cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25919/n146-7v55
title: Genetic data for wheat biparental mapping populations

description: Whole genome sequencing data for the four parents of two biparental mapping populations as well as wheat DArTseq (1.0) low coverage marker data for recombinant inbred lines and parents.

Parents for cross C3:
Maternal line: 263:ZWB18 (FRET2*2/BRAMBLING//MESIA/3/BECARD/4/HUIRIVIS #1*2/MURGA/5/BECARD/FRNCLN)
Paternal line: VIXEN (MACE/IGW3119)

Parents for cross C4:
Maternal line: 4:ZIG18 (KACHU/BECARD//WBLL1*2/BRAMBLING)
Paternal line: 70:ZIF14 (KARAWAN-1//BOW #1/FENGKANG 15/3/MOUKA-4)
DOI: 10.5281/zenodo.16410102
title: Benchmark datasets for the PCE-GPR Toolbox

description: This archive contains benchmark datasets used to validate and compare the performance of the PCE-GPR Toolbox. Each benchmark test case includes input/output data for surrogate model training and evaluation. The test cases comprise analytically defined or numerically evaluated functions implemented in MATLAB, as well as SPICE-based simulations of electronic circuits. For each test case, the MATLAB functions or SPICE netlists used to generate the data are provided.

 

📁 Structure

 

Each test case is organized in the following format:

 

```

test_case_name/

├── data/

│   ├── data_train.mat # Training data for the surrogate model

│   └── data_mc.mat # Reference (Monte Carlo) test data for validation

├── function/

│   └── function_name.m # MATLAB function(s) to generate the data (when applicable)

└── netlists/

    ├── netlist_name_train.sp # SPICE netlist to generate training data (when applicable)

├── netlist_name_mc.sp # SPICE netlist to generate reference (Monte Carlo) data

└── *.lib # Other SPICE files (library models, sweep definitions)

```

 

🧪 Test Cases





 `01D_Forrester_function`: Forrester function (1D analytical example)

`03D_Ishigami_function`: Ishigami function (3D analytical example)

`05D_Duffing_van_der_Pol_oscillator`: Duffing-van der Pol oscillator (5D numerical example in MATLAB)

`05D_Friedman_function`: Friedman function (5D analytical example)

`06D_Undamped_oscillator`: Undamped oscillator (6D analytical example)

`08D_Borehole function`: Borehole function (8D analytical example)

`10D_Wingweight_function`: Wingweight function (10D analytical example)

`20D_Morris_function`: Morris function (20D analytical example)

`25D_Amplifier_gain`: Low-noise amplifier gain (25D numerical example in SPICE)

`26D_Maximum_crosstalk`: Transient maximum crosstalk in coupled transmission lines (26D numerical example in SPICE)

`54D_Interconnect_delay`: Delay in a network of transmission lines (54D numerical example in SPICE)



 

Each `.mat` file contains MATLAB variables ready to load into your analysis workflow. The MATLAB functions or SPICE netlists can be used to reproduce the training and test data, if needed.

 

📝 Notes





Data files are saved in MATLAB format (`.mat`) and are compatible with R2023b and newer.

All test cases are intended for reproducibility and benchmarking.

Refer to the PCE-GPR Toolbox documentation for usage examples.



 

🔗 Related

 

This dataset is associated with the [PCE-GPR Toolbox](https://doi.org/10.5281/zenodo.15348860).
DOI: 10.5281/zenodo.15349690
title: Benchmark datasets for the PCE-GPR Toolbox

description: This archive contains benchmark datasets used to validate and compare the performance of the PCE-GPR Toolbox. Each benchmark test case includes input/output data for surrogate model training and evaluation. The test cases comprise analytically defined or numerically evaluated functions implemented in MATLAB, as well as SPICE-based simulations of electronic circuits. For each test case, the MATLAB functions or SPICE netlists used to generate the data are provided.

