cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15086112
title: A Structural Geometry Dataset for Proteins: Distance Maps and Dihedral Angles for Computational Applications

description: This dataset provides a comprehensive collection of geometric features for 6,464 single-chain proteins, including precomputed distance maps and dihedral angles. The dataset was curated from the SCOPe 2.08 database, focusing on proteins with chain lengths between 25 to 700 residues, ensuring diversity while avoiding redundancy.

Key Features:



Distance Maps: The dataset includes pairwise Cβ-Cβ (or Cα for Glycine) distance matrices, which are essential for understanding protein tertiary structure, interactions, and folding. The maps are generated using the mean squared error (MSE) metric, providing a 2D representation of inter-residue distances.

Dihedral Angles: Backbone torsion angles (ϕ, ψ, and ω) are calculated based on atomic positions of Cα, N, and C atoms. These angles are vital for describing protein conformation, secondary structure (e.g., alpha-helices and beta-sheets), and stability.

Handling of Missing Residues: The dataset addresses missing residues from PDB files through interpolation for small gaps (1-2 residues) and masking for larger gaps, ensuring accurate structural data.

File Format: The geometric data is stored in compressed .npz files, which include the distance matrix, dihedral angle vectors, sequence indices, resolution classification, and a mask for missing residues.


Applications:

This dataset is intended for a range of structural bioinformatics applications, including:



Protein Structure Prediction: Distance maps and dihedral angles can serve as inputs for machine learning models that predict protein structures from sequences.

Molecular Dynamics Simulations: The dataset provides geometric constraints that can enhance molecular simulations and improve conformational sampling.

Structural Analysis: The dataset facilitates comparative studies of protein families, structural motifs, and evolutionary relationships.

Drug Discovery: The geometric features can aid in ligand binding and protein-ligand docking studies, which are crucial for structure-based drug design.


By making this dataset publicly available, we aim to support advancements in protein structure prediction, molecular simulations, and AI-driven bioinformatics research.
DOI: 10.34740/kaggle/dsv/11261049
title: e-IPO Indonesia Data - Investment
DOI: 10.21410/7e4/rzx0nb
title: Enquête électorale française 2019 (ENEF 2019), Vague 19

description: Ce dispositif de suivi et de mesure de l’opinion permet l’analyse des évolutions de l’électorat français et de ses réactions aux événements politiques, sociaux et économiques à travers 5 vagues, entre la veille des élections européennes de 2019 et celle des élections municipales de 2020. Il fournit une série d’indicateurs et de mesures sur les tendances de l’électorat français, ses valeurs politiques, ses préférences partisanes et son rapport à l'Union européenne. L'enquête détaille les ressorts psychologiques de la décision électorale à la lumière des valeurs des Français et des contextes politiques, géographiques et socio-économiques. Elle permet également des analyses détaillées sur les opinions des français envers Emmanuel Macron ainsi que les Gilets Jaunes et le Grand débat national de janvier 2019. L'enquête est en continuum de l'Enquête électorale française 2017 qui a repris une partie de ses enquêtés. Cette dix-neuvième vague a été réalisée en février 2019, avant les élections européennes de mai 2019 ; 10 002 personnes inscrites sur les listes électorales y ont participé.
DOI: 10.21410/7e4/rzx0nb/2z89ot
title: fr_cdsp_ddi_enef2019_v19.xml

description: :unav
DOI: 10.21410/7e4/rzx0nb/c3xxsy
title: Documentation_ENEF2019_V19.pdf

description: :unav
DOI: 10.21410/7e4/rzx0nb/ekdw8i
title: fr_cdsp_ddi_enef2019_v19_csv_2.7z

description: :unav
DOI: 10.21410/7e4/rzx0nb/etjf8k
title: Questionnaire_ENEF2019_Vague 19.pdf

description: :unav
DOI: 10.21410/7e4/rzx0nb/keoj4e
title: fr_cdsp_ddi_enef2019_v19.tab

description: :unav
DOI: 10.21410/7e4/rzx0nb/ujsh88
title: ENEF2019_Dictionnaire_variable_2.7z

description: :unav
DOI: 10.21410/7e4/rzx0nb/zbh5pm
title: fr_cdsp_ddi_enef2019_v19_sas_2.7z

description: :unav
DOI: 10.21410/7e4/rzx0nb/zctorl
title: fr_cdsp_ddi_enef2019_v19-1.tab

description: :unav
DOI: 10.15468/dl.qupacy
title: Occurrence Download

description: A dataset containing 201433 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "Geometry POLYGON((113.13651 12.57616,99.74552 13.97968,89.85679 12.17371,85.53047 -4.00559,101.39364 -10.54136,114.99064 -14.3628,129.82374 -14.96072,139.30044 -13.5631,146.51097 -6.46726,145.68691 1.55337,141.97864 9.74681,113.13651 12.57616))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Year 2000-2010"
 ]
}

 The dataset includes 201433 records from 362 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001752-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15129852
title: Supporting Information for physics-constrained neuroforecasting of time-dependent local scour depths at bridges under unsteady flows
DOI: 10.5281/zenodo.15129823
title: Diretores escolares, pandemia da Covid-19 e exclusão digital

description: Trata-se dos dados qualitativos utilizados na pesquisa "Diretores escolares, pandemia da Covid-19 e exclusão digital", conforme processados pelo programa Iramuteq.

