cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25582/data.2024-02.3031599/2548226
title: Data for EMSL Project 60580 from February 2024
DOI: 10.25582/data.2024-02.3031617/2548227
title: Data for EMSL Project 60580 from February 2024
DOI: 10.25582/data.2024-02.3031663/2548228
title: Data for EMSL Project 60580 from February 2024
DOI: 10.6084/m9.figshare.28741601
title: Diffusion coefficients, D*, from MSD values, Fig. 6f

description: .csv files ('msd_*.csv') contain all the MSD values.Python script ('generate_Ds.py') converts the MSD values to diffusion coefficients ('whole_D_data.csv').'signs.csv' contains observed directions of generated flows, inward: +1; outward: -1.From 'whote_D_data.csv', another Python script ('integrate_*.csv'), together with 'signs.csv', generates the scatter plot of signed D*, as presented in Fig. 6f.
DOI: 10.6084/m9.figshare.28741601.v1
title: Diffusion coefficients, D*, from MSD values, Fig. 6f

description: .csv files ('msd_*.csv') contain all the MSD values.Python script ('compute*.py') converts the MSD values to diffusion coefficients ('whole_D_data.csv')..csv file ('signs.csv') contains info on direction of generated flows, inward: +1; outward: -1.Another Python script ('integrate_.csv'), together with 'signs.csv', generates the scatter plot of signed D*, as presented in Fig. 6f.
DOI: 10.25582/data.2024-02.3031670/2548229
title: Data for EMSL Project 60580 from February 2024
DOI: 10.25582/data.2024-02.3031681/2548230
title: Data for EMSL Project 60580 from February 2024
DOI: 10.5281/zenodo.15313912
title: SIGSPATIAL GISCUP 2025 Human Mobility Prediction Challenge: Open Data

description: Human mobility dataset for the ACM SIGSPATIAL GIS CUP 2025: Human Mobility Prediction Challenge 

https://sigspatial2025.sigspatial.org/giscup/index.html 

This data contains movement information generated from user location data obtained from LY Corporation smartphone applications. It does not reveal the actual timestamp, latitude, longitude, etc., and does not identify individuals. This data was originally made available only be used for the purpose of participating in the GIS CUP 2025, but we are releasing the full dataset now. 

When you use this data for publications, analysis, reports, please give us credits by citing the following publication, which is the Data Descriptor of this dataset. 



Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. https://www.nature.com/articles/s41597-024-03237-9
DOI: 10.5281/zenodo.18050604
title: SIGSPATIAL GISCUP 2025 Human Mobility Prediction Challenge: Open Data

description: Human mobility dataset for the ACM SIGSPATIAL GIS CUP 2025: Human Mobility Prediction Challenge 

https://sigspatial2025.sigspatial.org/giscup/index.html 

This data contains movement information generated from user location data obtained from LY Corporation smartphone applications. It does not reveal the actual timestamp, latitude, longitude, etc., and does not identify individuals. This data was originally made available only be used for the purpose of participating in the GIS CUP 2025, but we are releasing the full dataset now. 

When you use this data for publications, analysis, reports, please give us credits by citing the following publication, which is the Data Descriptor of this dataset. 



Yabe, T., Tsubouchi, K., Shimizu, T., Sekimoto, Y., Sezaki, K., Moro, E., & Pentland, A. (2024). YJMob100K: City-scale and longitudinal dataset of anonymized human mobility trajectories. Scientific Data, 11(1), 397. https://www.nature.com/articles/s41597-024-03237-9
DOI: 10.15468/s7k7zt
title: Two remarkable new species of Hubbardiidae Cook, 1899 (Arachnida: Schizomida) from eastern Cuba

