each 30 second recording, both raw and undistorted (after lens distortion correction) data is saved. RGB frame and visual event labels are integers, with 0 meaning `background', -1 meaning `ambiguous' (multiple props overlap) and any <i>i</i> &gt; 0 denoting class <i>i</i>. The fields of the RGB frame HDF5 file <b>frame.h5</b> are as follows, where <i>i</i> in {0, ..., <i>n</i> - 1} and <i>n</i> = 2 is the number of cameras:<br>timestamp_<i>i</i>: frame timestamp (camera <i>i</i>)<br>image_raw_<i>i</i>: frame before distortion correction (camera <i>i</i>)<br>image_undistorted_<i>i</i>: frame after distortion correction (camera <i>i</i>)<br>label_<i>i</i>: pixelwise frame label (camera <i>i</i>)<br>The fields of the DVS event HDF5 file <b>event.h5</b> are as follows:<br>timestamp_<i>i</i>: event timestamp (camera <i>i</i>)<br>polarity_<i>i</i>: event polarity (camera <i>i</i>)<br>xy_raw_<i>i</i>: event <i>x</i> and <i>y</i> before distortion correction (camera <i>i</i>)<br>xy_undistorted_<i>i</i>: event <i>x</i> and <i>y</i> after distortion correction (camera <i>i</i>)<br>label_<i>i</i>: event label (camera <i>i</i>)<br>Prop pose information, including translation and rotation (respectively millimetres and degrees) are stored as floating-point numbers for both global 3D tracking coordinates and camera-centric coordinates. Floating-point not-a-number indicates bad or missing data. The fields of the prop pose HDF5 file <b>pose.h5</b> are as follows:<br>timestamp: pose timestamp<br>extrapolated[<i>p</i>]: true when the pose of prop <i>p</i> was extrapolated<br>rotation[<i>p</i>]: prop <i>p</i> rotation in global coordinates<br>camera_rotation_<i>i</i>[<i>p</i>]: prop <i>p</i> rotation relative to camera <i>i</i><br>translation[<i>p</i>][<i>m</i>]: prop <i>p</i> marker <i>m</i> translation in global coordinates<br>camera_translation_<i>i</i>[<i>p</i>][<i>m</i>]: prop <i>p</i> marker <i>m</i> translation relative to camera <i>i</i><i><br></i>The data is in HDF5 format which is accessible with a number of tools eg PyTables in Python.<br><br><br>
DOI: 10.25377/sussex.17112080.v1
title: Stereo event- and frame-based benchmark dataset for scene understanding

description: This is a proof-of-concept labelled dataset for training semantic segmentation and pose estimation vision systems with neuromorphic event-based vision<br>Recorded data directories take the form<b> ./data/[prop]/[sample #]</b>. Prop meshes are in <b>./props/[prop]</b> in STL format. Each data directory contains three HDF5 files containing segmented RGB frames, segmented DVS events and prop pose information. For each 30 second recording, both raw and undistorted (after lens distortion correction) data is saved. RGB frame and visual event labels are integers, with 0 meaning `background', -1 meaning `ambiguous' (multiple props overlap) and any <i>i</i> &gt; 0 denoting class <i>i</i>. The fields of the RGB frame HDF5 file <b>frame.h5</b> are as follows, where <i>i</i> in {0, ..., <i>n</i> - 1} and <i>n</i> = 2 is the number of cameras:<br>timestamp_<i>i</i>: frame timestamp (camera <i>i</i>)<br>image_raw_<i>i</i>: frame before distortion correction (camera <i>i</i>)<br>image_undistorted_<i>i</i>: frame after distortion correction (camera <i>i</i>)<br>label_<i>i</i>: pixelwise frame label (camera <i>i</i>)<br>The fields of the DVS event HDF5 file <b>event.h5</b> are as follows:<br>timestamp_<i>i</i>: event timestamp (camera <i>i</i>)<br>polarity_<i>i</i>: event polarity (camera <i>i</i>)<br>xy_raw_<i>i</i>: event <i>x</i> and <i>y</i> before distortion correction (camera <i>i</i>)<br>xy_undistorted_<i>i</i>: event <i>x</i> and <i>y</i> after distortion correction (camera <i>i</i>)<br>label_<i>i</i>: event label (camera <i>i</i>)<br>Prop pose information, including translation and rotation (respectively millimetres and degrees) are stored as floating-point numbers for both global 3D tracking coordinates and camera-centric coordinates. Floating-point not-a-number indicates bad or missing data. The fields of the prop pose HDF5 file <b>pose.h5</b> are as follows:<br>timestamp: pose timestamp<br>extrapolated[<i>p</i>]: true when the pose of prop <i>p</i> was extrapolated<br>rotation[<i>p</i>]: prop <i>p</i> rotation in global coordinates<br>camera_rotation_<i>i</i>[<i>p</i>]: prop <i>p</i> rotation relative to camera <i>i</i><br>translation[<i>p</i>][<i>m</i>]: prop <i>p</i> marker <i>m</i> translation in global coordinates<br>camera_translation_<i>i</i>[<i>p</i>][<i>m</i>]: prop <i>p</i> marker <i>m</i> translation relative to camera <i>i</i><i><br></i>The data is in HDF5 format which is accessible with a number of tools eg PyTables in Python.<br><br><br>
DOI: 10.5281/zenodo.5783868
title: uprolls2021-k

