cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4455198
title: Time Series from Smart Meters

description:  

This file contains technical problems that make it insuitable for public use. Please use this dataset instead.

 

 

 



Name: Time Series from Smart Meters

Summary: The dataset contains: (1) raw and cleaned time series of smart meters from Spanish electric cooperatives, and (2) feature values and metadata extracted from residential load profiles, both from Spanish electric cooperatives and publicly available datasets.

License: CC BY-NC-SA

Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.

Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein.

Collection Date: The time series on Spanish electric cooperatives were collected between 2014 and 2021. Data on feature values and metadata were extracted between 2020 and 2021.

Publication Date:

DOI: 10.5281/zenodo.4455198

Other repositories:

Author: University of Deusto

Objective of collection: This data is collected originally for invoice of the electrical consumption. In this project will be used to segment the households.

Description: The dataset contains a CSV file with one entry for both each load profile of the Spanish electric cooperatives and publicly available load profiles. The fields that can be found for each entry are (1) metadata such as the originating dataset to which they belong, the start and end dates, number of days that have been imputed and/or extended, provenance (country, administrative division, municipality, zip code, origin identifier (households, businesses, industry, etc. ), classification of socioeconomic conditions, and tariffs; and (2) extracted features, grouped by types such as statistical moments, quantiles, lag d-day autocorrelations, seasonal aggregates, peak and off-peak periods, load factors, energy consumed, features obtained using the R package "tsfeatures", and Catch-22 features. In addition, the dataset also contains a rData file for each entry of the Spanish electric cooperatives. These files contain the original time series (timestamped values), a field indicating whether the signal has been imputed and/or extended and another field indicating the date of the extension.

5 star: ⭐⭐⭐

Preprocessing steps: anonymization, data fusion, imputation of gaps, extension of time series shorter than 800 days, computation of features.

Reuse: NA

Update policy: The data will be updated throughout 2021.

Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a MD5 hash.

Technical aspects: Decompressed data is quite large (big data).

Other:
DOI: 10.5281/zenodo.14408832
title: Dataset Deliverable 4.1. Carbon Farming Mitigation Potential:  Evaluating the mitigation potential  (and uncertainties) of carbon farming  practices

description: Deliverable 4.1, identifies and evaluates the mitigation potential and uncertainties of carbon farming (CF) practices across five key agricultural systems: woody crops, peatlands, arable lands, grasslands and agroforestry. Its goal is to assess these systems' capacities for carbon sequestration, reduction of greenhouse gas (GHG) emissions, and broader environmental benefits. This work aligns with the aim of MARVIC to develop context-specific, harmonised Monitoring, Reporting, and Verification (MRV) systems for CF, supporting EU Mission Soil objectives and the Carbon Removal and Carbon Farming Certification Regulation Framework (CRCF). 

This document is intended for policymakers, researchers and agricultural stakeholders involved in carbon farming and land-use planning at regional, national, and European levels. Its relevance extends to environmental agencies, agricultural cooperatives, and research institutions focusing on climate change mitigation and sustainable agriculture.
DOI: 10.5281/zenodo.4455197
title: Time Series from Smart Meters

description:  

This file contains technical problems that make it insuitable for public use. Please use this dataset instead.

 

 

 



Name: Time Series from Smart Meters

Summary: The dataset contains: (1) raw and cleaned time series of smart meters from Spanish electric cooperatives, and (2) feature values and metadata extracted from residential load profiles, both from Spanish electric cooperatives and publicly available datasets.

License: CC BY-NC-SA

Acknowledge: These data have been collected in the framework of the WHY project. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 891943.

Disclaimer: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the Executive Agency for Small and Medium-sized Enterprises (EASME) or the European Commission (EC). EASME or the EC are not responsible for any use that may be made of the information contained therein.

Collection Date: The time series on Spanish electric cooperatives were collected between 2014 and 2021. Data on feature values and metadata were extracted between 2020 and 2021.

Publication Date:

DOI: 10.5281/zenodo.4455198

Other repositories:

Author: University of Deusto

Objective of collection: This data is collected originally for invoice of the electrical consumption. In this project will be used to segment the households.

Description: The dataset contains a CSV file with one entry for both each load profile of the Spanish electric cooperatives and publicly available load profiles. The fields that can be found for each entry are (1) metadata such as the originating dataset to which they belong, the start and end dates, number of days that have been imputed and/or extended, provenance (country, administrative division, municipality, zip code, origin identifier (households, businesses, industry, etc. ), classification of socioeconomic conditions, and tariffs; and (2) extracted features, grouped by types such as statistical moments, quantiles, lag d-day autocorrelations, seasonal aggregates, peak and off-peak periods, load factors, energy consumed, features obtained using the R package "tsfeatures", and Catch-22 features. In addition, the dataset also contains a rData file for each entry of the Spanish electric cooperatives. These files contain the original time series (timestamped values), a field indicating whether the signal has been imputed and/or extended and another field indicating the date of the extension.