description: This dataset contains the digitized treatments in Plazi based on the original journal article Teruel, Rolando, Rodríguez-Cabrera, Tomás M. (2019): Two remarkable new species of Hubbardiidae Cook, 1899 (Arachnida: Schizomida) from eastern Cuba. Ecologica Montenegrina 20: 40-54, DOI: 10.37828/em.2019.20.4AbstractTwo new, remarkable species of schizomids are described in the present paper, both from eastern Cuba. One of them is the second known member of Troglocubazomus Teruel, 2003, which allows the redefinition of the generic diagnosis and expands the known range of this genus more than 200 km to the west, but still in the same orographic system (the Sierra Maestra Mountains). The other represents the ninth Cuban member of Antillostenochrus Armas & Teruel, 2002, but the first to be found living here in a desertic habitat (only one Hispaniolan species was previously known to live under the same aridity conditions). As results, the schizomid fauna of Cuba reaches 59 species (58 national endemics), with 39 of them occurring in its eastern region (35 regional endemics).
DOI: 10.15468/f8drr2
title: Contribution to the knowledge of Gongolaria barbata (Sargassaceae, Fucales) from the Mediterranean: insights into infraspecific diversity

description: This dataset contains the digitized treatments in Plazi based on the original journal article Falace, Annalisa, Manfrin, Chiara, Furnari, Giovanni, Burchio, Sara D’Ambros, Pallavicini, Alberto, Descourvieres, Emmanuelle, Kaleb, Sara, Lokovšek, Ana, Grech, Daniele, Alongi, Giuseppina (2024): Contribution to the knowledge of Gongolaria barbata (Sargassaceae, Fucales) from the Mediterranean: insights into infraspecific diversity. Phytotaxa 635 (3): 191-205, DOI: 10.11646/phytotaxa.635.3.1, URL: https://phytotaxa.mapress.com/pt/article/download/phytotaxa.635.3.1/51486AbstractGongolaria barbata (Sargassaceae, Fucales) is a widespread species for which several infraspecific taxa have been described, indicating its polymorphism. This study contributes to the understanding of the molecular, nomenclatural, morphological and ecological aspects of G. barbata in the Mediterranean and sheds light on the infraspecific diversity and its implications for the taxonomy of this species. Molecular analyses were performed using sequencing of the mitochondrial cox1 gene on both haptophytic and pleustophytic forms from different sites in the Adriatic and Tyrrhenian Seas. Vegetative and reproductive morphology was studied on thalli samples from the Adriatic. Our results showed that there are different morphotypes within G. barbata populations related to specific environmental conditions, suggesting infraspecific variation. In contrast, molecular analyses showed no differences between samples, regardless of whether individuals are growing “attached” to a substrate or “unattached”. We also discussed the taxonomic status and nomenclatural issues related to certain infraspecific taxa previously proposed for G. barbata. In particular, the confusion surrounding Cystoseira aurantia is clarified.
DOI: 10.15468/dl.bgv2d9
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vespidae",
 "Year is 1921",
 "Continent is North America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 73 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040427-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.chvp5z
title: Occurrence Download

description: A dataset containing 42103 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Batrachoseps attenuatus"
 ]
}

 The dataset includes 42103 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040423-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4bdshh
title: Occurrence Download

description: A dataset containing 16326 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Bryophyte Herbarium (H): V. F. Brotherus Herbarium (H-BR)"
 ]
}

 The dataset includes 16326 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040426-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3759rf
title: Occurrence Download

description: A dataset containing 26212 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Specimen, Material sample)",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Zea mays",
 "Year 1970-2025"
 ]
}

 The dataset includes 26212 records from 107 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040425-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.59z4ds
title: Occurrence Download

description: A dataset containing 369 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Mendocino College Coast Center Herbarium - Bryophytes"
 ]
}

 The dataset includes 369 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040428-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13140/2.1.1599.9680
title: On Improving Sub-Pixel Accuracy by Means of B- Spline Main Idea - Presentation IEEE IST 2014
DOI: 10.15468/dl.jmmq7a
title: Occurrence Download

description: A dataset containing 279 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vespidae",
 "Year is 1922",
 "Continent is Europe",
 "HasCoordinate is true"
 ]
}

 The dataset includes 279 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040430-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hpuruc
title: Occurrence Download

description: A dataset containing 3211 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Rutgers University, Chrysler Herbarium - Lichen Collection"
 ]
}

 The dataset includes 3211 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040431-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16967619
title: NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering (replication package)

description: This is the replication package for the paper "NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering" where we present Neo Modeling Framework (NMF), an open-source set of tools primarily designed to manipulate ultra-large datasets in the Neo4j database.  