description: This deposit serves as a long-term archive for the unprocessed raw data behind the uprolls2021 table in my comprehensive dataset on religion and politics in India, which is available at https://github.com/raphael-susewind/india-religion-politics/tree/master/uprolls2021
DOI: 10.5281/zenodo.5783867
title: uprolls2021-k

description: This deposit serves as a long-term archive for the unprocessed raw data behind the uprolls2021 table in my comprehensive dataset on religion and politics in India, which is available at https://github.com/raphael-susewind/india-religion-politics/tree/master/uprolls2021
DOI: 10.5281/zenodo.5241286
title: Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets

description: The dataset of <em>Building Object and Outdoor Scene Segmentation (BOOSS)</em> is based on multi-channel aerial imagery data. It covers - Ground Truth - RGB - Thermal The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment Please cite as: Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." <em>Remote Sensing</em> 13, no. 21 (2021): 4357.
description: {"references": ["Y. Hou, L. Soibelman, R. Volk, and M. Chen, Factors Affecting the Performance of 3D Thermal Mapping for Energy Audits in A District by Using Infrared Thermography (IRT) Mounted on Unmanned Aircraft Systems (UAS), doi: https://doi.org/10.22260/ISARC2019/0036.", "Y. Hou, R. Volk, M. Chen, and L. Soibelman, (2021). Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions, Automation in Construction, vol. 124, doi: 10.1016/j.autcon.2021.103554."]}
DOI: 10.5281/zenodo.5241285
title: Building Object and Outdoor Scene Segmentation (BOOSS) - Multi-channel (RGB + Thermal) Aerial Imagery Datasets

description: The dataset of <em>Building Object and Outdoor Scene Segmentation (BOOSS)</em> is based on multi-channel aerial imagery data. It covers - Ground Truth - RGB - Thermal The annotations in version 1.0 include roofs, facades, cars, roof equipment, and ground equipment Please cite as: Hou, Yu, Meida Chen, Rebekka Volk, and Lucio Soibelman. "An Approach to Semantically Segmenting Building Components and Outdoor Scenes Based on Multichannel Aerial Imagery Datasets." <em>Remote Sensing</em> 13, no. 21 (2021): 4357.
description: {"references": ["Y. Hou, L. Soibelman, R. Volk, and M. Chen, Factors Affecting the Performance of 3D Thermal Mapping for Energy Audits in A District by Using Infrared Thermography (IRT) Mounted on Unmanned Aircraft Systems (UAS), doi: https://doi.org/10.22260/ISARC2019/0036.", "Y. Hou, R. Volk, M. Chen, and L. Soibelman, (2021). Fusing tie points' RGB and thermal information for mapping large areas based on aerial images: A study of fusion performance under different flight configurations and experimental conditions, Automation in Construction, vol. 124, doi: 10.1016/j.autcon.2021.103554."]}
DOI: 10.6084/m9.figshare.17209199
title: NCT05133557-Final Raw Saliva and Chip Data.xlsx

description: Dataset associated with "Effects of Chewing Gum Containing Freeze-Dried Blackberry Powder on Oral Microbiota: A Randomized Controlled Crossover Trial"
DOI: 10.6084/m9.figshare.17203784.v1
title: Dataset

description: Original GPM data can download from https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDF_06/summary?keywords="IMERG final"
DOI: 10.3886/e157401
title: School absenteeism and academic achievement: Does the reason for absence matter?