5 star: ⭐⭐⭐

Preprocessing steps: anonymization, data fusion, imputation of gaps, extension of time series shorter than 800 days, computation of features.

Reuse: NA

Update policy: The data will be updated throughout 2021.

Ethics and legal aspects: Spanish electric cooperative data contains the CUPS (Meter Point Administration Number), which is personal data. A pre-processing step has been carried out to substitute the CUPS by a MD5 hash.

Technical aspects: Decompressed data is quite large (big data).

Other:
DOI: 10.5281/zenodo.13889406
title: Developing Digital Image Processing methods to quantify internal and interfacial convection in the ice-ocean boundary layer during sea ice desalination

description: This dataset provides the video and image files obtained from Schlieren optical experiments performed in the Lab at the Universite de libre Bruxelles. A document detailing the data is presented (DataOverview.pdf). The unprocessed raw images for experiments 1, 2, and 3 (Raw Images Exp 1.zip, Raw Images Exp 2.zip and Raw Images Exp 3.zip) and pre-processed, segmented, and morphological processed images of experiment 3 (ProcessedImages.zip) are available. The videos presented include the unprocessed images (Video1), final processed images (Video2), and segmented ice-ocean interface (Video3) of experiment 3. 

Additional supporting graphs are also available, and are outlined below:



The Probability Density Function (PDF) and Cumulative Density Function (CDF) of experiments 1, 2, and 3.  

The scatter plot of the relative mass flux ratio prior to binning.
DOI: 10.5281/zenodo.14065600
title: Developing Digital Image Processing methods to quantify internal and interfacial convection in the ice-ocean boundary layer during sea ice desalination

description: This dataset provides the video and image files obtained from Schlieren optical experiments performed in the Lab at the Universite de libre Bruxelles. A document detailing the data is presented (DataOverview.pdf). The unprocessed raw images for experiments 1, 2, and 3 (Raw Images Exp 1.zip, Raw Images Exp 2.zip and Raw Images Exp 3.zip) and pre-processed, segmented, and morphological processed images of experiment 3 (ProcessedImages.zip) are available. The videos presented include the unprocessed images (Video1), final processed images (Video2), and segmented ice-ocean interface (Video3) of experiment 3. 

Additional supporting graphs are also available, and are outlined below:



The Probability Density Function (PDF) and Cumulative Density Function (CDF) of experiments 1, 2, and 3.  

The scatter plot of the relative mass flux ratio prior to binning.
DOI: 10.5281/zenodo.14882959
title: Developing Digital Image Processing methods to quantify internal and interfacial convection in the Hele-Shaw cell, with applications to the laboratory ice-ocean boundary layer

description: This dataset provides the video and image files obtained from Schlieren optical experiment 3 performed in the Laboratoire de Glaciologie (GLACIOL) at the Universite de libre Bruxelles. A document detailing the visual data and supporting figures is presented (DataOverview.pdf).
DOI: 10.15468/dl.gpkmpv
title: Occurrence Download

description: A dataset containing 17662382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Material citation, Material sample, Human Observation, Machine Observation, Observation)",
 "Geometry POLYGON((-180 -58,180 -58,180 84,-180 84,-180 -58))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "License is CC-BY-NC 4.0",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Animalia, Plantae)",
 "Year is 2018"
 ]
}

 The dataset includes 17662382 records from 739 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027733-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.8241794
title: Carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea

description: This dataset is created for manuscript entitle of <strong>Carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea</strong> submitted to <em>Journal of Geophysical Research: Oceans</em>.
DOI: 10.5281/zenodo.15004076
title: Twinning and Scale-Expanding Reconstruction of Complex Deep Rock Discontinuities: A Novel Theoretical and Applied Framework

description: All data used in the study to support this research is available on repository via 10.5281/zenodo.15004077
DOI: 10.5281/zenodo.15004077
title: Twinning and Scale-Expanding Reconstruction of Complex Deep Rock Discontinuities: A Novel Theoretical and Applied Framework

description: All data used in the study to support this research is available on repository via 10.5281/zenodo.15004077
DOI: 10.5281/zenodo.15301755
title: Organic carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea

description: This dataset is created for manuscript entitle of Carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea
DOI: 10.5281/zenodo.8241793
title: Organic carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea

description: This dataset is created for manuscript entitle of Carbon mineralization and its impact on metal behavior in sediments of the Yellow Sea and East China Sea
DOI: 10.15468/dl.4bdjsy
title: Occurrence Download

description: A dataset listing the 1 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((99.93946 6.86717,99.91207 6.79555,100.02288 6.75654,100.0529 6.86158,99.93946 6.86717))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (EN, VU, CR)",
 "Year 2010-2025"
 ]
}

