cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18610059
title: Aboveground Biomass (AGB) Map – TEAPOTS Virtual Pilot, Netherlands (2023,2024,2025)

description: This dataset provides a spatially explicit map of Aboveground Biomass (AGB) produced within the TEAPOTS Virtual Pilot framework over Netherlands for the year 2024. The raster represents estimated AGB values at high spatial resolution and is intended for research and environmental assessment purposes.

Units

Aboveground Biomass values are expressed in Mg ha⁻¹ (megagrams per hectare). 

Usage notes

This dataset is intended for scientific research, carbon stock assessment, and ecosystem monitoring applications. Users should consider the spatial resolution and coordinate system when integrating with other datasets.
DOI: 10.15468/dl.gdw9cd
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is The holotypes of the upper Pleistocene Crocuta crocuta spelaea (Goldfuss, 1823: Hyaenidae) and Panthera leo spelaea (Goldfuss, 1810: Felidae) of the Zoolithen Cave hyena den (South Germany) and their palaeo-ecological interpretation"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000248-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.36nz59
title: Occurrence Download

description: A dataset containing 409 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Rhinolophus borneensis"
 ]
}

 The dataset includes 409 records from 29 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000252-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8x8ksy
title: Occurrence Download

description: A dataset containing 16929 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Estudios bióticos: Plantas, edafofauna epígea, anfibios y aves de los Complejos de Páramos de Sonsón y Urrao"
 ]
}

 The dataset includes 16929 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000249-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8uez5a
title: Occurrence Download

description: A dataset containing 10189 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera onca"
 ]
}

 The dataset includes 10189 records from 311 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000251-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qj5a7f
title: Occurrence Download

description: A dataset containing 11609 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panthera pardus"
 ]
}

 The dataset includes 11609 records from 175 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000253-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4u2ukf
title: Occurrence Download

description: A dataset containing 308 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "GadmGid is one of (ESP.2_1, ESP.2.2_1, ESP.2.3_1, ESP.2.1_1)",
 "Geometry POLYGON((-2.07113 41.09753,-1.9758 41.10573,-1.84576 41.05046,-1.69342 40.93173,-1.68315 40.85054,-1.61312 40.79567,-1.61629 40.64257,-1.69906 40.64353,-1.74686 40.61703,-1.76051 40.51808,-1.86824 40.4291,-1.85767 40.35693,-1.78768 40.32018,-1.7888 40.25713,-1.75403 40.22973,-1.68352 40.22895,-1.55617 40.13734,-1.50903 40.14575,-1.50962 40.11873,-1.46331 40.0911,-1.29914 40.08881,-1.19434 40.06019,-1.23043 40.02471,-1.23159 39.97969,-1.19765 39.93412,-1.16304 39.91556,-0.97568 39.92144,-0.97779 39.84941,-0.92074 39.80336,-0.85098 39.79307,-0.75615 39.83632,-0.73071 39.89889,-0.77601 39.94481,-0.69232 39.99723,-0.57432 40.02184,-0.49814 40.18237,-0.3321 40.2237,-0.28154 40.3126,-0.22165 40.33819,-0.21869 40.41018,-0.2415 40.42874,-0.20506 40.45489,-0.20319 40.49988,-0.23559 40.57272,-0.23262 40.64471,-0.1728 40.66127,-0.17051 40.71526,0.06725 40.64062,0.10152 40.66851,0.20749 40.68013,0.34469 40.80046,0.31701 40.8989,0.3623 40.95399,0.36086 40.98998,0.34682 41.0437,0.28555 41.08731,0.44717 41.2171,0.4563 41.28935,0.43138 41.31582,0.4412 41.37008,0.4166 41.38754,0.41445 41.44153,0.46159 41.4606,0.51865 41.53389,0.49087 41.63236,0.41819 41.64876,0.40511 41.67548,0.45169 41.71255,0.49973 41.71361,0.53369 41.76838,0.59346 41.77865,0.67502 41.85238,0.67175 41.94238,0.73042 41.98858,0.78511 42.15175,0.77175 42.18752,0.83945 42.3329,0.78587 42.47597,0.82078 42.52166,0.82889 42.63889,0.67862 42.744,0.32542 42.71828,0.32349 42.76326,0.23723 42.77922,0.06673 42.76597,0.0558 42.73868,0 42.71922,0 42.6922,-0.04573 42.69339,-0.15176 42.77712,-0.15052 42.80411,-0.19979 42.79632,-0.2711 42.84306,-0.35667 42.84507,-0.38264 42.80965,-0.43189 42.80176,-0.51633 42.83064,-0.52999 42.79492,-0.55442 42.79544,-0.66099 42.88772,-0.72217 42.88895,-0.71985 42.95194,-0.74369 42.97042,-0.86716 42.94577,-0.91858 42.87468,-0.9207 42.81169,-0.99607 42.75,-1.05714 42.75105,-1.10763 42.69786,-1.18084 42.69905,-1.24338 42.64599,-1.24414 42.61899,-1.30557 42.6019,-1.35573 42.5486,-1.35738 42.48559,-1.40695 42.45027,-1.42159 42.35141,-1.45866 42.32489,-1.4602 42.26187,-1.48487 42.24419,-1.46413 42.09982,-1.40503 42.04497,-1.44303 41.97344,-1.55182 41.96586,-1.61117 42.01163,-1.69568 42.01262,-1.71929 42.0399,-1.81559 42.05895,-1.87601 42.05956,-1.91272 42.03289,-1.92722 41.88892,-1.89184 41.84355,-1.91729 41.76274,-1.86966 41.73526,-1.87013 41.70824,-1.95513 41.655,-2.05146 41.63782,-2.06451 41.56587,-2.04079 41.54766,-2.04172 41.48462,-2.13742 41.49439,-2.23484 41.35996,-2.21331 41.16165,-2.07113 41.09753),(-0.73855 40.01615,-0.7387 40.01167,-0.73853 40.01678,-0.73855 40.01615))"
 ]
 },
 "EventDate 2026-01-26-2026-02-25"
 ]
}