description: Studies consistently show associations between school absences and academic achievement. However, questions remain about whether this link depends on the reason for children’s absence. Using a sample of the Scottish Longitudinal Study (n = 4,419), we investigated whether the association between school absenteeism and achievement in high-stakes exams at the end of compulsory and post-compulsory schooling varies with the reason for absence. In line with previous research, our findings show that overall absences are negatively associated with academic achievement at both school stages. Likewise, all forms of absences (truancy, sickness absence, exceptional domestic circumstances, family holidays) are negatively associated with achievement at the end of compulsory and post-compulsory schooling. First difference regressions confirm these negative associations, except for family holidays. These results suggest that, in addition to lost instruction, other mechanisms such as behavioral, health-related, and psychosocial pathways may account for the association between absenteeism and achievement. The findings have implications for designing tailored absenteeism interventions to improve pupils’ academic achievement.
DOI: 10.3886/e157401v1
title: School absenteeism and academic achievement: Does the reason for absence matter?

description: Studies consistently show associations between school absences and academic achievement. However, questions remain about whether this link depends on the reason for children’s absence. Using a sample of the Scottish Longitudinal Study (n = 4,419), we investigated whether the association between school absenteeism and achievement in high-stakes exams at the end of compulsory and post-compulsory schooling varies with the reason for absence. In line with previous research, our findings show that overall absences are negatively associated with academic achievement at both school stages. Likewise, all forms of absences (truancy, sickness absence, exceptional domestic circumstances, family holidays) are negatively associated with achievement at the end of compulsory and post-compulsory schooling. First difference regressions confirm these negative associations, except for family holidays. These results suggest that, in addition to lost instruction, other mechanisms such as behavioral, health-related, and psychosocial pathways may account for the association between absenteeism and achievement. The findings have implications for designing tailored absenteeism interventions to improve pupils’ academic achievement.
DOI: 10.5519/qd.m6f0g187
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.5519/qd.ep23smmp
title: Query on the Natural History Museum Data Portal (data.nhm.ac.uk) (1 records)
DOI: 10.6084/m9.figshare.17209244
title: 2020 ACi Curves and Drought Tolerance for B. curtipendula and S. scoparium

description: All data, scripts, figures, and manuscript drafts for an experiment measuring the effects of drought on the photosynthetic physiology and aboveground primary production of side oats grama and little bluestem.<br>
DOI: 10.6084/m9.figshare.17209244.v1
title: 2020 ACi Curves and Drought Tolerance for B. curtipendula and S. scoparium

description: All data, scripts, figures, and manuscript drafts for an experiment measuring the effects of drought on the photosynthetic physiology and aboveground primary production of side oats grama and little bluestem.<br>
DOI: 10.5517/ccdc.csd.cc28xx6b
title: CCDC 2113152: Experimental Crystal Structure Determination

description: Related Article: Xinzhe Shi, Sihan Li, Melanie Reiß, Anke Spannenberg, Thorsten Holtrichter-Rößmann, Fabian Reiß, Torsten Beweries|2021|Chemical Science|12|16074|doi:10.1039/D1SC06052J
DOI: 10.5517/ccdc.csd.cc28xx7c
title: CCDC 2113153: Experimental Crystal Structure Determination

description: Related Article: Xinzhe Shi, Sihan Li, Melanie Reiß, Anke Spannenberg, Thorsten Holtrichter-Rößmann, Fabian Reiß, Torsten Beweries|2021|Chemical Science|12|16074|doi:10.1039/D1SC06052J
DOI: 10.5517/ccdc.csd.cc28xx8d
title: CCDC 2113154: Experimental Crystal Structure Determination

description: Related Article: Xinzhe Shi, Sihan Li, Melanie Reiß, Anke Spannenberg, Thorsten Holtrichter-Rößmann, Fabian Reiß, Torsten Beweries|2021|Chemical Science|12|16074|doi:10.1039/D1SC06052J
DOI: 10.5517/ccdc.csd.cc28xx9f
title: CCDC 2113155: Experimental Crystal Structure Determination