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027758-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atusff
title: Occurrence Download

description: A dataset containing 4263 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (India, China, Myanmar, Singapore, Viet Nam, Cambodia, Indonesia, Nepal, Philippines, Malaysia, Chinese Taipei, Bangladesh, Lao People’s Democratic Republic, Thailand, Bhutan)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Draba Dill. ex L."
 ]
}

 The dataset includes 4263 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027772-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18112/openneuro.ds005540.v1.0.5
title: EmoEEG-MC: A Multi-Context Emotional EEG Dataset for Cross-Context Emotion Decoding
DOI: 10.5281/zenodo.14730728
title: Supplementary Material for "Comparison of Interpolation Methods for Global Scale Topographic Maps based on Plate Tectonics" (Submitted)

description: Supplementary Material for "Comparison of Interpolation Methods for Global Scale Topographic Maps based on Plate Tectonics" by Franziskakis et al, 2025 (submitted).

SM1 folder contains all interpolations and statisticsSM2 folder contains the compiled statistics table with the following fields:
DOI: 10.5281/zenodo.14730729
title: Supplementary Material for "Comparison of Interpolation Methods for Global Scale Topographic Maps based on Plate Tectonics" (Submitted)

description: Supplementary Material for "Comparison of Interpolation Methods for Global Scale Topographic Maps based on Plate Tectonics" by Franziskakis et al, 2025 (submitted).

SM1 folder contains all interpolations and statisticsSM2 folder contains the compiled statistics table with the following fields:
DOI: 10.15468/dl.r2aesg
title: Occurrence Download

description: A dataset containing 647 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((127.36604 37.97261,120.23512 35.12024,116.28569 34.29744,113.76244 33.74891,111.89743 33.47464,109.59359 32.54214,107.948 28.59271,109.15477 24.31416,111.29404 24.31416,113.32361 25.24666,118.42496 23.93018,124.29426 30.2383,123.96514 33.5295,127.42089 33.96832,130.16355 34.68141,130.54753 36.87554,127.36604 37.97261))",
 "OccurrenceStatus is Present",
 "TaxonKey is Ilex cornuta Lindl. & Paxton"
 ]
}

 The dataset includes 647 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033865-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14658453
title: From Genes to Shapes: Exploring Local Adaptation in Carpathian Ox-eye Daisies

description: Data Accompanying the Article: "From Genes to Shapes: Exploring Local Adaptation in Carpathian Ox-eye Daisies"Authors: Kamil Konowalik and Olga Łuczak

This repository contains the complete dataset accompanying the article, "From Genes to Shapes: Exploring Local Adaptation in Carpathian Ox-eye Daisies." The data include all raw materials and processed outputs utilized in the study, which investigates patterns of differentiation in Leucanthemum species, with a focus on the Carpathian subendemic L. rotundifolium.

The repository comprises the following:



Images of involucral bracts



High-resolution images used for morphological analysis.



Outlines of involucral bracts



Shape data derived from bract morphology.



TPS files



Thin-plate spline (TPS) files for both datasets: the full dataset (all taxa) and the subset focusing on L. rotundifolium.



SNP and SilicoDArT data



Genomic data used for population genetics and phylogenetic analyses.



Distance matrices



Pairwise distance matrices calculated for all taxa and L. rotundifolium, covering genetic, environmental, geographic, and morphological distances.



Clustering results



Output of clustering analyses for the full dataset and the L. rotundifolium subset.




R Code for Analyses:The complete R code used to conduct the analyses presented in the article is available on the associated GitHub repository: https://github.com/kkonowalik/From_Genes_to_Shapes_in_Carpathian_Ox-eye_Daisies.

This dataset and associated code provide a comprehensive resource for reproducing the analyses and exploring patterns of local adaptation, morphological differentiation, and genetic structure in Leucanthemum species.
DOI: 10.5281/zenodo.14658454
title: From Genes to Shapes: Exploring Local Adaptation in Carpathian Ox-eye Daisies

description: Data Accompanying the Article: "From Genes to Shapes: Exploring Local Adaptation in Carpathian Ox-eye Daisies"Authors: Kamil Konowalik and Olga Łuczak

This repository contains the complete dataset accompanying the article, "From Genes to Shapes: Exploring Local Adaptation in Carpathian Ox-eye Daisies." The data include all raw materials and processed outputs utilized in the study, which investigates patterns of differentiation in Leucanthemum species, with a focus on the Carpathian subendemic L. rotundifolium.