Em atenção dos preceitos éticos, os dados estão codificados em anonimato. Outrossim, consta a identificação dos segmentos de textos aproveitados nas classes da classificação hierárquica descente processados no Iramuteq
DOI: 10.5281/zenodo.15129824
title: Diretores escolares, pandemia da Covid-19 e exclusão digital

description: Trata-se dos dados qualitativos utilizados na pesquisa "Diretores escolares, pandemia da Covid-19 e exclusão digital", conforme processados pelo programa Iramuteq.

Em atenção dos preceitos éticos, os dados estão codificados em anonimato. Outrossim, consta a identificação dos segmentos de textos aproveitados nas classes da classificação hierárquica descente processados no Iramuteq
DOI: 10.5281/zenodo.14823916
title: Maximum Flood Depth Survey Data for Hong Kong MTR Stations Area (September 7-8, 2023)

description: This dataset contains flood depth measurements collected after a severe flooding event in Hong Kong during September 7-8, 2023. The survey was conducted around MTR stations by measuring flood marks on walls and other structures, complemented by interviews with local residents in the affected areas. Data includes location coordinates (longitude and latitude) and maximum water depth measurements in meters.
DOI: 10.5281/zenodo.14823917
title: Maximum Flood Depth Survey Data for Hong Kong MTR Stations Area (September 7-8, 2023)

description: This dataset contains flood depth measurements collected after a severe flooding event in Hong Kong during September 7-8, 2023. The survey was conducted around MTR stations by measuring flood marks on walls and other structures, complemented by interviews with local residents in the affected areas. Data includes location coordinates (longitude and latitude) and maximum water depth measurements in meters.
DOI: 10.5281/zenodo.8239053
title: Live Iterative Ptychography

description: This deposition contains data and code for live-updating ptychographic reconstruction with ePIE, an iterative ptychography method, during ongoing data acquisition. See upcoming paper, a reference will be updated in due time.
description: Parts of this research were carried out at beamline P06 of PETRA III at Deutsches Elektronen-Synchrotron DESY, a member of the Helmholtz Association (HGF). We acknowledge support from Helmholtz Association under contract No. ZT-I-0025 (Ptychography 4.0) and JL-MDMC (Joint Lab on Model and Data-Driven Material Characterization). This research was supported in part through the Maxwell computational resources operated at Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany. This work was partly funded by the Center for Advanced Systems Understanding (CASUS) which is financed by Germany's Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture, and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.
DOI: 10.25345/c5k35ms2f
title: MassIVE MSV000097502 - coIP proteome of KERP2-related Entamoeba histolytica transformants
title: Entamoeba histolytica, the protozoan parasite responsible for amoebiasis, employs a diverse arsenal of virulence factors to facilitate infection and evade host immune responses. In this study, we identify KERP2 as a dual-function effector involved in both parasite homeostasis and host cell modulation. To investigate its subcellular interactions, E. histolytica trophozoites expressing HA-tagged full-length KERP2 or the truncated variant HA-KERP2(1-184) were fractionated into nuclear and cytosolic components, followed by co-immunoprecipitation analysis.
DOI: 10.5281/zenodo.10390322
title: Live Iterative Ptychography

description: This deposition contains data and code for live-updating ptychographic reconstruction with ePIE, an iterative ptychography method, during ongoing data acquisition.

Corresponding paper: https://doi.org/10.1093/mam/ozae004 and https://doi.org/10.48550/arXiv.2308.10674
description: Parts of this research were carried out at beamline P06 of PETRA III at Deutsches Elektronen-Synchrotron DESY, a member of the Helmholtz Association (HGF).

We acknowledge support from Helmholtz Association under contract No. ZT-I-0025 (Ptychography 4.0) and  JL-MDMC (Joint Lab on Model and Data-Driven Material Characterization).

This research was supported in part through the Maxwell computational resources operated at Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany.

This work was partly funded by the Center for Advanced Systems Understanding (CASUS) which is financed by Germany's Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture, and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.
DOI: 10.5281/zenodo.8239052
title: Live Iterative Ptychography

description: This deposition contains data and code for live-updating ptychographic reconstruction with ePIE, an iterative ptychography method, during ongoing data acquisition.

