cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.1285/i20356609v15i3p1008
title: Gary Alan Fine, The Hinge. Civil Society, Group Cultures and the Power of Local Commitments. Chicago: Chicago University Press, 2021.
DOI: 10.1285/i20356609v15i3p1019
title: Nicolas Duvoux and Nadége Vezinat (eds) (2022). La santé sociale, Paris: Puf.
DOI: 10.5281/zenodo.7529260
title: Western Florida Panhandle Electric Transmission Grid Substations, Lines, and Towers

description: The upload consists of 5 different datasets pertaining to the electric transmission grid in the nine counties of the western Florida Panhandle. The area largely coincides with the former operation area of the Gulf Power Company (GPCO) but is not limited to this utility. The five datasets describe the substations, lines, and transmission towers of the grid. The data were created and validated through a variety of datasets from the utility, national data, and state-level information. In total, 195 substations, 1800 miles of transmission lines, and over 18,000 transmission towers were cataloged and described spatially in the data. The spatial files are uploaded as feature classes within a ArcGIS geodatabase. Three metadata files are provided, one each for substations, transmission lines, and transmission towers, which describe the process and sources for creating each data as well as a detailed list of all fields in the files.
DOI: 10.5281/zenodo.7568094
title: S669

description: A dataset of protein variants with annotated with their experimental DDG values, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. It includes the protein structures (both wild-type and mutated) for the variants. Please refer to the original publication: Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli, Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset, <em>Briefings in Bioinformatics</em>, Volume 23, Issue 2, March 2022, bbab555, https://doi.org/10.1093/bib/bbab555
description: {"references": ["Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli, Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset,\u00a0Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbab555,\u00a0https://doi.org/10.1093/bib/bbab555"]}
DOI: 10.5281/zenodo.7568093
title: S669

description: A dataset of protein variants with annotated with their experimental DDG values, collected and manually cleaned from the latest version of the ThermoMutDB database, consisting of 669 variants not included in the most widely used training datasets. It includes the protein structures (both wild-type and mutated) for the variants. Please refer to the original publication: Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli, Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset, <em>Briefings in Bioinformatics</em>, Volume 23, Issue 2, March 2022, bbab555, https://doi.org/10.1093/bib/bbab555
description: {"references": ["Corrado Pancotti, Silvia Benevenuta, Giovanni Birolo, Virginia Alberini, Valeria Repetto, Tiziana Sanavia, Emidio Capriotti, Piero Fariselli, Predicting protein stability changes upon single-point mutation: a thorough comparison of the available tools on a new dataset,\u00a0Briefings in Bioinformatics, Volume 23, Issue 2, March 2022, bbab555,\u00a0https://doi.org/10.1093/bib/bbab555"]}
DOI: 10.34740/kaggle/dsv/4896327
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.7568237
title: Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity

description: Malavazos AE, Secchi F, Basilico S, Capitanio G, Boveri S, Milani V, Dubini C, Schiaffino S, Morricone L, Foschini C, Gobbo G, Piccinni R, Saibene A, Sardanelli F, Menicanti L, Guazzi M, Dong C, Romanelli MMC, Carruba M, Iacobellis G. Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity. Eat Weight Disord. 2022 Feb;27(1):345-359. doi: 10.1007/s40519-021-01173-w. Epub 2021 Apr 5. PMID: 33821453; PMCID: PMC8020829. Abstract <strong>Purpose: </strong>Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored. <strong>Methods: </strong>This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19. <strong>Results: </strong>Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P &lt; 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P &lt; 0.001; r = 0.41, P &lt; 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P &lt; 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P &lt; 0.001) as the only independent factors associated with high CXR severity scores. <strong>Conclusion: </strong>Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.
DOI: 10.5281/zenodo.7568236
title: Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity

description: Malavazos AE, Secchi F, Basilico S, Capitanio G, Boveri S, Milani V, Dubini C, Schiaffino S, Morricone L, Foschini C, Gobbo G, Piccinni R, Saibene A, Sardanelli F, Menicanti L, Guazzi M, Dong C, Romanelli MMC, Carruba M, Iacobellis G. Abdominal obesity phenotype is associated with COVID-19 chest X-ray severity score better than BMI-based obesity. Eat Weight Disord. 2022 Feb;27(1):345-359. doi: 10.1007/s40519-021-01173-w. Epub 2021 Apr 5. PMID: 33821453; PMCID: PMC8020829. Abstract <strong>Purpose: </strong>Chest X-ray (CXR) severity score and BMI-based obesity are predictive risk factors for COVID-19 hospital admission. However, the relationship between abdominal obesity and CXR severity score has not yet been fully explored. <strong>Methods: </strong>This retrospective cohort study analyzed the association of different adiposity indexes, including waist circumference and body mass index (BMI), with CXR severity score in 215 hospitalized patients with COVID-19. <strong>Results: </strong>Patients with abdominal obesity showed significantly higher CXR severity scores and had higher rates of CXR severity scores ≥ 8 compared to those without abdominal obesity (P &lt; 0.001; P = 0.001, respectively). By contrast, patients with normal weight, with overweight and those with BMI-based obesity showed no significant differences in either CXR severity scores or in the rates of CXR severity scores ≥ 8 (P = 0.104; P = 0.271, respectively). Waist circumference and waist-to-height ratio (WHtR) correlated more closely with CXR severity scores than BMI (r = 0.43, P &lt; 0.001; r = 0.41, P &lt; 0.001; r = 0.17, P = 0.012, respectively). The area under the curves (AUCs) for waist circumference and WHtR were significantly higher than that for BMI in identifying a high CXR severity score (≥ 8) (0.68 [0.60-0.75] and 0.67 [0.60-0.74] vs 0.58 [0.51-0.66], P = 0.001). A multivariate analysis indicated abdominal obesity (risk ratio: 1.75, 95% CI: 1.25-2.45, P &lt; 0.001), bronchial asthma (risk ratio: 1.73, 95% CI: 1.07-2.81, P = 0.026) and oxygen saturation at admission (risk ratio: 0.96, 95% CI: 0.94-0.97, P &lt; 0.001) as the only independent factors associated with high CXR severity scores. <strong>Conclusion: </strong>Abdominal obesity phenotype is associated with a high CXR severity score better than BMI-based obesity in hospitalized patients with COVID-19. Therefore, when visiting the patient in a hospital setting, waist circumference should be measured, and patients with abdominal obesity should be monitored closely. Level of evidence Cross-sectional descriptive study, Level V.
DOI: 10.5281/zenodo.7568158
title: Companion dataset for paper "Immersive Learning Environments for Self-Regulation of Learning: A Literature Review"

description: This dataset is a companion of extra materials for the paper "Immersive Learning Environments for Self-Regulation of Learning: A Literature Review" which focuses on a literature review about pedagogical uses, practices and strategies with Immersive Learning Environments (ILE) that have an explicit focus on Self-regulation of Learning (SRL). This dataset is an file containing information about: (1) list of papers forming the corpus of the literature review; (2) Criteria for associating accounts of SRL with ILE in relation to the Beck et al. (2020) Framework; and (3) Graphs with Overviews of studies about ILE for SRL. This dataset is hosted on zenodo, a data repository.
DOI: 10.5281/zenodo.7568157
title: Companion dataset for paper "Immersive Learning Environments for Self-Regulation of Learning: A Literature Review"

description: This dataset is a companion of extra materials for the paper "Immersive Learning Environments for Self-Regulation of Learning: A Literature Review" which focuses on a literature review about pedagogical uses, practices and strategies with Immersive Learning Environments (ILE) that have an explicit focus on Self-regulation of Learning (SRL). This dataset is an file containing information about: (1) list of papers forming the corpus of the literature review; (2) Criteria for associating accounts of SRL with ILE in relation to the Beck et al. (2020) Framework; and (3) Graphs with Overviews of studies about ILE for SRL. This dataset is hosted on zenodo, a data repository.
DOI: 10.6084/m9.figshare.17126507.v1
title: Bizic_2021_List_of_representative_species_Major_Phytoplankton_grops.pdf

description: List of phytoplankton species available in culture to be studied as representatives of major phytoplankton phylogenetic groups.
DOI: 10.15129/2db504b8-3736-4ba0-9829-b7cc0c5db38a
title: Source code for: "Reconstruction and Validation of Arterial Geometries from 4D Flow-MRI Images for CFD: A Novel Approach"

description: Purpose: The purpose of this code is to combine several DICOM stacks on a slice-by-slice basis to create a new dataset. Specifically, this script was used to combine 3 DICOM stacks of human aortae which were created from 4D Flow-MRI data at discrete time steps (systolic acceleration, peak systole, systolic deceleration). The resultant dataset is therefore a temporal composite which combines each of these time-steps. This code can be altered to combine any number of time steps. Matlab version 2020b
DOI: 10.5281/zenodo.7568260
title: Analysis MS, NMR omimAl(NTf2)4

description: Analysis (MS and NMR) of synthesised ionic liquids based on omim cation and Al(NTf2)3 anion.
DOI: 10.5281/zenodo.7568259
title: Analysis MS, NMR omimAl(NTf2)4

