cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15156/bio/sh0204887.10fu
title: SH0204887.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15468/dl.m6yq36
title: Occurrence Download

description: A dataset containing 1123 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pinus durangensis Martínez"
 ]
}

 The dataset includes 1123 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000729-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nsn6hu
title: Occurrence Download

description: A dataset containing 2340 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Observation, Occurrence evidence, Human Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Lupinus latifolius subsp. leucanthus (Rydb.) Kenny & D.B.Dunn, Mabrya acerifolia (Pennell) Elisens, Erigeron anchana G.L.Nesom, Desmodium metcalfei (Rose & J.H.Painter) Kearney & Peebles, Salix bebbiana Sarg., Perityle saxicola (Eastw.) Shinners, Heuchera eastwoodiae Rosend. & Lakela, Eremogone aberrans (M.E.Jones) Ikonn., Agave ×arizonica Gentry & J.H.Weber)",
 "Year 2015-2025"
 ]
}

 The dataset includes 2340 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000730-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25438/wes02.28288016
title: CREATIVE: Comparable 2022 General Election Advertising Datasets from Meta and Google

description: We are pleased to provide scholarly access to our digital advertising datasets, which include ads active on Meta and Google platforms from September 1 to Election Day 2022.We are providing these datasets to researchers free of charge. Please complete this form to gain immediate access to the available datasets.
DOI: 10.25438/wes02.28288016.v4
title: CREATIVE: Comparable 2022 General Election Advertising Datasets from Meta and Google

description: We are pleased to provide scholarly access to our digital advertising datasets, which include ads active on Meta and Google platforms from September 1 to Election Day 2022.We are providing these datasets to researchers free of charge. Please complete this form to gain immediate access to the available datasets.
DOI: 10.15468/dl.hwbwq6
title: Occurrence Download

description: A dataset containing 48 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "PublishingOrg is one of (335880a0-e2e1-11dd-8102-b8a03c50a862, 90fd6680-349f-11d8-aa2d-b8a03c50a862, c54097e6-0972-4acb-b849-eaeba29c917d, 7b8aff00-a9f8-11d8-944b-b8a03c50a862, bc092ff0-02e4-11dc-991f-b8a03c50a862, 061b4f20-f241-11da-a328-b8a03c50a862, 1016a6bc-b032-48d6-9a29-bd01de073a10, 33d232b5-5cc2-4521-8bd6-121c5bb41c32, 814cdfb5-d4f8-4453-815f-ea5df98e76bf)",
 "TaxonKey is Calophyllum brasiliense Cambess."
 ]
}

 The dataset includes 48 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000731-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13351572
title: Associated modeling data & materials for manuscript: Observing the evolution of the Sun's global coronal magnetic field over eight months

description: This archive contains the magnetohydrodynamic (MHD) modeling materials associated with the manuscript:

"Observing the evolution of the Sun’s global coronal magnetic field over eight months"

by Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu, Sarah Gibson,Richard Morton, and Cooper Downs

Science, 386(6717), 76-82, 2024, DOI: 10.1126/science.ado2993

This archive is intended for transparency and reproduceability purposes. It contains the MHD model source code, run inputs, run outputs, and example python scripts for working with the model data.

# ContentsThe subfolders are organized as follows:

### sourceThis folder contains the the high-performance MHD code "Magnetohydrodynamic Algorithm outside a Sphere" (MAS) and associated files. MAS is written in Fortran. The dependencies are very straightforward. See `README_MAS.txt` and the associated Makefile for compilation instructions.

### runsThis folder contains the three MHD model runs that are described in the manuscript. See `README_Runs.txt` for more information on the inputs and outputs. Each folder contains all files required for recreating the run.

### scriptsThis folder contains some example python scripts that illustrate how to read the model data files. This includes an example that will convert the raw 3D data to physical units and place all variables on a common, non-staggered mesh. See `README_Scripts.txt` for more information.

