Find research datasets worth reusing

opencc-zeroJul 2024View details →

Linked collectors and determiners for: A revision of the West African freshwater crab genus Afrithelphusa Bott, 1969 (Brachyura: Deckeniidae: Deckeniinae) based on new morphological and genetic data.

Natural history specimen data linked to collectors and determiners held within, "A revision of the West African freshwater crab genus Afrithelphusa Bott, 1969 (Brachyura: Deckeniidae: Deckeniinae) based on new morphological and genetic data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7">https://bionomia.net/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7">https://gbif.org/dataset/1cba0b87-5819-44c5-a905-a5fbdf5ab7b7</a>. Formatted as a Frictionless Data package.

opencc-zeroMay 2024View details →

Linked collectors and determiners for: Resurrection of Oxythyrea abigailoides Mikšić, 1978 (Coleoptera: Scarabaeidae: Cetoniinae) based on new morphological, morphometrical and molecular data.

Natural history specimen data linked to collectors and determiners held within, "Resurrection of Oxythyrea abigailoides Mikšić, 1978 (Coleoptera: Scarabaeidae: Cetoniinae) based on new morphological, morphometrical and molecular data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/14aeff19-f503-43f4-af68-bc55aa4adb9a">https://bionomia.net/dataset/14aeff19-f503-43f4-af68-bc55aa4adb9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/14aeff19-f503-43f4-af68-bc55aa4adb9a">https://gbif.org/dataset/14aeff19-f503-43f4-af68-bc55aa4adb9a</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov..

Natural history specimen data linked to collectors and determiners held within, "A synopsis of Ptisana Murdock ferns (Marattiaceae) in New Caledonia based on sequence data and morphology with the recognition of a new vulnerable species, P. soluta (Compton) Murdock & Perrie, comb. nov., stat. nov.". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://bionomia.net/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9">https://gbif.org/dataset/9c423299-73fd-4c27-b497-fa7b98850ed9</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Phylogenetic analysis and revision of the leafhopper genus Acuera DeLong & Freytag (Hemiptera: Cicadellidae: Gyponini) based on morphological data.

Natural history specimen data linked to collectors and determiners held within, "Phylogenetic analysis and revision of the leafhopper genus Acuera DeLong & Freytag (Hemiptera: Cicadellidae: Gyponini) based on morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c">https://bionomia.net/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c">https://gbif.org/dataset/6ba6d2ca-5dad-4270-914f-1042327d503c</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data.

Natural history specimen data linked to collectors and determiners held within, "Stolonochloa, a new Australian genus segregated from Panicum (Poaceae: Panicoideae: Paniceae: Boivinellinae) based on phenetic analysis of morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706">https://bionomia.net/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706">https://gbif.org/dataset/4d791cf1-3b5b-4f5f-b07f-3bb277298706</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Phylogenetic relationships based on morphological data and taxonomy of the genus Salvadora Baird & Girard, 1853 (Reptilia, Colubridae).

Natural history specimen data linked to collectors and determiners held within, "Phylogenetic relationships based on morphological data and taxonomy of the genus Salvadora Baird & Girard, 1853 (Reptilia, Colubridae)". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/d2a76db8-6bb1-4183-86ad-e1e2d792fc53">https://bionomia.net/dataset/d2a76db8-6bb1-4183-86ad-e1e2d792fc53</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/d2a76db8-6bb1-4183-86ad-e1e2d792fc53">https://gbif.org/dataset/d2a76db8-6bb1-4183-86ad-e1e2d792fc53</a>. Formatted as a Frictionless Data package.

Linked collectors and determiners for: Eight new species of the genus Anaplecta Burmeister, 1838 (Blattodea: Blattoidea: Anaplectidae) from China based on molecular and morphological data.

Natural history specimen data linked to collectors and determiners held within, "Eight new species of the genus Anaplecta Burmeister, 1838 (Blattodea: Blattoidea: Anaplectidae) from China based on molecular and morphological data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09">https://bionomia.net/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09">https://gbif.org/dataset/b7ec7b7c-5b54-401f-a513-7776957b6a09</a>. Formatted as a Frictionless Data package.

Research Data for the Journal Article: Metal-free catalytic systems based on imidazolium chloride and strong bases for selective oxidative esterification of furfural to methyl furoate

Open the record for dataset details and reuse information.

Research Data for the Journal Article: Insertion of CO2 to 2-methyl furoate promoted by a cobalt hypercrosslinked polymer catalyst to obtain a monomer of CO2-based biopolyesters

Open the record for dataset details and reuse information.

opencc-by-4.0Mar 2023View details →

A Submesoscale Eddy Identification Dataset Derived from GOCI I Chlorophyll–a Data based on Deep Learning