description: Analysis (MS and NMR) of synthesised ionic liquids based on omim cation and Al(NTf2)3 anion.
DOI: 10.6084/m9.figshare.17126507.v2
title: Bizic_2023_List_of_representative_species_Major_Phytoplankton_groups.pdf

description: List of phytoplankton species available in culture to be studied as representatives of major phytoplankton phylogenetic groups.
DOI: 10.6084/m9.figshare.21953267
title: Papers included

description: This data set contains a list of research articles that were included in a conducted literature research on the green seaweed <em>Caulerpa lentillifera</em>.
DOI: 10.6084/m9.figshare.21953267.v1
title: Papers included

description: This data set contains a list of research articles that were included in a conducted literature research on the green seaweed <em>Caulerpa lentillifera</em>.
DOI: 10.6084/m9.figshare.21953276
title: Papers excluded

description: This data set contains a list of research publications that were excluded from a conducted literature review on the green seaweed <em>Caulerpa lentillifera</em>.
DOI: 10.6084/m9.figshare.21953276.v1
title: Papers excluded

description: This data set contains a list of research publications that were excluded from a conducted literature review on the green seaweed <em>Caulerpa lentillifera</em>.
DOI: 10.6084/m9.figshare.21372000
title: The best model PTH files from the study: "Impact evaluation of score classes and annotation regions in deep learning-based dairy cow body condition prediction"

description: Body condition scoring is a simple method to estimate the energy supply of dairy cattle. We investigated how computer vision-based supervised deep learning, specifically neural networks, can automate body condition scoring. To execute this, we have used video recordings of the rumps of a large number of animals. We have trained and tested various convolutional neural networks with this collected data. The weights of our trained CNNs are freely available, supporting similar works.
DOI: 10.6084/m9.figshare.21953345
title: Napieretal_RawData.xlsx

description: Raw data for Napier et al. 2023: Evaluating the cues that coordinate a shift towards the robbing foraging tactic in the honey bee (Apis mellifera) <br>
DOI: 10.6084/m9.figshare.21953345.v1
title: Napieretal_RawData.xlsx

description: Raw data for Napier et al. 2023: Evaluating the cues that coordinate a shift towards the robbing foraging tactic in the honey bee (Apis mellifera) <br>
DOI: 10.35078/nnj2cf
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Uberaba | Município: Serra do Salitre

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)
DOI: 10.5517/ccdc.csd.cc2dmnzp
title: CCDC 2223443: Experimental Crystal Structure Determination

description: Related Article: Appanapalli N. V. Satyanarayana, Nilanjana Mukherjee, Tanmay Chatterjee|2023|Green Chemistry|25|779|doi:10.1039/D2GC04101D
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.6084/m9.figshare.21953366
title: Supplementary Table 2.docx

description: <strong>Suplementary Table 2. Comparison of pre-COVID-19 classical SAT and COVID-19 period classical SAT in terms of hypothyroidism and recurrence</strong>
DOI: 10.6084/m9.figshare.21953366.v1
title: Supplementary Table 2.docx

description: <strong>Suplementary Table 2. Comparison of pre-COVID-19 classical SAT and COVID-19 period classical SAT in terms of hypothyroidism and recurrence</strong>
DOI: 10.6084/m9.figshare.21739616.v1
title: Thousand-seed weight data from Central Europe

description: The dataset contains measured thousand-seed weight data (g) for taxa of the Central European flora.
DOI: 10.5281/zenodo.7568049
title: Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.

description: An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018). The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature. The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here: &lt;product_type&gt;_&lt;mission&gt;_&lt;mode&gt;_&lt;product&gt;_&lt;polarisation&gt;_&lt;starttime&gt;_&lt;endtime&gt;_&lt;orbitnumber&gt;_&lt;dataID&gt;_&lt;image&gt;.fileextension For example: extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif
description: {"references": ["Miles, K.E., Willis, I.C., Benedek, C.L., Williamson, A.G. and Tedesco, M., 2017. Toward monitoring surface and subsurface lakes on the Greenland ice sheet using Sentinel-1 SAR and Landsat-8 OLI imagery. Frontiers in Earth Science, p.58."]}
DOI: 10.5281/zenodo.7568048
title: Supraglacial lakes derived from Sentinel-1 SAR imagery over the Watson basin on the Greenland Ice Sheet.