# Additional NotesMAS is developed and maintained by Predictive Science Inc. (PSI) in San Diego California.

The version of MAS in the source folder is not the most recent version. It is the exact version of MAS from the main branch that was used for MHDweb CORHEL runs circa 2022 when similar runs were first posted to PSI's website. For this reason, we used this exact version of MAS for the runs described in the manuscript. As such, MAS is licensed here using the Creative Commons  Attribution-NonCommercial-NoDerivatives 4.0 International license. Please see the LICENSE file or visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for details.  

We are currently working on a project that includes a public, open source release of MAS on GitHub, which will be licensed appropriately. This archive is not intended for that purpose.

If you have questions, concerns, or issues installing or running this code for reproduceability purposes, please contact Cooper Downs <cdowns@predsci.com>.
DOI: 10.5281/zenodo.13351573
title: Associated modeling data & materials for manuscript: Observing the evolution of the Sun's global coronal magnetic field over eight months

description: This archive contains the magnetohydrodynamic (MHD) modeling materials associated with the manuscript:

"Observing the evolution of the Sun’s global coronal magnetic field over eight months"

by Zihao Yang, Hui Tian, Steven Tomczyk, Xianyu Liu, Sarah Gibson,Richard Morton, and Cooper Downs

Science, 386(6717), 76-82, 2024, DOI: 10.1126/science.ado2993

This archive is intended for transparency and reproduceability purposes. It contains the MHD model source code, run inputs, run outputs, and example python scripts for working with the model data.

# ContentsThe subfolders are organized as follows:

### sourceThis folder contains the the high-performance MHD code "Magnetohydrodynamic Algorithm outside a Sphere" (MAS) and associated files. MAS is written in Fortran. The dependencies are very straightforward. See `README_MAS.txt` and the associated Makefile for compilation instructions.

### runsThis folder contains the three MHD model runs that are described in the manuscript. See `README_Runs.txt` for more information on the inputs and outputs. Each folder contains all files required for recreating the run.

### scriptsThis folder contains some example python scripts that illustrate how to read the model data files. This includes an example that will convert the raw 3D data to physical units and place all variables on a common, non-staggered mesh. See `README_Scripts.txt` for more information.

# Additional NotesMAS is developed and maintained by Predictive Science Inc. (PSI) in San Diego California.

The version of MAS in the source folder is not the most recent version. It is the exact version of MAS from the main branch that was used for MHDweb CORHEL runs circa 2022 when similar runs were first posted to PSI's website. For this reason, we used this exact version of MAS for the runs described in the manuscript. As such, MAS is licensed here using the Creative Commons  Attribution-NonCommercial-NoDerivatives 4.0 International license. Please see the LICENSE file or visit https://creativecommons.org/licenses/by-nc-nd/4.0/ for details.  

We are currently working on a project that includes a public, open source release of MAS on GitHub, which will be licensed appropriately. This archive is not intended for that purpose.

If you have questions, concerns, or issues installing or running this code for reproduceability purposes, please contact Cooper Downs <cdowns@predsci.com>.
DOI: 10.15468/dl.bzbe4k
title: Occurrence Download

description: A dataset containing 26181 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lepidochelys olivacea (Eschscholtz, 1829)"
 ]
}

 The dataset includes 26181 records from 226 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000733-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6069/gw81-6r88
title: United States Health Care Spending by Health Condition and County 2010-2019

description: The Disease Expenditure Project (DEX) at IHME produced estimates for US spending on health care across 3,110 US counties by health condition, payer type, age group, and type of care for 2010 through 2019. Tupes of care include ambulatory care, dental care, emergency department care, home health care, inpatient care, nursing facility care, and retail pharmaceutical care. This study compiles data from over 40 billion insurance claims and nearly 1 billion facility records, tracking spending across Medicare, Medicaid, private insurance, and out-of-pocket payments. These publicly available estimates can be explored using the US Health Spending visualization. This dataset contains both the full estimates as well as tables for the aggregate health categories.
DOI: 10.15468/dl.8aaxs8
title: Occurrence Download