 

📁 Structure

 

Each test case is organized in the following format:

 

```

test_case_name/

├── data/

│   ├── data_train.mat # Training data for the surrogate model

│   └── data_mc.mat # Reference (Monte Carlo) test data for validation

├── function/

│   └── function_name.m # MATLAB function(s) to generate the data (when applicable)

└── netlists/

    ├── netlist_name_train.sp # SPICE netlist to generate training data (when applicable)

├── netlist_name_mc.sp # SPICE netlist to generate reference (Monte Carlo) data

└── *.lib # Other SPICE files (library models, sweep definitions)

```

 

🧪 Test Cases





 `01D_Forrester_function`: Forrester function (1D analytical example)

`03D_Ishigami_function`: Ishigami function (3D analytical example)

`05D_Duffing_van_der_Pol_oscillator`: Duffing-van der Pol oscillator (5D numerical example in MATLAB)

`05D_Friedman_function`: Friedman function (5D analytical example)

`06D_Undamped_oscillator`: Undamped oscillator (6D analytical example)

`08D_Borehole function`: Borehole function (8D analytical example)

`10D_Wingweight_function`: Wingweight function (10D analytical example)

`20D_Morris_function`: Morris function (20D analytical example)

`25D_Amplifier_gain`: Low-noise amplifier gain (25D numerical example in SPICE)

`26D_Maximum_crosstalk`: Transient maximum crosstalk in coupled transmission lines (26D numerical example in SPICE)

`54D_Interconnect_delay`: Delay in a network of transmission lines (54D numerical example in SPICE)



 

Each `.mat` file contains MATLAB variables ready to load into your analysis workflow. The MATLAB functions or SPICE netlists can be used to reproduce the training and test data, if needed.

 

📝 Notes





Data files are saved in MATLAB format (`.mat`) and are compatible with R2023b and newer.

All test cases are intended for reproducibility and benchmarking.

Refer to the PCE-GPR Toolbox documentation for usage examples.



 

🔗 Related

 

This dataset is associated with the [PCE-GPR Toolbox](https://doi.org/10.5281/zenodo.15348860).
DOI: 10.5281/zenodo.17696085
title: Place Characteristics and Innovative Firm Location Dataset
DOI: 10.17632/bw3s68cxhg
title: Coronaviruses remodel the mature human tRNAome to facilitate infection

description: Current research on virus-host interactions primarily focuses on the transcription and translation of viral and host genes. However, there is a major knowledge gap between transcription and translation, known as translational decoding mediated by mature transfer RNAs (tRNAs) charged with amino acids. Codon usage analysis of seven human coronaviruses indicates that they are highly dissimilar from the human host. Quantification of the human tRNAome consisting of 57 species, demonstrated that infections with these coronaviruses robustly upregulate the global tRNAome landscapes in host cells. Deprivation of individual amino acids or knockdown of TRNT1, the enzyme adding 3’-ACC terminal for tRNA aminoacylation, inhibited coronavirus infection. Integrative analysis of codon usage and the tRNAome landscape identified a prominent role of tRNA-Asn-AUU in translational decoding of different human coronaviruses. Deprivation of asparagine (Asn) or knockdown of Asparaginyl-tRNA Synthetase 1, an enzyme that charges the Asn amino acid onto tRNA-Asn acceptors, including tRNA-Asn-AUU, profoundly inhibited coronavirus 229E infection, but to a much lesser extent for NL63 and SARS-CoV-2. Collectively, we demonstrated that human coronaviruses are capable of remodeling the host mature tRNAome to facilitate infection. However, the regulatory patterns and sensitivities to interference, particularly at the single amino acid or tRNA levels, vary among different coronavirus species. These findings provide a new perspective for understanding virus-host interactions.
DOI: 10.17632/bw3s68cxhg.1
title: Coronaviruses remodel the mature human tRNAome to facilitate infection