 The dataset includes 308 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000215-260225131425191/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18414615
title: Data for "Catalytic pKa Attenuation in a Hydrolytic Metalloenzyme by Genetic Code Expansion"

description: Raw data, Source data, Figures, code, and crystallographic supplements
DOI: 10.5281/zenodo.18414616
title: Data for "Catalytic pKa Attenuation in a Hydrolytic Metalloenzyme by Genetic Code Expansion"

description: Raw data, Source data, Figures, code, and crystallographic supplements
DOI: 10.5281/zenodo.18683137
title: OPEN DATASET - Canopy height models of SFB ALS acquisitions in Amazonian forest concessions (2017–2025) - Jacundá and Jamari National Forests

description: OPEN DATASET - Canopy height models of Brazilian Forest Service (SFB) airborne laser scanning (ALS) acquisitions in Amazonian forest concessions (2017–2025) - Jacundá and Jamari National Forests

The Brazilian Forest Service (SFB) has acquired high-precision Light Detection and Ranging (LiDAR) data within forest management units of federal concessions in the Brazilian Amazon. Airborne laser scanning (ALS) is used by SFB to produce reliable, high-detail information on topography and forest canopy structure. These ALS surveys in forest concessions support: (1) improved forest inventories; (2) topographic mapping for planning, including slope, water bodies, and flood-prone areas; and (3) assessment of management impacts on biodiversity, such as effects from roads, skid trails, canopy openings from tree removal, and log decks.

This dataset provides standardised canopy height and elevation products for point cloud data acquired by SFB between 2017 and 2025. We organized and processed all raw ALS data from SFB into Canopy Height Models (CHMs), acquired across Altamira, Amapá, Bom Futuro, Caxiuanã, Crepori, Jacundá, Jamari, and Saracá-Taquera forests. The ALS datasets were processed using the Processing pipeline for Airborne Laser Scanning (ALS) v1.0.4 by Fischer et al. 2025 (https://doi.org/10.1101/2025.08.31.673375). 

The raw data used includes .las point clouds and additional acquisition information such as reports, trajectory files and metadata. The raw data are owned by CGMAF-SFB and are publicly available through an online repository (SFB open data policy). A table summarizing the raw datasets is available.

This dataset will be included in future versions of the Global Canopy Atlas!