description: A dataset containing 3932 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-88.06993 42.84333,-87.82629 42.84202,-87.82801 42.84512,-87.82514 42.84633,-87.82841 42.84586,-87.82371 42.84656,-87.82722 42.85024,-87.82968 42.84971,-87.83191 42.85186,-87.83085 42.85312,-87.83249 42.85281,-87.8348 42.85544,-87.83925 42.86727,-87.83742 42.86685,-87.83995 42.86801,-87.84337 42.87323,-87.84631 42.88397,-87.84362 42.8853,-87.84346 42.8902,-87.84593 42.89044,-87.84791 42.89711,-87.84573 42.90347,-87.84297 42.90635,-87.84227 42.9043,-87.8413 42.90643,-87.84452 42.90757,-87.84054 42.90638,-87.84073 42.91084,-87.83926 42.91304,-87.84387 42.92128,-87.84598 42.9294,-87.84304 42.94284,-87.84534 42.96213,-87.85431 42.97498,-87.85331 42.97626,-87.85459 42.97799,-87.85687 42.97808,-87.87792 42.99219,-87.88271 42.99775,-87.88786 43.00054,-87.88569 43.00203,-87.88792 43.00231,-87.88439 43.00489,-87.88763 43.00815,-87.89158 43.00721,-87.89378 43.01214,-87.89114 43.01443,-87.89384 43.0123,-87.89546 43.01592,-87.89304 43.01813,-87.89387 43.01956,-87.89618 43.01752,-87.89466 43.02137,-87.89735 43.02014,-87.89418 43.02294,-87.89582 43.02598,-87.89588 43.02879,-87.894 43.03095,-87.89478 43.03524,-87.89714 43.03215,-87.89556 43.02979,-87.89732 43.02906,-87.89821 43.03033,-87.89698 43.0366,-87.89407 43.0375,-87.89418 43.03489,-87.89361 43.03616,-87.89393 43.03766,-87.89665 43.03783,-87.89497 43.04249,-87.88885 43.04292,-87.88178 43.04615,-87.8863 43.04647,-87.88794 43.04485,-87.88986 43.04588,-87.88699 43.05302,-87.88294 43.05021,-87.88196 43.05121,-87.87967 43.04757,-87.8815 43.04526,-87.88021 43.04472,-87.87953 43.04746,-87.88195 43.05332,-87.86868 43.06458,-87.86382 43.0709,-87.86385 43.07422,-87.86675 43.07457,-87.87039 43.08064,-87.87065 43.08652,-87.87635 43.0991,-87.89396 43.11451,-87.90084 43.127,-87.90084 43.13662,-87.89197 43.14905,-87.88093 43.17059,-87.88637 43.1849,-87.89273 43.19204,-88.0537 43.19219,-88.06339 43.19001,-88.06923 42.96037,-88.06993 42.84333))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Lepidoptera"
 ]
}

 The dataset includes 3932 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000734-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e219941
title: HUD Insured Multifamily Mortgages

description: Current as of December 31, 2024<br><br>These MS Excel files include all active and terminated FHA Multifamily insured mortgages and are updated once per month after month-end closing activities. <br><br>The data is extracted from the FHA Subsidiary Ledger-Revenue Management database and includes the following data elements:<br>FHA Project Number<br>Project Name<br>Project Street/City/State/Zip<br>Number of total Units (or total beds for health and hospital care)<br>Initial/Final Endorsement Dates<br>Original Mortgage Amount<br>First Payment Date<br>Maturity Date<br>Term of loan in months<br>Interest Rate<br>Monthly Principal and Interest Payment<br>Amortized Unpaid Principal Balance<br>Holding Lender Name/City/State<br>Servicing Lender Name/City/State<br>Section of the Act Code (SOA)<br>Section of the Act Category/Subcategory (Note: SOA codes are available on the SOA_list.xlsx document in this dataset.)<br>TE code (mortgages financed by tax exempt bonds)<br>TC code (mortgages that include Low-Income Housing Tax Credits (LIHTC))<br><br>Also included in this dataset is a fact sheet on topics related to the Multifamily Insurance Operations Branch (MFIOB).<br><br>
DOI: 10.3886/e219941v1
title: HUD Insured Multifamily Mortgages