Corresponding paper: https://doi.org/10.1093/mam/ozae004 and https://doi.org/10.48550/arXiv.2308.10674
description: Parts of this research were carried out at beamline P06 of PETRA III at Deutsches Elektronen-Synchrotron DESY, a member of the Helmholtz Association (HGF).

We acknowledge support from Helmholtz Association under contract No. ZT-I-0025 (Ptychography 4.0) and  JL-MDMC (Joint Lab on Model and Data-Driven Material Characterization).

This research was supported in part through the Maxwell computational resources operated at Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany.

This work was partly funded by the Center for Advanced Systems Understanding (CASUS) which is financed by Germany's Federal Ministry of Education and Research (BMBF) and by the Saxon Ministry for Science, Culture, and Tourism (SMWK) with tax funds on the basis of the budget approved by the Saxon State Parliament.
DOI: 10.5281/zenodo.10550991
title: DRLab Household Data

description: During the project ANIMATION, measurements from household appliances were gathered using the Schneider Electric PowerTag system at a high frequency (around two seconds). The data has been collected since April 2022 and is organized in CSV files by month.

Data available on GitLab.
DOI: 10.5281/zenodo.10550992
title: DRLab Household Data

description: During the project ANIMATION, measurements from household appliances were gathered using the Schneider Electric PowerTag system at a high frequency (around two seconds). The data has been collected since April 2022 and is organized in CSV files by month.

Data available on GitLab.
DOI: 10.5281/zenodo.15125400
title: DRLab Household Data

description: During the project ANIMATION, measurements from household appliances were gathered using the Schneider Electric PowerTag system at a high frequency (around two seconds). The data has been collected since April 2022 and is organized in CSV files by month.

Data available on GitLab.
DOI: 10.5281/zenodo.15129873
title: Global Burial Insurance Market 2025 To 2034

description: Burial Insurance Market Size, Trends and Insights By Coverage (Level Death Benefit, Guaranteed Acceptance, Modified or Graded Death Benefit), By End Use (Over 50, Over 60, Over 70, Over 80), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

Global Burial Insurance Market size was valued at USD 313.5 Billion in 2025 and the total Automotive Engine Cover Market revenue is expected to grow at a CAGR of 5.94% over the forecast period, reaching nearly USD 458 Billion by 2034.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=68446
DOI: 10.5281/zenodo.15129874
title: Global Burial Insurance Market 2025 To 2034

description: Burial Insurance Market Size, Trends and Insights By Coverage (Level Death Benefit, Guaranteed Acceptance, Modified or Graded Death Benefit), By End Use (Over 50, Over 60, Over 70, Over 80), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025 – 2034.

Reports Description

Global Burial Insurance Market size was valued at USD 313.5 Billion in 2025 and the total Automotive Engine Cover Market revenue is expected to grow at a CAGR of 5.94% over the forecast period, reaching nearly USD 458 Billion by 2034.

For more information, DOWNLOAD FREE SAMPLE Now at https://www.custommarketinsights.com/request-for-free-sample/?reportid=68446
DOI: 10.5281/zenodo.15088427
title: BSTT_CT: A CT Imaging Dataset of Lung Nodule Metastases from Bone and Soft Tissue Tumors

description:  This dataset exclusively comprises CT scan images of patients with BSTTs lung metastases. The dataset includes 1,098 annotated nodules from 74 patients admitted between 2010 and 2024 (44 males and 30 females; age range: 11-79 years; mean age: 32 years.
DOI: 10.5281/zenodo.15088428
title: BSTT_CT: A CT Imaging Dataset of Lung Nodule Metastases from Bone and Soft Tissue Tumors

description:  This dataset exclusively comprises CT scan images of patients with BSTTs lung metastases. The dataset includes 1,098 annotated nodules from 74 patients admitted between 2010 and 2024 (44 males and 30 females; age range: 11-79 years; mean age: 32 years.
DOI: 10.15468/dl.u2ggd5
title: Occurrence Download

description: A dataset containing 511 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Habenaria zambesina Rchb.f.",
 "Continent is Africa"
 ]
}

 The dataset includes 511 records from 36 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001800-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tyr5au
title: Occurrence Download

description: A dataset containing 257038 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Opuntia Mill.",
 "HasCoordinate is true"
 ]
}