Special thanks to Fabian J. Fischer and to José Humberto Chaves for his availability and kindness to support the processing. We are also very grateful to Silke Kämmerer (CEO | LAStools LiDAR Processing – rapidlasso GmbH) for providing the necessary LAStools license.
DOI: 10.5281/zenodo.18683136
title: OPEN DATASET - Canopy height models of SFB ALS acquisitions in Amazonian forest concessions (2017–2025) - Jacundá and Jamari National Forests

description: OPEN DATASET - Canopy height models of Brazilian Forest Service (SFB) airborne laser scanning (ALS) acquisitions in Amazonian forest concessions (2017–2025) - Jacundá and Jamari National Forests

The Brazilian Forest Service (SFB) has acquired high-precision Light Detection and Ranging (LiDAR) data within forest management units of federal concessions in the Brazilian Amazon. Airborne laser scanning (ALS) is used by SFB to produce reliable, high-detail information on topography and forest canopy structure. These ALS surveys in forest concessions support: (1) improved forest inventories; (2) topographic mapping for planning, including slope, water bodies, and flood-prone areas; and (3) assessment of management impacts on biodiversity, such as effects from roads, skid trails, canopy openings from tree removal, and log decks.

This dataset provides standardised canopy height and elevation products for point cloud data acquired by SFB between 2017 and 2025. We organized and processed all raw ALS data from SFB into Canopy Height Models (CHMs), acquired across Altamira, Amapá, Bom Futuro, Caxiuanã, Crepori, Jacundá, Jamari, and Saracá-Taquera forests. The ALS datasets were processed using the Processing pipeline for Airborne Laser Scanning (ALS) v1.0.4 by Fischer et al. 2025 (https://doi.org/10.1101/2025.08.31.673375). 

The raw data used includes .las point clouds and additional acquisition information such as reports, trajectory files and metadata. The raw data are owned by CGMAF-SFB and are publicly available through an online repository (SFB open data policy). A table summarizing the raw datasets is available.

This dataset will be included in future versions of the Global Canopy Atlas!

Special thanks to Fabian J. Fischer and to José Humberto Chaves for his availability and kindness to support the processing. We are also very grateful to Silke Kämmerer (CEO | LAStools LiDAR Processing – rapidlasso GmbH) for providing the necessary LAStools license.
DOI: 10.5281/zenodo.15869986
title: E-Learning Service Quality and Student Satisfaction in Public Universities

description: A systematic bibliographic search of Scopus and Web of Science (June 2025) identified 15 empirical studies (2016-2025) examining e-learning quality-satisfaction relationships in public universities. Following PRISMA 2020 guidelines, studies underwent MMAT 2018 quality assessment (mean score: 3.67/5). Standardised regression coefficients (β) were extracted, tabulated, and stratified by effect magnitude. Synthesis employed SWiM guidelines given methodological heterogeneity precluding meta-analysis.
DOI: 10.5281/zenodo.15869985
title: E-Learning Service Quality and Student Satisfaction in Public Universities

description: A systematic bibliographic search of Scopus and Web of Science (June 2025) identified 15 empirical studies (2016-2025) examining e-learning quality-satisfaction relationships in public universities. Following PRISMA 2020 guidelines, studies underwent MMAT 2018 quality assessment (mean score: 3.67/5). Standardised regression coefficients (β) were extracted, tabulated, and stratified by effect magnitude. Synthesis employed SWiM guidelines given methodological heterogeneity precluding meta-analysis.
DOI: 10.5281/zenodo.18773375
title: WP3 Datasets - Lusitanian Basin (Portugal)

description: Datasets for the dynamic simulations conducted by the Portuguese team (Lusitanian Basin, Portugal) in the WP3 (Simulation) of the PilotSTRATEGY project, and described in the following project public deliverables:

D3.3: "Report on optimization - Injection strategy and storage capacity";

D3.4: "Report on CO2 fate on the long-term";

D3.5: "Report on near wellbore, caprock and faults/ fractures integrity".
DOI: 10.5281/zenodo.18773376
title: WP3 Datasets - Lusitanian Basin (Portugal)

description: Datasets for the dynamic simulations conducted by the Portuguese team (Lusitanian Basin, Portugal) in the WP3 (Simulation) of the PilotSTRATEGY project, and described in the following project public deliverables:

D3.3: "Report on optimization - Injection strategy and storage capacity";

D3.4: "Report on CO2 fate on the long-term";