description: Related Article: Xinzhe Shi, Sihan Li, Melanie Reiß, Anke Spannenberg, Thorsten Holtrichter-Rößmann, Fabian Reiß, Torsten Beweries|2021|Chemical Science|12|16074|doi:10.1039/D1SC06052J
DOI: 10.5517/ccdc.csd.cc28xxbg
title: CCDC 2113156: Experimental Crystal Structure Determination

description: Related Article: Xinzhe Shi, Sihan Li, Melanie Reiß, Anke Spannenberg, Thorsten Holtrichter-Rößmann, Fabian Reiß, Torsten Beweries|2021|Chemical Science|12|16074|doi:10.1039/D1SC06052J
DOI: 10.5517/ccdc.csd.cc28xxch
title: CCDC 2113157: Experimental Crystal Structure Determination

description: Related Article: Xinzhe Shi, Sihan Li, Melanie Reiß, Anke Spannenberg, Thorsten Holtrichter-Rößmann, Fabian Reiß, Torsten Beweries|2021|Chemical Science|12|16074|doi:10.1039/D1SC06052J
DOI: 10.25318/3210015901-fra
title: Serres et champignons, données chronologiques du Recensement de l'agriculture

description: Recensement de l’agriculture, 1981 à 2021. Superficie de serre utilisée et superficie de culture de champignons.
DOI: 10.25318/3210015901-eng
title: Greenhouses and mushrooms, Census of Agriculture historical data

description: Census of Agriculture, 1981 to 2021. Greenhouse area in use and mushroom growing area.
DOI: 10.25318/3210016101-eng
title: Maple taps, Census of Agriculture historical data

description: Census of Agriculture, 1981 to 2021. Maple taps.
DOI: 10.25318/3210016101-fra
title: Entailles d'érable, données chronologiques du Recensement de l'agriculture

description: Recensement de l’agriculture, 1981 à 2021. Entailles d'érable.
DOI: 10.25422/azu.data.16823602
title: Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi-arid grassland

description: This dataset supports the associated publication "Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi-arid grassland" https://doi.org/10.1111/1365-2435.13980. <br>It includes 1) weekly plot-level flux, 2) <i>Digitaria californica </i>reproductive culm, 3) community structure for five blocks (H1-H5) and four precipitation treatments (S1-S4). Each block consists of a rainout shelter (itself consisting of four randomized plots). Precipitation treatments consist of3.5 days dry interval (S1)7 days dry interval (S2)14 days dry interval (S3)21 days dry interval (S4)<br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.25422/azu.data.16823602.v1
title: Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi-arid grassland

description: This dataset supports the associated publication "Precipitation temporal repackaging into fewer, larger storms delayed seasonal timing of peak photosynthesis in a semi-arid grassland" https://doi.org/10.1111/1365-2435.13980. <br>It includes 1) weekly plot-level flux, 2) <i>Digitaria californica </i>reproductive culm, 3) community structure for five blocks (H1-H5) and four precipitation treatments (S1-S4). Each block consists of a rainout shelter (itself consisting of four randomized plots). Precipitation treatments consist of3.5 days dry interval (S1)7 days dry interval (S2)14 days dry interval (S3)21 days dry interval (S4)<br><br><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i>
DOI: 10.25318/3210016201-fra
title: Produits appliqués sur les terres et pratiques de travail du sol et d'ensemencement, données chronologiques du Recensement de l'agriculture

description: Recensement de l’agriculture, 1981 à 2021. Certains produits appliqués sur les terres et pratiques de travail du sol et d'ensemencement utilisées sur les terres ensemencées ou à ensemencer.
DOI: 10.25318/3210016401-eng
title: Farm capital, operating revenues and operating expenses, Census of Agriculture historical data

description: Census of Agriculture, 1976 to 2021. Farm capital, operating revenues, forest product sale and operating expenses.
DOI: 10.5066/p94a4pyw
title: Large-Scale Marsh Creation Project - Upper Barataria Component (BA-0207): 2018 land-water classification