 The dataset includes 257038 records from 843 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001792-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kvupj7
title: Occurrence Download

description: A dataset containing 16683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "ScientificName is Bupleurum fruticosum L.",
 "Continent is Europe",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 16683 records from 142 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001801-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21410/7e4/4x08l3/2bf3at
title: Questionnaire_ENEF2019_Vague 20.pdf

description: :unav
DOI: 10.21410/7e4/4x08l3
title: Enquête électorale française 2019 (ENEF 2019), Vague 20

description: Ce dispositif de suivi et de mesure de l’opinion permet l’analyse des évolutions de l’électorat français et de ses réactions aux événements politiques, sociaux et économiques à travers 5 vagues, entre la veille des élections européennes de 2019 et celle des élections municipales de 2020. Il fournit une série d’indicateurs et de mesures sur les tendances de l’électorat français, ses valeurs politiques, ses préférences partisanes et son rapport à l'Union européenne. L'enquête détaille les ressorts psychologiques de la décision électorale à la lumière des valeurs des Français et des contextes politiques, géographiques et socio-économiques. Elle permet également des analyses détaillées sur les opinions des français envers Emmanuel Macron ainsi que les Gilets Jaunes et le Grand débat national de janvier 2019. L'enquête est en continuum de l'Enquête électorale française 2017 qui a repris une partie de ses enquêtés. Cette vingtième vague a été réalisée en mars 2019, avant les élections européennes de mai 2019 ; 10 049 personnes inscrites sur les listes électorales y ont participé.
DOI: 10.21410/7e4/4x08l3/gvlxrk
title: ENEF2019_Dictionnaire_variable_2.7z

description: :unav
DOI: 10.21410/7e4/4x08l3/jvbyfs
title: fr_cdsp_ddi_enef2019_v20.tab

description: :unav
DOI: 10.21410/7e4/4x08l3/jxigyf
title: fr_cdsp_ddi_enef2019_v20-1.tab

description: :unav
DOI: 10.21410/7e4/4x08l3/sxabfw
title: fr_cdsp_ddi_enef2019_v20.xml

description: :unav
DOI: 10.21410/7e4/4x08l3/u3f4ki
title: fr_cdsp_ddi_enef2019_v20_sas_2.7z

description: :unav
DOI: 10.21410/7e4/4x08l3/uq7iyl
title: Documentation_ENEF2019_V20.pdf

description: :unav
DOI: 10.21410/7e4/4x08l3/ygteh1
title: fr_cdsp_ddi_enef2019_v20_csv.7z

description: :unav
DOI: 10.57745/2qp9ha
title: ReadMe_Covering_Power.txt

description: :unav
DOI: 10.57745/44hcn1
title: Macro_Mask_ROI.ijm

description: This macro allows you to obtain the binary images of each ROI image
DOI: 10.57745/erlxj5
title: Macro_Crop_ROI.ijm

description: This macro allows you to cut out a region of interest using 4 markers (blue circles) located in the image.
DOI: 10.57745/g8c9gj
title: Macro_Split_Genotypes.ijm

description: This macro allows you to split the ROI images into four parts, each corresponding to a different variety.
DOI: 10.57745/gqkfds
title: Macro_Covering_power.ijm

description: This macro allows you to calculate the percentage of plant coverage in each image.
DOI: 10.57745/ltmzqb
title: Original_Images.zip

description: Original images of the 3 repetitions
DOI: 10.57745/sjhmkw
title: Crop_Masks.zip

description: Binary images to separate plants from the rest of the image.
DOI: 10.57745/zgm8d9
title: Crop_ROI.zip

description: Cropped images of the ROI.
DOI: 10.15468/dl.4kbvze
title: Occurrence Download

description: A dataset containing 2726 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-12.49324 49.30413,4.3038 49.30413,4.3038 61.08619,-12.49324 61.08619,-12.49324 49.30413))",
 "HasGeospatialIssue is false",
 "TaxonKey is Paulownia tomentosa (Thunb.) Steud."
 ]
}

 The dataset includes 2726 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001804-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dtv69f
title: Occurrence Download

description: A dataset containing 69 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-8.55581 36.55255,-0.49274 36.01502,4.07633 43.36136,-11.87062 44.25726,-8.55581 36.55255))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Argemone mexicana L."
 ]
}

 The dataset includes 69 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001807-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.78nrmv
title: Occurrence Download

description: A dataset containing 34 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingOrg is 28eb1a3f-1c15-4a95-931a-4af90ecb574d",
 "TaxonKey is Romulea barkerae M.P.de Vos"
 ]
}

 The dataset includes 34 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001803-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15129895
title: Instances for the Clustered Vehicle Routing Problem

description: These are the banchmark test instances used in the following article: Defryn, C., & Sörensen, K. (2017). A fast two-level variable neighborhood search for the clustered vehicle routing problem. Computers & Operations Research, 83, 78-94.
DOI: 10.5281/zenodo.15129896
title: Instances for the Clustered Vehicle Routing Problem

description: These are the banchmark test instances used in the following article: Defryn, C., & Sörensen, K. (2017). A fast two-level variable neighborhood search for the clustered vehicle routing problem. Computers & Operations Research, 83, 78-94.