description: Current research on virus-host interactions primarily focuses on the transcription and translation of viral and host genes. However, there is a major knowledge gap between transcription and translation, known as translational decoding mediated by mature transfer RNAs (tRNAs) charged with amino acids. Codon usage analysis of seven human coronaviruses indicates that they are highly dissimilar from the human host. Quantification of the human tRNAome consisting of 57 species, demonstrated that infections with these coronaviruses robustly upregulate the global tRNAome landscapes in host cells. Deprivation of individual amino acids or knockdown of TRNT1, the enzyme adding 3’-ACC terminal for tRNA aminoacylation, inhibited coronavirus infection. Integrative analysis of codon usage and the tRNAome landscape identified a prominent role of tRNA-Asn-AUU in translational decoding of different human coronaviruses. Deprivation of asparagine (Asn) or knockdown of Asparaginyl-tRNA Synthetase 1, an enzyme that charges the Asn amino acid onto tRNA-Asn acceptors, including tRNA-Asn-AUU, profoundly inhibited coronavirus 229E infection, but to a much lesser extent for NL63 and SARS-CoV-2. Collectively, we demonstrated that human coronaviruses are capable of remodeling the host mature tRNAome to facilitate infection. However, the regulatory patterns and sensitivities to interference, particularly at the single amino acid or tRNA levels, vary among different coronavirus species. These findings provide a new perspective for understanding virus-host interactions.
DOI: 10.17632/3nw5747hzk
title: No toxicity to the tropical marine microalgae Tisochrysis lutea from gamma radiation

description: Raw data obtained in 72 h chronic toxicity tests exposing microalgae to gamma radiation and nickel as a reference toxicant following standard methods from OECD Guideline Test 201, and the code used to analyse data in R Studio using Bayesnec package.
DOI: 10.17632/3nw5747hzk.1
title: No toxicity to the tropical marine microalgae Tisochrysis lutea from gamma radiation

description: Raw data obtained in 72 h chronic toxicity tests exposing microalgae to gamma radiation and nickel as a reference toxicant following standard methods from OECD Guideline Test 201, and the code used to analyse data in R Studio using Bayesnec package.
DOI: 10.17632/mhg6n3mzcb
title: Dataset of ASTDL, OCS and GPA

description: This DATASET was collected to determine university students' attitudes toward distance education, their satisfaction with online courses, and their academic achievement. The data was collected using ASTDL developed by Çelik and Uzunboylu (2022)and OCSS developed by (Bayrak et al., 2020).
Bayrak, F., Tibi, M. H., & Altun, A. (2020). Development Of Online Course Satisfaction Scale. Turkish Online Journal of Distance Education, 21(4), 110–123. https://doi.org/10.17718/tojde.803378
Çelik, B., & Uzunboylu, H. (2022). Developing an attitude scale towards distance learning. Behaviour & Information Technology, 41(4), 731–739. https://doi.org/10.1080/0144929X.2020.1832576
DOI: 10.17632/mhg6n3mzcb.1
title: Dataset of ASTDL, OCS and GPA

description: This DATASET was collected to determine university students' attitudes toward distance education, their satisfaction with online courses, and their academic achievement. The data was collected using ASTDL developed by Çelik and Uzunboylu (2022)and OCSS developed by (Bayrak et al., 2020).
Bayrak, F., Tibi, M. H., & Altun, A. (2020). Development Of Online Course Satisfaction Scale. Turkish Online Journal of Distance Education, 21(4), 110–123. https://doi.org/10.17718/tojde.803378
Çelik, B., & Uzunboylu, H. (2022). Developing an attitude scale towards distance learning. Behaviour & Information Technology, 41(4), 731–739. https://doi.org/10.1080/0144929X.2020.1832576
DOI: 10.15468/dl.yzvaqe
title: Occurrence Download

description: A dataset containing 71 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Burundi",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Dendrocygna viduata (Linnaeus, 1766)",
 "Year 1932-2025"
 ]
}

 The dataset includes 71 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013804-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jsaaj7
title: Occurrence Download

description: A dataset containing 1080 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((26.68561 41.83067,68.13804 41.83067,68.13804 58.40454,26.68561 58.40454,26.68561 41.83067))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Carex supina Willd. ex Wahlenb."
 ]
}