The repository comprises the following:



Images of involucral bracts



High-resolution images used for morphological analysis.



Outlines of involucral bracts



Shape data derived from bract morphology.



TPS files



Thin-plate spline (TPS) files for both datasets: the full dataset (all taxa) and the subset focusing on L. rotundifolium.



SNP and SilicoDArT data



Genomic data used for population genetics and phylogenetic analyses.



Distance matrices



Pairwise distance matrices calculated for all taxa and L. rotundifolium, covering genetic, environmental, geographic, and morphological distances.



Clustering results



Output of clustering analyses for the full dataset and the L. rotundifolium subset.




R Code for Analyses:The complete R code used to conduct the analyses presented in the article is available on the associated GitHub repository: https://github.com/kkonowalik/From_Genes_to_Shapes_in_Carpathian_Ox-eye_Daisies.

This dataset and associated code provide a comprehensive resource for reproducing the analyses and exploring patterns of local adaptation, morphological differentiation, and genetic structure in Leucanthemum species.
DOI: 10.15468/dl.gks9ys
title: Occurrence Download

description: A dataset containing 189 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Iran (Islamic Republic of)",
 "TaxonKey is Parrotia persica (DC.) C.A.Mey.",
 "Year 1980-2025"
 ]
}

 The dataset includes 189 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027778-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29045180
title: IEEE_Conference_Template.pdf

description: The <b>Smart Complaint Management System</b> is a web-based application designed to enhance urban service delivery and citizen engagement. This system allows residents to file <b>personal</b> or <b>public complaints</b> directly to the city headquarters. Personal complaints—such as issues related to electricity, water leakage, or drainage—are automatically assigned to specific technicians based on expertise and area. Public complaints—like malfunctioning street lights or road damage—can be upvoted by other users, enabling the authorities to prioritize issues based on public interest.The platform features <b>two primary modules</b>:<b>Public Module</b>: Open to all citizens for submitting and voting on public complaints.<b>Personal Module</b>: Requires login and allows users to file private complaints with status tracking.Technicians can log in using their unique ID to <b>view assigned tasks</b>, <b>update status</b>, and <b>provide resolution remarks</b>. The system supports <b>file uploads</b>, <b>email notifications</b>, and an <b>admin dashboard</b> for monitoring overall complaint trends and technician performance.Developed using technologies like <b>MongoDB</b>, <b>Express.js</b>, <b>React.js</b>, and <b>Node.js (MERN stack)</b>, this project aims to digitize and streamline civic complaint redressal mechanisms in smart cities.
DOI: 10.15468/dl.68y7sb
title: Occurrence Download

description: A dataset listing the 127 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((99.93946 6.86717,99.91207 6.79555,100.02288 6.75654,100.0529 6.86158,99.93946 6.86717))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "IucnRedListCategory is one of (NT, VU, EN, LC)",
 "OccurrenceStatus is Present",
 "Year 2010-2025"
 ]
}

 The dataset's 127 records were derived from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027765-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.29045201
title: Data of "Reproducing scattering matrix of black carbon: A comparison of the overlapping and necking models"

description: Light scattering matrix for necking model and overlapping model
DOI: 10.21227/a5r7-2038
title: "Imbalanced multi-view dataset"

description: "Imbalanced Multi-view datasets"
DOI: 10.57680/asep.0619579
title: Quantum Correlations in Three-Beam Symmetric Gaussian States Accessed via Photon-Number-Resolving Detection and Quantum Universal Invariants - data

description: The data are related to the manuscript 'Quantum Correlations in Three-Beam Symmetric Gaussian States Accessed via Photon-Number-Resolving Detection and Quantum Universal Invariants‘, written by J. Perina Jr., N. Sudak, A. Barasinski, A. Cernoch that will be published soon.
DOI: 10.5281/zenodo.15388573
title: DOCBO (Datasets of Celestial Bodies Ontology)

description: DOCBO (Datasets of Celestial Bodies Ontology), models entities, such as celestial bodies (e.g., galaxies), and their associated physical properties (e.g., volume, mass, position in the sky, luminosity).

DOCBO is based on 5 main classes and their connections, namely E02 Celestial Body, E17 Identifier, E05 Data Source, E08 Measurement and E16 Data Column.