cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.sr9bus
title: Occurrence Download

description: A dataset listing the 78 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is Serbia",
 "TaxonKey is Boraginaceae"
 ]
}

 The dataset's 78 records were derived from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003611-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ew3vrm
title: Occurrence Download

description: A dataset containing 149399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pteropus"
 ]
}

 The dataset includes 149399 records from 146 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003598-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/14690756
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.5281/zenodo.18443492
title: Tamil Worship Albums 4–7 – Anointed Songs of Praise & Worship

description: Collection of 4 anointed Tamil Christian worship albums (Albums 4–7) — freshly inspired songs exalting Jesus Christ.

Offered freely to the remnant —Songs+ lyrics PDFs +Cover + Thumbnail

All glory to the Lamb who was slain and reigns forever!

ஆமென். அல்லேலூயா! Hallelujah forever!

Remnant, Tamil Intercessor  KJC-DNN Organization  January 2026
DOI: 10.5281/zenodo.18443493
title: Tamil Worship Albums 4–7 – Anointed Songs of Praise & Worship

description: Collection of 4 anointed Tamil Christian worship albums (Albums 4–7) — freshly inspired songs exalting Jesus Christ.

Offered freely to the remnant —Songs+ lyrics PDFs +Cover + Thumbnail

All glory to the Lamb who was slain and reigns forever!

ஆமென். அல்லேலூயா! Hallelujah forever!

Remnant, Tamil Intercessor  KJC-DNN Organization  January 2026
DOI: 10.5281/zenodo.18444272
title: Electron density and molecular electrostatic potential of N2 molecule
DOI: 10.5281/zenodo.18444271
title: Electron density and molecular electrostatic potential of N2 molecule
DOI: 10.6084/m9.figshare.28566166
title: Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer

description: The relationship between cervical cancer (CESC) and T cells is mainly seen in the anti-tumor functions of T cells. This study aims to identify prognostic genes associated with CESC and T cells, providing a foundation for developing immunotherapy strategies. This study used data from public databases to identify T cell-related prognostic genes for CESC patients through differential expression analysis and single-cell clustering. A prognostic risk model and nomogram were constructed and validated based on these genes. Pseudotime analysis clarified T cell differentiation processes in CESC. Ultimately, Mendelian randomization (MR) was applied to determine the causal relationship between the prognostic genes and CESC. In this study, CXCL2, ANKRD22, SPP1, and C1QB were identified as prognostic genes for CESC. Survival analysis indicated that the survival rate of the high-risk cohort (HRC) was significantly lower compared to that of the low-risk cohort (LRC). A nomogram also demonstrated strong predictive capability. Notably, higher expression levels of prognostic genes were observed during the early stages of T cell differentiation. MR analyses revealed that SPP1 was a risk factor for CESC (OR = 1.165; 95% CI: 1.028–1.320; <i>p</i> = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685–0.983; <i>p</i> = .032). CXCL2, ANKRD22, SPP1, and C1QB showed strong prognostic characteristics in CESC and significant predictive capabilities for patient outcomes. The study also emphasized the critical role of T cells in CESC progression.
DOI: 10.6084/m9.figshare.28566166.v1
title: Integrated immunological analysis of single-cell and bulky tissue transcriptomes reveals the role of prognostic value of T cell-related genes in cervical cancer

description: The relationship between cervical cancer (CESC) and T cells is mainly seen in the anti-tumor functions of T cells. This study aims to identify prognostic genes associated with CESC and T cells, providing a foundation for developing immunotherapy strategies. This study used data from public databases to identify T cell-related prognostic genes for CESC patients through differential expression analysis and single-cell clustering. A prognostic risk model and nomogram were constructed and validated based on these genes. Pseudotime analysis clarified T cell differentiation processes in CESC. Ultimately, Mendelian randomization (MR) was applied to determine the causal relationship between the prognostic genes and CESC. In this study, CXCL2, ANKRD22, SPP1, and C1QB were identified as prognostic genes for CESC. Survival analysis indicated that the survival rate of the high-risk cohort (HRC) was significantly lower compared to that of the low-risk cohort (LRC). A nomogram also demonstrated strong predictive capability. Notably, higher expression levels of prognostic genes were observed during the early stages of T cell differentiation. MR analyses revealed that SPP1 was a risk factor for CESC (OR = 1.165; 95% CI: 1.028–1.320; <i>p</i> = .017), while C1Q8 acted as a protective factor (OR = 0.820; 95% CI: 0.685–0.983; <i>p</i> = .032). CXCL2, ANKRD22, SPP1, and C1QB showed strong prognostic characteristics in CESC and significant predictive capabilities for patient outcomes. The study also emphasized the critical role of T cells in CESC progression.
DOI: 10.5281/zenodo.18444225
title: Data set supporting Estrela et al. (2009): hourly data on fog water collection and meteorological observations (Machos, Ayora) 2007

description: This Zenodo record provides the original hourly (60-minutes) time series of passive fog-water collection and basic meteorological observations measured during  2007 at Mount Machos (Valencia Region, eastern Spain). These data were used in the analyses and results reported in the associated journal article (see “Related publication” below).