 The dataset includes 1080 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0013808-250515123054153/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xjx9rh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Gallus varius",
 "Country is Chinese Taipei",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049488-250811113504898/datasets/export for details.
DOI: 10.15468/dl.qft5py
title: Occurrence Download

description: A dataset containing 78 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((-108.28678527065046 39.95345372225592, -108.29806424123461 39.95306586267464, -108.30902752935131 39.95099074774817, -108.31925294295148 39.94730832812074, -108.32834688954156 39.94216045664583, -108.33595959857836 39.935745385277805, -108.3417986077872 39.92831008200343, -108.34563998834726 39.92014067289747, -108.34733687979391 39.91155138684387, -108.34682501751293 39.90287243684863, -108.34412505821645 39.89443731019598, -108.33934163603803 39.886569958886696, -108.33265920835117 39.87957238165361, -108.32433487114254 39.87371306997836, -108.3146884345411 39.86921675425114, -108.30409014653956 39.86625583436842, -108.29294653449641 39.86494381393217, -108.28168489786879 39.86533098135579, -108.27073703061265 39.86740249733157, -108.26052277708831 39.87107895912347, -108.25143403076972 39.876219420905315, -108.24381977050955 39.88262675878173, -108.23797269474652 39.89005518211984, -108.23411796036379 39.89821961225214, -108.23240446082362 39.90680657829754, -108.23289898916825 39.9154862204497, -108.23558352761587 39.923924946050036, -108.24035578971483 39.93179825519119, -108.24703301711337 39.93880324211527, -108.25535890552514 39.94467028728277, -108.26501340866908 39.949173482949284, -108.27562505049694 39.952139381809346, -108.28678527065046 39.95345372225592))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 78 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049487-250811113504898/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.29189798
title: JBS MAC final model only.sav

description: This is the dataset for a manuscript submitted to the Journal of Borderlands Studies.
DOI: 10.6084/m9.figshare.29189798.v1
title: JBS MAC final model only.sav

description: This is the dataset for a manuscript submitted to the Journal of Borderlands Studies.
DOI: 10.15468/dl.m2cu9v
title: Occurrence Download

description: A dataset containing 137315 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Arrhenatherum elatius (L.) P.Beauv. ex J.Presl & C.Presl",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 137315 records from 349 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007254-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15474332
title: euphorbia_tuckeyana_zanfona_1.fasta.gz
DOI: 10.5281/zenodo.15474333
title: euphorbia_tuckeyana_zanfona_1.fasta.gz
DOI: 10.17632/5jk2w4m2ny.1
title: Soybean1
DOI: 10.15468/dl.x4sq4d
title: Occurrence Download

description: A dataset containing 68182 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Anser canagicus"
 ]
}

 The dataset includes 68182 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005645-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/g322h7dt96
title: Bangla Sign Language Word Dataset Image based Sign Recognition

description: This data set was developed to facilitate the study of automatic identification of the Bangali Sign Language (BdSL) in response to the lack of publicly accessible materials for the Bangladeshi Deaf and hard-of-hearing community. It is a collection of 376 high-quality JPG pictures of 40 distinct BdSL words, including categories of actions, objects, context, feelings, status, and so forth. A still hand gesture of each word of Bangla was captured in each image and had been resized to 500 x 500 pixels to provide uniformity to the application of machine learning. 

The information was gathered at the southeast university campus and residential areas of the authors at Dhaka, Bangladesh, from June 2025 to August 2025, using consumer-level smartphones (Redmi Note 7 Pro, Honor 200, Oppo A5, and POCO X3). Five participants repeated the signs according to demonstrations of references of reputable sign language teachers and Deaf actors. Several photos of each word were taken at different angles, different distances, and different lighting conditions to get the natural variations. 

Each image is coded with a distinct identifier (e.g. W10101 where W is "word," the first digit is a word category, the next two digits are a word index, and the final two digits are an instance of the image). The data is hierarchically arranged within the semantic category into a folder structure that is easily reused to train, test, and benchmark recognition models. By offering one of the first organized datasets on word-level BdSL recognition, this dataset is designed to ease computer vision research, inclusive education, and assistive technology development.
DOI: 10.17632/g322h7dt96.1
title: Bangla Sign Language Word Dataset Image based Sign Recognition

description: This data set was developed to facilitate the study of automatic identification of the Bangali Sign Language (BdSL) in response to the lack of publicly accessible materials for the Bangladeshi Deaf and hard-of-hearing community. It is a collection of 376 high-quality JPG pictures of 40 distinct BdSL words, including categories of actions, objects, context, feelings, status, and so forth. A still hand gesture of each word of Bangla was captured in each image and had been resized to 500 x 500 pixels to provide uniformity to the application of machine learning. 