D3.5: "Report on near wellbore, caprock and faults/ fractures integrity".
DOI: 10.5281/zenodo.18773667
title: JEWELRY CRAFT–BASED PROVERBS IN UZBEK FOLKLORE: A LINGUOCULTURAL ANALYSIS

description: This article examines Uzbek folk proverbs formed on the basis of traditional jewelry craft from a linguocultural perspective. The study analyzes the semantic transformation of lexemes such as gold, silver, jeweler, gem, and rust, and explores their metaphorical and conceptual functions in paremiological discourse. The research reveals how material craft terminology evolves into abstract ethical and cultural values within the national worldview. Jewelry-related proverbs are interpreted as linguistic representations of moral, aesthetic, and axiological codes embedded in Uzbek culture.
DOI: 10.5281/zenodo.18773666
title: JEWELRY CRAFT–BASED PROVERBS IN UZBEK FOLKLORE: A LINGUOCULTURAL ANALYSIS

description: This article examines Uzbek folk proverbs formed on the basis of traditional jewelry craft from a linguocultural perspective. The study analyzes the semantic transformation of lexemes such as gold, silver, jeweler, gem, and rust, and explores their metaphorical and conceptual functions in paremiological discourse. The research reveals how material craft terminology evolves into abstract ethical and cultural values within the national worldview. Jewelry-related proverbs are interpreted as linguistic representations of moral, aesthetic, and axiological codes embedded in Uzbek culture.
DOI: 10.48322/kxyh-8989
title: Ulysses URAP PFR average 10-min Electric Field Data

description: This set of PFR (Plasma Frequency Receiver) data from the Ulysses URAP (Unified Radio and Plasma Waves) experiment contains averages of the average electric field intensities in 16 channels and 10-minute intervals via CDAWeb. The frequency spectra are determined from measurements on the X antenna of the URAP instrument in units of microvolt/Hz^0.5 for the frequency channel from 0.57 to 35 kHz.
DOI: 10.48322/enaf-kt86
title: Ulysses URAP PFR Peak 10-min Electric Field Data

description: This set of PFR (Plasma Frequency Receiver) data from the Ulysses URAP (Unified Radio and Plasma Waves) experiment contains averages of the peak electric field intensities in 16 channels and 10-minute intervals via CDAWeb. The frequency spectra are determined from measurements on the X antenna of the URAP instrument in units of microvolt/Hz^0.5 for the frequency channel from 0.57 to 35 kHz.
DOI: 10.5281/zenodo.18773581
title: Supporting data files for the manuscript titled "Reversal of tectonic subsidence across the South China Sea driven by spreading cessation and mantle dynamics"

description: This is the tectonic subsidence rates data for the paper "Reversal of tectonic subsidence across the South China Sea driven by spreading cessation and mantle dynamics"
DOI: 10.5281/zenodo.18773582
title: Supporting data files for the manuscript titled "Reversal of tectonic subsidence across the South China Sea driven by spreading cessation and mantle dynamics"

description: This is the tectonic subsidence rates data for the paper "Reversal of tectonic subsidence across the South China Sea driven by spreading cessation and mantle dynamics"
DOI: 10.5281/zenodo.18761295
title: Climate TRACE Emissions Inventory

description: Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country globally.

Each download package includes CSVs of:



Annual country-level emissions by sub-sector and by gas beginning in 2015

Monthly source-level emissions by sub-sector and gas beginning in 2021 and confidence

Emissions source ownership where and when available.


All Climate TRACE data are free and publicly available under a CC-BY 4.0 license to enable action and accountability at the massive scale necessary for global progress. Beginning in March 2025, Climate TRACE is releasing updated data with a 60-day lag during the final week of each month. All previous versions of the Climate TRACE asset- and country-level database will be uploaded to this page.

With the addition of December 2025 data, the Climate TRACE database is now updated to version 5.4.0.

Version 5.3.0 (released January 29, 2026) will be available here. Note: V5.0.0 release launched at COP30 and now includes data on potential options for reducing emissions at every asset in the Climate TRACE database.

V5.3.0 includes 1,888 new assets in the electricity generation sector, all of which are biomass plants from the Global Energy Monitor Global Bioenergy Power Tracker. The release also includes updated Q1 2025 data for refineries and petrochemical steam cracking; new non-GHG emissions data for petrochemical steam cracking sources in Asia Pacific and the Middle East; and data fixes in the rice cultivation and copper mining sectors.