description: An experimental dataset produced for the 4D-Greenland project, one of the Polar+ projects funded by the European Space Agency. The dataset provides a classification of supraglacial lake extent, derived using Sentinel-1 SAR imagery, over the Watson case study site. The dataset is produced using a dynamic thresholding approach (Miles et al 2018). The dataset is produced for the period May 2017- Sept 2019. The temporal resolution of the dataset is approximately fortnightly (subject to methodological limitations) and is delivered as rasters in GeoTIFF format (epsg:3413). Raster pixels are denoted as: 0 where no surface water was detected; 1 where either HH or HV polarisation detected a backscatter signature representative of surface water; 2 where both HH and HV polarisations detected a backscatter signature representative of surface water; or 999 where the signal has been saturated and the output cannot distinguish if the signal is due to melt or other surface characteristics with the same backscattered signature. The naming convention indicates the original SAR tile used in the analysis and is identified by the sequence of fields described here: &lt;product_type&gt;_&lt;mission&gt;_&lt;mode&gt;_&lt;product&gt;_&lt;polarisation&gt;_&lt;starttime&gt;_&lt;endtime&gt;_&lt;orbitnumber&gt;_&lt;dataID&gt;_&lt;image&gt;.fileextension For example: extent_S1B_EW_GRDH_1SDH_20180811T202931_20180811T203031_012220_016839_916F.tif
description: {"references": ["Miles, K.E., Willis, I.C., Benedek, C.L., Williamson, A.G. and Tedesco, M., 2017. Toward monitoring surface and subsurface lakes on the Greenland ice sheet using Sentinel-1 SAR and Landsat-8 OLI imagery. Frontiers in Earth Science, p.58."]}
DOI: 10.5281/zenodo.7568396
title: Experimental results and the user questionnaires for KG profiling tool (ABSTAT vs Protégé) evaluation

description: Experimental results and the user questionnaires for KG profiling tool (ABSTAT vs Protégé) evaluation
DOI: 10.5281/zenodo.7568395
title: Experimental results and the user questionnaires for KG profiling tool (ABSTAT vs Protégé) evaluation

description: Experimental results and the user questionnaires for KG profiling tool (ABSTAT vs Protégé) evaluation
DOI: 10.5281/zenodo.2616892
title: Summary for Policymakers of the Assessment Report on Biodiversity and Ecosystem Services for Africa: Chapter 5 Systematic Review Database

description: Systematic review database: results of the classification and analysis of scientific papers and reports, published between 2005 and 2016, that deal with scenarios of biodiversity and/or ecosystem services in Africa.
DOI: 10.5281/zenodo.2616893
title: Summary for Policymakers of the Assessment Report on Biodiversity and Ecosystem Services for Africa: Chapter 5 Systematic Review Database

description: Systematic review database: results of the classification and analysis of scientific papers and reports, published between 2005 and 2016, that deal with scenarios of biodiversity and/or ecosystem services in Africa.
DOI: 10.34740/kaggle/dsv/4896488
title: 2021 World Population (updated daily)
DOI: 10.21227/ggp8-je65
title: Process task durations in a hospital billing process

description: The data refer to times of payment from a hospital billing (HB) data set. The source data were collected from a hospital in the Netherlands over three years provided by Felix Mannhardt, Massimiliano de Leoni, Hajo A. Reijers and Wil M. P. van der Aalst in the paper with the title "Data-Driven Process Discovery - Revealing Conditional Infrequent Behavior from Event Logs". Based on the original data, duration data in four tasks are extracted for the analysis of patient patterns from a time perspective. The four tasks are New-FIN (N--F), Change Diagnosis-FIN (CD--F), CodeOK-Billed (CO--B) and CodeNotOK-Billed (CN--B).
DOI: 10.5281/zenodo.7568428
title: DACOS

description: DACOS Dataset
DOI: 10.5281/zenodo.7568427
title: DACOS

description: DACOS Dataset
DOI: 10.35078/rqw1c2
title: Replication Data for: Ficha epidemiológica de localidade: Distrito: Uberaba | Município: São Gotardo

description: Levantamento Triatomínico com operações de campo ocorridas em Minas Gerais pela Superintendência de Campanhas de Saúde Pública (SUCAM) e FUNASA (Fundação Nacional de Saúde), no período de 1979 a 1999, objetivando a busca da presença de vetores da doença de Chagas ou de vestígios dessa presença. A partir dela são orientadas as operações com inseticida (pt) Triatomine survey with field operations carried out in Minas Gerais by the Public Health Campaigns Superintendence (SUCAM) and FUNASA (National Health Foundation), from 1979 to 1999, aiming to search for the presence of Chagas disease vectors or traces of that presence. From it, operations with insecticide are guided (en) Encuesta de triatominos con operaciones de campo realizada en Minas Gerais por la Superintendencia de Campañas de Salud Pública (SUCAM) y FUNASA (Fundación Nacional de Salud), de 1979 a 1999, con el objetivo de buscar la presencia de vectores de la enfermedad de Chagas o rastros de esa presencia. Desde ella se orientan las operaciones con insecticida (es)