The information was gathered at the southeast university campus and residential areas of the authors at Dhaka, Bangladesh, from June 2025 to August 2025, using consumer-level smartphones (Redmi Note 7 Pro, Honor 200, Oppo A5, and POCO X3). Five participants repeated the signs according to demonstrations of references of reputable sign language teachers and Deaf actors. Several photos of each word were taken at different angles, different distances, and different lighting conditions to get the natural variations. 

Each image is coded with a distinct identifier (e.g. W10101 where W is "word," the first digit is a word category, the next two digits are a word index, and the final two digits are an instance of the image). The data is hierarchically arranged within the semantic category into a folder structure that is easily reused to train, test, and benchmark recognition models. By offering one of the first organized datasets on word-level BdSL recognition, this dataset is designed to ease computer vision research, inclusive education, and assistive technology development.
DOI: 10.5281/zenodo.17699925
title: Table 2 in The ichthyofauna of upper rio Capivari: defining conservation strategies based on the composition and distribution of fish species

description: Table 2. Recorded species in the region of upper Capivari River (*exotic species; SP - proposed State Park, EPA - proposed Environmental Protection Area; O - outside the proposed conservation areas).
Species / OccurrenceSPEPAOOrder CharaciformesAnostomidaeLeporellus vittatus (Valenciennes, 1850)xxLeporinus amblyrhynchus Garavello & Britski, 1987xLeporinus paranensis Garavello & Britski, 1987xLeporinus sp.CharacidaeAstyanax altiparanae Garutti & Britski, 2000xxAstyanax fasciatus (Cuvier, 1819)xxxAstyanax aff. scabripinnis (Jenyns, 1842)xxxBrycon nattereri Günther, 1864xOdontostilbe sp.xPiabina argentea Reinhardt, 1867xSalminus hilarii Valenciennes, 1850xSerrapinnus sp.xCrenuchidaeCharacidium gomesi Travassos, 1956xxCharacidium zebra Eigenmann, 1909xCharacidium sp.xCurimatidaexSteindachnerina insculpta (Fernández-Yépez, 1948)xHerythrinidaeHoplias malabaricus (Bloch, 1794)xParodontidaeApareiodon affinis (Steindachner, 1879)xApareiodon ibitiensis Amaral Campos, 1944xParodon nasus Kner, 1859xOrder CyprinodontiformesPoeciliidaePhalloceros harpagos Lucinda, 2008xPoecilia reticulata Peters, 1859 *xOrder GymnotiformesSternopygidaeEigenmannia virescens (Valenciennes, 1836)xOrder PerciformesCichlidaeCichlasoma aff. facetum (Jenyns, 1842)xxTilapia rendalli (Boulenger, 1897) *xGeophagus brasiliensis (Quoy & Gaimard, 1824)xxOrder SiluriformesHeptapteridaeCetopsorhamdia iheringi Schubart & Gomes, 1959xxHeptapterus sp.xRhamdia quelen (Quoy & Gaimard, 1824)xLoricariidaeHypostomus sp. 1xxxHypostomus sp. 2xxxHypostomus sp. 3xNeoplecostomus paranensis Langeani, 1990xxxNeoplecostomus sp. 2xPareiorhaphis sp.xxPareiorhina carrancas Bockmann & Ribeiro, 2003xxxPimelodidaeIheringichthys labrosus (Lütken, 1874)xPimelodus heraldoi Azpelicueta, 2001xPimelodus maculatus Lacepède, 1803xTrichomycteridaeTrichomycterus brasiliensis Lütken, 1874xxxTrichomycterus aff. itatiayae Miranda Ribeiro, 1906xxx
description: Published as part of Pompeu, Paulo dos Santos, Reis, Liana Sisi dos, Gandini, Cíntia Veloso, Souza, Rafael Couto Rosa de & Favero, Jana Menegassi del, 2009, The ichthyofauna of upper rio Capivari: defining conservation strategies based on the composition and distribution of fish species, pp. 663 in Neotropical Ichthyology 7 (4) on page 663, DOI: 10.1590/s1679-62252009000400015, http://zenodo.org/record/17699870