description: The Louisiana Coastal Protection and Restoration Authority and the Natural Resource Damage Assessment (NRDA) develop restoration plans in order to conserve, restore, create and enhance Louisiana's coastal wetlands. The wetland restoration plans developed specifically require an evaluation of the effectiveness of each coastal wetlands restoration project in achieving long-term solutions to arresting coastal wetlands loss. This data set includes mosaicked aerial photographs for the Large-Scale Marsh Creation Project - Upper Barataria Component Restoration (BA-0207) project for 2018. This data is used as a basemap land-water classification. It also serves as a visual tool for project managers to help them identify any obvious problems or land loss within their project boundary. To better evaluate the effectiveness of restoration efforts, a land-water classification is performed on specific restoration sites to help assess landscape changes.
DOI: 10.25318/3210016401-fra
title: Capital agricole, revenus d'exploitation et dépenses d'exploitation, données chronologiques du Recensement de l'agriculture

description: Recensement de l’agriculture, 1976 à 2021. Capital agricole, revenus d'exploitation et dépenses d'exploitation.
DOI: 10.25318/3210016601-eng
title: Farms classified by farm type, Census of Agriculture historical data

description: Census of Agriculture, 2001 to 2021. Farms classified by farm type.
DOI: 10.6084/m9.figshare.17197262
title: Collaborations by world bank economic classification

description: Data describing collaboration of world bank economic classification.
DOI: 10.6084/m9.figshare.17197262.v1
title: Collaborations by world bank economic classification

description: Data describing collaboration of world bank economic classification.
DOI: 10.5281/zenodo.5784143
title: Spontaneous parthenogenesis in the parasitoid wasp Cotesia typhae: low frequency anomaly or evolving process?

description: Raw data linked to the manuscript, including phenotyping and genotyping results for all Cotesia typhae females analyzed in this study.
DOI: 10.5281/zenodo.5774521
title: KiSSim: Predicting off-targets from structural similarities in the kinome

description: <strong>KiSSim: Predicting off-targets from structural similarities in the kinome</strong> <strong>Project description.</strong> KiSSim (Kinase Structural Similarity) is a novel fingerprint designed specifically for kinase pockets, allowing for similarity studies across the structurally covered kinome. The kinase fingerprint is based on the KLIFS pocket alignment, which defines 85 pocket residues for all kinase structures. This enables a residue-by-residue comparison without a computationally expensive alignment step. The pocket fingerprint encodes each pocket residue’s spatial and physicochemical properties. The spatial properties describe the residue’s position in relation to the kinase pocket center and important kinase subpockets, i.e. the hinge region, the DFG region, and the front pocket. The physicochemical properties encompass for each residue its size and pharmacophoric features, solvent exposure, and side chain orientation. Some datasets are not part of the `kissim_app` GitHub repository due to their size but can be downloaded from here to the respective kissim_app folders. <strong>Data.</strong> `20210902_KLIFS_HUMAN.tar.gz` --- save in `kissim_app/data/external/structures` `complete_SiteAlign.txt.gz` --- save in `kissim_app/data/external/sitealign` <strong>Results.</strong> `results.tar.bz2`--- save as `kissim_app/results` These are the KiSSim results: fingerprints, feature/fingerprint distances, kinase matrices, and kinase trees for structures in all (`all`), DFG-in (`dfg_in`), and DFG-out (`dfg_out`) conformation. In the case of the DFG-in conformation, we also have KiSSim runs with fingerprint subsets based on only residues that interact with certain ligands in KLIFS IFPs: Erlotinib (`dfg_in_IRE`), Imatinib (`dfg_in_STI`), Bosutinib (`dfg_in_DB8`), and Dopamapimod (`dfg_in_B96`). The folder contains README with a detailed file list. <strong>Usage.</strong> This dataset can be used to run the notebooks available on https://github.com/volkamerlab/kissim_app. Clone the kissim_app repository. Download the files provided here. If applicable, extract the archive content to the folders as indicated above and run the notebooks. <pre><code class="language-bash">cd /path/to/your/download tar -xvf results.tar.bz2 -C /path/to/kissim_app/ tar -xvf 20210902_KLIFS_HUMAN.tar.bz2 -C /path/to/kissim_app/data/external/structures/ # In case you want the raw SiteAlign data mv complete_SiteAlign.txt.gz /path/to/kissim_app/data/external/sitealign</code></pre> <strong>Citation.</strong> These datasets are part of the KiSSim publication: TBA