This is an observational dataset on submesoscale eddies, which obtains from high–resolution chlorophyll–a distribution images from GOCI I. We employed a combination of digital image processing, filtering, YOLOv7–X, and small object detection techniques, along with specific chlorophyll image enhancement processing, to extract information on submesoscale eddies, including their time, polarity, geographical coordinates of the eddy center, eddy radius, coordinates of the upper left and lower right corners of the prediction box, area of the eddy's inner ellipse, and confidence score, which covers eight daily periods between 00:00 and 08:00 (UTC) from April 1, 2011, to March 31, 2021. We identified a total of 19,136 anticyclonic eddies and 93,897 cyclonic eddies at a confidence threshold of 0.2. The mean radius of anticyclonic eddies is 24.44 km (range 2.5 km to 44.25 km), while that of cyclonic eddies is 12.34 km (range 1.75 km to 44 km). The unprecedented hourly resolution dataset on submesoscale eddies provides information on their distribution, morphology, and energy dissipation, making it a significant contribution to understanding marine environments and ecosystems, as well as improving climate model predictions. The article doi associated with this dataset is https://doi.org/10.5194/essd-2024-188.

opencc-by-4.0Aug 2023View details →

Code and data for molecular dating benchmark based on real and simulated Primates gene trees

Benchmark of molecular clock dating applied to single gene trees separately, whose results are described in “Factors influencing the accuracy and precision in dating single gene trees” by Guillaume Louvel and Hugues Roest Crollius. <ol> <li>Real Primates gene trees are analyzed to identify what characteristics of a gene tree are related to the precision of dating; </li> <li>alignments are also simulated on the tree of Primates to measure the accuracy of dating under controlled parameters such as the degree of rate variation and the length of the alignment.</li> </ol> Content <code>Louvel_Accuracy-dating_results_2024.tar.gz</code>:     - <code>notebook/</code>: statistical analyses in Python;     - <code>outputs/</code>: html reports with figures/tables resulting from the analysis;     - <code>lib/</code>: required libraries.     - <code>data/</code>: intermediate data needed for the final analysis (dates, gene tree features); <code>Louvel_Accuracy-dating_dating-source_2024.tar.gz</code>:     - <code>dating-source/</code>: input data and config files necessary to reproduce <code>data</code>; <code>Louvel_Accuracy-dating_raw-data-preparation_2024.tar.gz</code>:     - <code>raw-data-preparation/</code>: raw data and steps to produce <code>dating-source</code>. Requirements This code requires the libraries developed in the lab for this project, available at <a href="https://github.com/DyogenIBENS/">github.com/DyogenIBENS/</a>, but also included here in <code>lib/</code>. - <a href="https://github.com/DyogenIBENS/Phylorgs">Phylorgs</a> - <a href="https://github.com/DyogenIBENS/LibsDyogen_py3">LibsDyogen_py3</a> - <a href="https://github.com/DyogenIBENS/ToolsDyogen_py3">ToolsDyogen_py3</a>.

Occupant Simulation Data based on Honda Accord 2024 Simplified Passenger Model and Full-factorial Sampling with 243 samples and VIRTHUMAN 5, 50, 95 Percentiles

Database with 729 Honda Accord 2014 passenger occupant simulations featuring VIRTHUMAN.

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 1

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: unused productive wilderness areas (WILD-core); productive wilderness areas that are sporadically used at very low intensity (WILD-periphery); unused unproductive wilderness areas (WILD-nps); forestry areas, mainly coniferous (FO-con); forestry areas, mainly non-coniferous (FO-ncon); settlements, urban areas and infrastructure (BU-builtup)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 6

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: cropland used for production of potatoes (CL-POTA); sweet potatoes and yams (CL-SWPY); and rest of crops (CL-REST)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 4

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.  This Zenodo repository provides data on following land-use classes: cropland used for production of wheat (CL-WHEA); maize (CL-MAIZ); soybean (CL-SOYB)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 5

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: cropland used for production of millet (CL-MILL); barley (CL-BARL); sorghum (CL-SORG); rice (CL-RICE)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 7

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes:  cropland used for production of cassava (CL-CASS); sugarcane (CL-SUGC); sugarbeet (CL-SUGB); cotton (CL-COTT); fruits and vegetables (CL-VEFR)

opencc-by-4.0Nov 2024View details →

Rivals Reloaded - Adapting to Sample-Based Speed–Accuracy Trade-Offs Through Competitive Pressure: Data

Data and codebook for experiment described in publication titled Rivals Reloaded - Adapting to Sample-Based Speed–Accuracy Trade-Offs Through Competitive Pressure published in Journal of Experimental Psychology: Learning, Memory, and Cognition authored by Linda McCaughey, Johannes Prager and Klaus Fiedler

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 8

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes:  cropland used for production of beans (CL-BEAN); other pulses (CL-OPUL); groundnuts (CL-GROU); bananas and plantains (CL-BANP)

A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 9

This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPPluc), the harvested NPP (including losses, HANPPharv), and the NPP remaining in ecosystems after harvest (NPPeco) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication. The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPPharv to NPPeco results in the actual NPP available before harvest (NPPact=NPPeco+HANPPharv), and adding HANPPluc to NPPact results in the potential NPP available in the hypothetical absence of land use (NPPpot=NPPact+HANPPluc) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPPpot can be calculated by summing up HANPPharv and HANPPluc and dividing it by NPPpot. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP. This Zenodo repository provides data on following land-use classes: cropland used for production of other oilcrops (CL-OOIL); coffee (CL-COFF); fodder crops (CL-FODD)