A detailed description of data updates is available in our changelog here.

We recommend getting familiar with how Climate TRACE data are created and structured by reviewing our FAQs, resources, and guidance:



The Climate TRACE FAQs page is an especially useful resource to quickly learn how we develop the emissions inventory, what data are contained in the inventory, details on units of measurement and coverage, and much more.

The Sectors page summarizes methodological details on dozens of sub-sectors. The data are licensed under the Creative Commons Attribution 4.0 International License; more details can be found on our terms of use page.

Detailed guidance on the data schema, licensing, and citations can be found in the data guide, as well as in the “detailed-data-schema” in the download packages.


If you have any questions, please reach out to us via our contact page.
DOI: 10.5281/zenodo.15014790
title: Climate TRACE Emissions Inventory

description: Climate TRACE is a non-profit coalition of organizations building a timely, open, and accessible inventory of exactly where greenhouse gas emissions are coming from. Climate TRACE estimates greenhouse gas (GHG) and air pollutant emissions for over 2.7 million sources (from over 744 million assets), and every single country globally.

Each download package includes CSVs of:



Annual country-level emissions by sub-sector and by gas beginning in 2015

Monthly source-level emissions by sub-sector and gas beginning in 2021 and confidence

Emissions source ownership where and when available.


All Climate TRACE data are free and publicly available under a CC-BY 4.0 license to enable action and accountability at the massive scale necessary for global progress. Beginning in March 2025, Climate TRACE is releasing updated data with a 60-day lag during the final week of each month. All previous versions of the Climate TRACE asset- and country-level database will be uploaded to this page.

With the addition of December 2025 data, the Climate TRACE database is now updated to version 5.4.0.

Version 5.3.0 (released January 29, 2026) will be available here. Note: V5.0.0 release launched at COP30 and now includes data on potential options for reducing emissions at every asset in the Climate TRACE database.

V5.3.0 includes 1,888 new assets in the electricity generation sector, all of which are biomass plants from the Global Energy Monitor Global Bioenergy Power Tracker. The release also includes updated Q1 2025 data for refineries and petrochemical steam cracking; new non-GHG emissions data for petrochemical steam cracking sources in Asia Pacific and the Middle East; and data fixes in the rice cultivation and copper mining sectors.

A detailed description of data updates is available in our changelog here.

We recommend getting familiar with how Climate TRACE data are created and structured by reviewing our FAQs, resources, and guidance:



The Climate TRACE FAQs page is an especially useful resource to quickly learn how we develop the emissions inventory, what data are contained in the inventory, details on units of measurement and coverage, and much more.

The Sectors page summarizes methodological details on dozens of sub-sectors. The data are licensed under the Creative Commons Attribution 4.0 International License; more details can be found on our terms of use page.

Detailed guidance on the data schema, licensing, and citations can be found in the data guide, as well as in the “detailed-data-schema” in the download packages.


If you have any questions, please reach out to us via our contact page.
DOI: 10.5281/zenodo.14888008
title: Supplementary code to "Algebraic surfaces as Hadamard products of curves"

description: README

How to use the code?

This repository contains Macaulay2 files and a Jupyter Notebook in Macaulay2.



In order to use the Jupyter Notebook: install Jupyter Notebook and add the Macaulay2 kernel, following the guide reported in the following GitHub page. Then, download the Jupyter Notebook file in the same folder used by Jupyter Notebook;

In order to use the Macaulay2 files: download the files in the same folder used by Macaulay2. If you want to use the functions and the results of the computation contained in the file sample.m2, you should use the command load "~/sample.m2".


What does this repository contain?



M2_Hadamard.ipynb: a Jupyter Notebook in Macaulay2 with all the computations related to the paper;

Hadamard_curves_P3.m2: a file containing a function that performs the Hadamard product of two curves L and R in 3-dimensional projective space defined by equations over the rationals. After loading this file, you can define the ideal I1 of L in the variables x0,...,x3, the ideal I2 of R in the variables y0,...,y3 and type X = preimage_Hadamard(I1+I2) to get the ideal of L \star R in the variables z0,...,z3;

example_3_3.m2: a file containing the computation related to Example 3.3 of the paper;

lemma_3_8.m2: a file containing the computation related to Lemma 3.8 of the paper;

example_4_6.m2: a file containing the computation related to Example 4.6 of the paper.