Files included1) Data files (hourly, year 2007)

Machos.xlsx

This file contains hourly records for 2007 with the following columns:

FECHA — date and time (timestamp)

H_SOLAR — solar time

DIR_M — mean wind direction (degrees)

VEL_M — mean wind speed (m/s)

VEL_X — maximum wind speed (m/s)

TEMP_M — mean air temperature (°C)

HR — relative humidity (%)

PRECIP — precipitation (L/m²)

FOG — fog-water yield (L/m²)

2) Site metadata

Site_locations.xlsxCoordinates and site metadata for the instrumented location.

3) Variable documentation

Data_description.txtConcise variable definitions, units, and notes for interpretation.

Related publication (please cite)

Estrela, M.J., Valiente J.A, Corell D., Fuentes D., Valdecantos, A., 2009. Prospective use of collected fog water in the restoration of degraded burned areas under dry Mediterranean conditions. Agricultural and Forest Meteorology 149 (2009), 1896-1906. https://doi.org/10.1016/j.agrformet.2009.06.016

Funding

This research was supported by the Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and the CONSOLI- DER-INGENIO 2010 program, GRACCIE project CSD2007-00067.

Keywords

fog water; fog collection; meteorology; Mediterranean; water resources; Valencia Region; Spain; reforestation

How to cite

If you use these data, please cite (i) this Zenodo dataset (the DOI assigned by Zenodo) and (ii) the related publication listed above.
DOI: 10.5281/zenodo.18444226
title: Data set supporting Estrela et al. (2009): hourly data on fog water collection and meteorological observations (Machos, Ayora) 2007

description: This Zenodo record provides the original hourly (60-minutes) time series of passive fog-water collection and basic meteorological observations measured during  2007 at Mount Machos (Valencia Region, eastern Spain). These data were used in the analyses and results reported in the associated journal article (see “Related publication” below).

Files included1) Data files (hourly, year 2007)

Machos.xlsx

This file contains hourly records for 2007 with the following columns:

FECHA — date and time (timestamp)

H_SOLAR — solar time

DIR_M — mean wind direction (degrees)

VEL_M — mean wind speed (m/s)

VEL_X — maximum wind speed (m/s)

TEMP_M — mean air temperature (°C)

HR — relative humidity (%)

PRECIP — precipitation (L/m²)

FOG — fog-water yield (L/m²)

2) Site metadata

Site_locations.xlsxCoordinates and site metadata for the instrumented location.

3) Variable documentation

Data_description.txtConcise variable definitions, units, and notes for interpretation.

Related publication (please cite)

Estrela, M.J., Valiente J.A, Corell D., Fuentes D., Valdecantos, A., 2009. Prospective use of collected fog water in the restoration of degraded burned areas under dry Mediterranean conditions. Agricultural and Forest Meteorology 149 (2009), 1896-1906. https://doi.org/10.1016/j.agrformet.2009.06.016

Funding

This research was supported by the Ministerio de Educación y Ciencia, contract number CGL2005-03386/CLI, and the CONSOLI- DER-INGENIO 2010 program, GRACCIE project CSD2007-00067.

Keywords

fog water; fog collection; meteorology; Mediterranean; water resources; Valencia Region; Spain; reforestation

How to cite

If you use these data, please cite (i) this Zenodo dataset (the DOI assigned by Zenodo) and (ii) the related publication listed above.
DOI: 10.5281/zenodo.18444277
title: Intracellular Cyclisation-Coupled Peptide Library Screening Yields Potent Transcription Factor Antagonists - Library screening sequencing data

description: Sequencing data derived from library screening against CREB1. Uploaded as word documents which illustrate the amino acid sequences observed from Sanger sequencing of randomised peptide regions, across bacterial passages. This also includes sequencing for library verification. These data were used to generate sequence logo graphics but were not used for any statistical inference, as library screening produced single hit sequences upon repeated passaging.
DOI: 10.5281/zenodo.18444278
title: Intracellular Cyclisation-Coupled Peptide Library Screening Yields Potent Transcription Factor Antagonists - Library screening sequencing data

description: Sequencing data derived from library screening against CREB1. Uploaded as word documents which illustrate the amino acid sequences observed from Sanger sequencing of randomised peptide regions, across bacterial passages. This also includes sequencing for library verification. These data were used to generate sequence logo graphics but were not used for any statistical inference, as library screening produced single hit sequences upon repeated passaging.
DOI: 10.15468/dl.jbewq8
title: Occurrence Download

description: A dataset containing 303 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bothrops lanceolatus"
 ]
}