Repository structure



NeoModelingFramework.zip - contains the replication package, including the source code for NMF, test files to run the evaluation, used artifacts, and instructions to run the framework. The most import folders are listed below:



codeGenerator - NMF generator module

modelLoader - NMF loader module

modelEditor - NMF editor module

Evaluation - contains the evaluation artifacts and results (a copy



metamodels - Ecore files used for RQ1 and RQ2

results - CSV files with the results from RQ1, RQ2 and RQ3

analysis - Jupyter notebooks used to analyze and plot the results






Running NMF

The best way to run NMF is following the instructions at our GitHub repository. A copy of the Readme file is also present inside the zip file available here.

 

Empirical Evaluation

Make sure that you follow the instructions to run NMF.

The quantitative evaluation can be re-run by running RQ1Eval.kt, RQ2Eval.kt inside modelLoader/src/test/kotlin/evaluation and RQ2Eval.kt inside modelEditor/src/test/kotlin/evaluation.

Make sure that you have an empty instance of Neo4j running.

Results will be generated as CSV files, under Evaluation/results and the results can be plotted by running the Jupyter Notebooks at Evaluation/analysis.

Please note that due to differences in hardware, re-running the experiments will probably generate slightly different results than those reported in the paper.
DOI: 10.5281/zenodo.17252303
title: NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering (replication package)

description: This is the replication package for the paper "NeoModeling Framework: Leveraging Graph-Based Persistence for Large-Scale Model-Driven Engineering" where we present Neo Modeling Framework (NMF), an open-source set of tools primarily designed to manipulate ultra-large datasets in the Neo4j database.  

Repository structure



NeoModelingFramework.zip - contains the replication package, including the source code for NMF, test files to run the evaluation, used artifacts, and instructions to run the framework. The most import folders are listed below:



codeGenerator - NMF generator module

modelLoader - NMF loader module

modelEditor - NMF editor module

Evaluation - contains the evaluation artifacts and results (a copy



metamodels - Ecore files used for RQ1 and RQ2

results - CSV files with the results from RQ1, RQ2 and RQ3

analysis - Jupyter notebooks used to analyze and plot the results






Running NMF

The best way to run NMF is following the instructions at our GitHub repository. A copy of the Readme file is also present inside the zip file available here.

 

Empirical Evaluation

Make sure that you follow the instructions to run NMF.

The quantitative evaluation can be re-run by running RQ1Eval.kt, RQ2Eval.kt inside modelLoader/src/test/kotlin/evaluation and RQ2Eval.kt inside modelEditor/src/test/kotlin/evaluation.

Make sure that you have an empty instance of Neo4j running.

Results will be generated as CSV files, under Evaluation/results and the results can be plotted by running the Jupyter Notebooks at Evaluation/analysis.

Please note that due to differences in hardware, re-running the experiments will probably generate slightly different results than those reported in the paper.
DOI: 10.15468/dl.a6672p
title: Occurrence Download

description: A dataset containing 362 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is one of (USA.9_1, USA.8_1, USA.21_1, USA.31_1, USA.39_1, USA.47_1, USA.49_1)",
 "TaxonKey is Poanes aaroni"
 ]
}

 The dataset includes 362 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040432-250920141307145/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.28835837
title: Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection

description: This dataset contains real and randomly generated networks (random graphs) from a study on clique partitioning. In total, there are 145 network files. This includes five categories: 27 ABCD graphs, 20 LFR graphs, 53 real networks, 20 Barabasi-Albert graphs, and 25 portfolio networks (S&P 500) as described in the article linked below this description. The folders describe the five categories of the networks. Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as:1) They provide citations to this data repository (https://doi.org/10.6084/m9.figshare.28835837) and the following article: Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. PLOS Complex Syst 2(9): e0000062. https://doi.org/10.1371/journal.pcsy.00000622) They license the new creations under the identical terms.<br>For more information about the data, one may refer to the article below:Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. PLOS Complex Syst 2(9): e0000062. https://doi.org/10.1371/journal.pcsy.0000062
DOI: 10.6084/m9.figshare.28835837.v1
title: Dataset of networks used in assessing the Troika algorithm for clique partitioning and community detection