 The dataset includes 303 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003616-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18444109
title: SuperDARN data in netCDF format (2009-May)

description: 2009-May SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.5281/zenodo.6578737
title: SuperDARN data in netCDF format (2009-May)

description: 2009-May SuperDARN radar data in netCDF format. These files were produced using version 3.0 of the public FitACF algorithm, using the AACGM v2 coordinate system. Cite this dataset if using our data in a publication.


The RST is available here: https://github.com/SuperDARN/rst



The research enabled by SuperDARN is due to the efforts of teams of scientists and engineers working in many countries to build and operate radars, process data and provide access, develop and improve data products, and assist users in interpretation.
Users of SuperDARN data and data products are asked to acknowledge this support in presentations and publications.
A brief statement on how to acknowledge use of SuperDARN data is provided below.




Users are also asked to consult with a SuperDARN PI prior to submission of work intended for publication.
A listing of radars and PIs with contact information can be found here:
(SuperDARN Radar Overview)



Recommended form of acknowledgement for the use of SuperDARN data:



'The authors acknowledge the use of SuperDARN data. SuperDARN is a collection of radars funded by national scientific funding agencies of Australia, Canada, China, France, Italy, Japan, Norway, South Africa, United Kingdom and the United States of America.'
DOI: 10.15468/dl.wqxnmm
title: Occurrence Download

description: A dataset containing 4960993 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is USA.28_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Chordata"
 ]
}

 The dataset includes 4960993 records from 176 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003613-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dyyx85
title: Occurrence Download

description: A dataset containing 37 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nyctanolis pernix"
 ]
}

 The dataset includes 37 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003617-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3535/2wp-yw8-8gy
title: Tanaissus lilljeborgi (Stebbing 1891)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.3535/pnp-nj4-y8b
title: Liocarcinus pusillus (Leach 1815)

description: Digital Specimen for the physical specimen hosted at Senckenberg Research Institute and Natural History Museum Frankfurt/M.
DOI: 10.5281/zenodo.18444113
title: NEVO: A Neuromorphic EVolutionary Optimiser with Spike-Driven Cortico-Basal-Thalamic Coordination - Codes and Results

description: Metadata

Title: "NEVO: A Neuromorphic EVolutionary Optimiser with Spike-Driven Cortico-Basal-Thalamic Coordination"

Status: Submitted to IEEE WCCI/CEC 2026 (double-blind peer review)

Overview

This repository contains the complete research implementation, experimental data, and analysis scripts for NEVO, a neuromorphic evolutionary optimiser that coordinates search operators through spike-driven neural circuits. Operator selection is implemented as action gating within a Cortico-Basal-Thalamic Loop (CBTL) whilst maintaining standard black-box optimisation interfaces. Evaluation on the BBOB noiseless benchmark suite (2,160 runs across 24 functions, 6 dimensions, 15 instances each) identifies reward design as the primary scalability constraint in neuromorphic operator coordination.

Key Contributions

1. First demonstration of spike-driven operator coordination in evolutionary optimisation

2. Systematic identification of reward design as the primary scalability barrier

3. Open-source implementation compatible with neuromorphic hardware (Nengo, extensible to Loihi 2, SpiNNaker)

4. Comprehensive experimental analysis across 2,160 BBOB runs (24 functions × 6 dimensions × 15 instances)

Reproducibility

• All code, configurations, and raw experimental data included

• Complete step-by-step instructions for running benchmarks

• Anonymised for peer review; de-anonymisation upon acceptance

Repository Structure

nevo-repository-anonymised.zip

Complete Python implementation of NEVO framework. Contains core optimiser logic, thirteen metaheuristic operators, Nengo-based CBTL neural circuit, state feature extraction, and benchmark scripts. The  basic_example.py  script generates preliminary validation figures, whilst  benchmark_experiment.py  executing the full BBOB suite evaluation.

preliminary-figures.zip

Visualisations generated by  nevo/examples/basic_example.py  for the first experiment set. Shows single-run dynamics on representative BBOB functions (f01, f06, f10, f15, f20) in 10D with 5-second simulation time.

ppdata.zip

Post-processed analysis results derived from exdata.zip. Includes BBOB 2009 baseline algorithm data (BIPOP-CMA-ES, DE-PSO, RANDOMSEARCH), target attainment summaries for ECDF comparisons, and many more resources.