description: This dataset contains real and randomly generated networks (random graphs) from a study on clique partitioning. In total, there are 145 network files. This includes five categories: 27 ABCD graphs, 20 LFR graphs, 53 real networks, 20 Barabasi-Albert graphs, and 25 portfolio networks (S&P 500) as described in the article linked below this description. The folders describe the five categories of the networks. Each network is provided in .gml format or .pkl format which can be read into a networkX graph object using standard functions from the networkX library in Python. For accessing other networks used in the study, please refer to the article for references to the primary sources of those network data.This dataset is provided under a CC BY-NC-SA Creative Commons v 4.0 license (Attribution-NonCommercial-ShareAlike). This means that other individuals may remix, tweak, and build upon these data non-commercially, as long as:1) They provide citations to this data repository (https://doi.org/10.6084/m9.figshare.28835837) and the following article: Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. PLOS Complex Syst 2(9): e0000062. https://doi.org/10.1371/journal.pcsy.00000622) They license the new creations under the identical terms.<br>For more information about the data, one may refer to the article below:Aref S, Ng B (2025) Troika algorithm: Approximate optimization for accurate clique partitioning and clustering of weighted networks. PLOS Complex Syst 2(9): e0000062. https://doi.org/10.1371/journal.pcsy.0000062
DOI: 10.15468/dl.wuvxh2
title: Occurrence Download

description: A dataset containing 35 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vespidae",
 "Year is 1922",
 "Continent is North America",
 "HasCoordinate is true"
 ]
}

 The dataset includes 35 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040433-250920141307145/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.30266404
title: Experimental Data for Biochar-Mediated H₂S Removal

description: This dataset contains experimental results from a biochar-packed biofilter used for hydrogen sulfide (H₂S) removal from biogas. Key operational parameters include moisture content (MC), empty-bed residence time (EBRT), and influent H₂S concentration, with corresponding removal efficiency (RE). The data support modeling and optimization studies using regression and machine learning approaches for improving biogas desulfurization.
DOI: 10.6084/m9.figshare.30266404.v1
title: Experimental Data for Biochar-Mediated H₂S Removal

description: This dataset contains experimental results from a biochar-packed biofilter used for hydrogen sulfide (H₂S) removal from biogas. Key operational parameters include moisture content (MC), empty-bed residence time (EBRT), and influent H₂S concentration, with corresponding removal efficiency (RE). The data support modeling and optimization studies using regression and machine learning approaches for improving biogas desulfurization.
DOI: 10.15468/dl.yspcw9
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eldarca haematomera",
 "HasCoordinate is True"
 ]
}

 The dataset includes 5 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040434-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9p9bj
title: Occurrence Download

description: A dataset containing 162 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypselonotus bitrianguliger",
 "HasCoordinate is True"
 ]
}

 The dataset includes 162 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040435-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fm2wzf
title: Occurrence Download

description: A dataset containing 753 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hypselonotus fulvus",
 "HasCoordinate is True"
 ]
}

 The dataset includes 753 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040436-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18130/v3/dwxyfj
title: Data for "Free surface dynamics of water-to-air amphibious rotor transitions"

description: This dataset was generated to support the conclusions drawn the "Free surface dynamics of water-to-air amphibious rotor transitions" article in Journal of Fluid Mechanics. It contains .mp4 movies (with and without audio) of a representative rotor described in the manuscript, and .xlsx collections of .csv data files for convenience of analysis. The data files detail the rotors' geometry characteristics, free surface shape profiles, location of the transition depth, and force, torque, position, and frequency data for water-to-air (WA) and air-to-water (AW) directions of transition for two spinup time lengths (0.075 s, 1.075 s). See ReadMe file for further details
DOI: 10.25574/25952
title: Chandra X-ray Observatory ObsId 25952

description: Opening Pandora's Box: Direct Detection of AGN at z&gt;7
DOI: 10.25574/25961
title: Chandra X-ray Observatory ObsId 25961

description: Opening Pandora's Box: Direct Detection of AGN at z&gt;7
DOI: 10.15468/dl.nmutsf
title: Occurrence Download

description: A dataset containing 164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Vespidae",
 "Year is 1923",
 "Continent is Europe",
 "HasCoordinate is true"
 ]
}

 The dataset includes 164 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040438-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fuzhu7
title: Occurrence Download

description: A dataset containing 93099 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lapsana communis",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 93099 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0040409-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.