exdata.zip

Raw experimental data from  nevo/examples/benchmark_experiment.py  executing the complete BBOB noiseless suite. Contains 2,160 individual runs (24 functions × 6 dimensions × 15 instances) with cocoex format fitness trajectories and experiment metadata. 

cocoex-complementary.zip

COCO benchmark complementary data generated alongside exdata.zip by  benchmark_experiment.py. Contains aggregated performance metrics, operator weight evolution data, success rate statistics, and publication-ready figures (Figures 3-6 in manuscript). Includes CSV files for quantitative analysis and PNG/PDF vector graphics for manuscript figures.
DOI: 10.5281/zenodo.18444112
title: NEVO: A Neuromorphic EVolutionary Optimiser with Spike-Driven Cortico-Basal-Thalamic Coordination - Codes and Results

description: Metadata

Title: "NEVO: A Neuromorphic EVolutionary Optimiser with Spike-Driven Cortico-Basal-Thalamic Coordination"

Status: Submitted to IEEE WCCI/CEC 2026 (double-blind peer review)

Overview

This repository contains the complete research implementation, experimental data, and analysis scripts for NEVO, a neuromorphic evolutionary optimiser that coordinates search operators through spike-driven neural circuits. Operator selection is implemented as action gating within a Cortico-Basal-Thalamic Loop (CBTL) whilst maintaining standard black-box optimisation interfaces. Evaluation on the BBOB noiseless benchmark suite (2,160 runs across 24 functions, 6 dimensions, 15 instances each) identifies reward design as the primary scalability constraint in neuromorphic operator coordination.

Key Contributions

1. First demonstration of spike-driven operator coordination in evolutionary optimisation

2. Systematic identification of reward design as the primary scalability barrier

3. Open-source implementation compatible with neuromorphic hardware (Nengo, extensible to Loihi 2, SpiNNaker)

4. Comprehensive experimental analysis across 2,160 BBOB runs (24 functions × 6 dimensions × 15 instances)

Reproducibility

• All code, configurations, and raw experimental data included

• Complete step-by-step instructions for running benchmarks

• Anonymised for peer review; de-anonymisation upon acceptance

Repository Structure

nevo-repository-anonymised.zip

Complete Python implementation of NEVO framework. Contains core optimiser logic, thirteen metaheuristic operators, Nengo-based CBTL neural circuit, state feature extraction, and benchmark scripts. The  basic_example.py  script generates preliminary validation figures, whilst  benchmark_experiment.py  executing the full BBOB suite evaluation.

preliminary-figures.zip

Visualisations generated by  nevo/examples/basic_example.py  for the first experiment set. Shows single-run dynamics on representative BBOB functions (f01, f06, f10, f15, f20) in 10D with 5-second simulation time.

ppdata.zip

Post-processed analysis results derived from exdata.zip. Includes BBOB 2009 baseline algorithm data (BIPOP-CMA-ES, DE-PSO, RANDOMSEARCH), target attainment summaries for ECDF comparisons, and many more resources.

exdata.zip

Raw experimental data from  nevo/examples/benchmark_experiment.py  executing the complete BBOB noiseless suite. Contains 2,160 individual runs (24 functions × 6 dimensions × 15 instances) with cocoex format fitness trajectories and experiment metadata. 

cocoex-complementary.zip

COCO benchmark complementary data generated alongside exdata.zip by  benchmark_experiment.py. Contains aggregated performance metrics, operator weight evolution data, success rate statistics, and publication-ready figures (Figures 3-6 in manuscript). Includes CSV files for quantitative analysis and PNG/PDF vector graphics for manuscript figures.
DOI: 10.15468/tvrkn7
title: DFO Quebec Region MLI museum collection

description: Marine organism collection of the Maurice Lamontagne Institute, Fisheries and Oceans Canada. / Collection des organismes marins de l'Institut Maurice-Lamontage, Pêches et Océans Canada.
DOI: 10.15468/dl.ygvw5k
title: Occurrence Download

description: A dataset containing 123 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ethiopia, Burundi, Kenya, Tanzania, United Republic of, Uganda, Rwanda, Somalia, Eritrea, South Sudan, Sudan, Djibouti)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum lycopersicum"
 ]
}

 The dataset includes 123 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003618-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dd9fgu
title: Occurrence Download

description: A dataset containing 186479 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Lepomis macrochirus"
 ]
}

 The dataset includes 186479 records from 195 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003615-260129131611470/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.31222912
title: Mexican Course CSV

description: Informational list of all golf courses in Mexico
DOI: 10.6084/m9.figshare.31222912.v1
title: Mexican Course CSV

description: Informational list of all golf courses in Mexico