description: Current as of December 31, 2024<br><br>These MS Excel files include all active and terminated FHA Multifamily insured mortgages and are updated once per month after month-end closing activities. <br><br>The data is extracted from the FHA Subsidiary Ledger-Revenue Management database and includes the following data elements:<br>FHA Project Number<br>Project Name<br>Project Street/City/State/Zip<br>Number of total Units (or total beds for health and hospital care)<br>Initial/Final Endorsement Dates<br>Original Mortgage Amount<br>First Payment Date<br>Maturity Date<br>Term of loan in months<br>Interest Rate<br>Monthly Principal and Interest Payment<br>Amortized Unpaid Principal Balance<br>Holding Lender Name/City/State<br>Servicing Lender Name/City/State<br>Section of the Act Code (SOA)<br>Section of the Act Category/Subcategory (Note: SOA codes are available on the SOA_list.xlsx document in this dataset.)<br>TE code (mortgages financed by tax exempt bonds)<br>TC code (mortgages that include Low-Income Housing Tax Credits (LIHTC))<br><br>Also included in this dataset is a fact sheet on topics related to the Multifamily Insurance Operations Branch (MFIOB).<br><br>
DOI: 10.15468/dl.vz937z
title: Occurrence Download

description: A dataset containing 9316 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (December, November, October, September, August, July, June)",
 "TaxonKey is Erinaceus europaeus Linnaeus, 1758",
 "Year 2023-2025"
 ]
}

 The dataset includes 9316 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000735-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4jhnx
title: Occurrence Download

description: A dataset containing 290 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nototriche phyllanthos (Cav.) A.W.Hill"
 ]
}

 The dataset includes 290 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000736-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1810028401-eng
title: Machinery and equipment price index, by industry of purchase, monthly

description: Machinery and equipment price index (MEPI) by Input Output Final Demand Classification (IOFDC). Monthly Data are available from January 2016. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2016=100).
DOI: 10.25318/1810028401-fra
title: Indice des prix des machines et du matériel, par industrie d'achat, mensuel

description: Indice des prix des machines et du matériel, par industrie d'achat (IPMM) selon la Classification entrées-sorties de la demande finale (IOFDC). Les données mensuelles sont disponibles à partir du janvier 2016. Le tableau présente les données de la période de référence la plus récente et des quartre dernières périodes. La période de base de l’indice est (2016=100).
DOI: 10.5281/zenodo.14889624
title: Experimental data LLMProphet - AIST 2025 (co-located with ICST 2025)

description: Reproducing Results for LLMProphet - AIST 2025

Paper Information



Title: From Implemented to Expected Behaviors: Leveraging Regression Oracles for Non-Regression Fault Detection Using LLMs

Authors: Stefano Ruberto, Judith Perera, Gunel Jahangirova, Valerio Terragni

Affiliations:



Stefano Ruberto, JRC European Commission, Ispra, Italy (stefano.ruberto@ec.europa.eu)

Judith Perera, University of Auckland, Auckland, New Zealand (jper120@aucklanduni.ac.nz)

Gunel Jahangirova, King’s College London, London, United Kingdom (gunel.jahangirova@kcl.ac.uk)

Valerio Terragni, University of Auckland, Auckland, New Zealand (v.terragni@auckland.ac.nz)



Published in: at the 5th International Workshop on Artificial Intelligence in Software Testing (AIST 2025) co-located with 18th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2025


Reproduction Instructions

Requirements

To reproduce the results, you will need access to OpenAI's GPT-4o model.

Steps





Access GPT-4o:



Open the OpenAI Playground: GPT-4o Playground

Set the following parameters:



Temperature: 0.0

Top-p: 1.0







Run the prompts:



Copy and paste the prompts from the respective folders into the "User" prompt field, one at a time.

Make sure to clear the input before inserting a new prompt.





Prompt Folders:



Prompts_with_test_case_combinations_evaluating_19/05/22

Prompts_with_test_case_combinations_evaluating_15/05/22




File Naming Convention

Inside the folders, files are named as follows:



15_10_wrong_0_correct.java


This indicates: - 15/05/2022: The test case being evaluated. - 10_wrong: Number of test cases with faulty assertions. - 0_correct: Number of test cases with correct assertions. - .java: The files are stored in .java format to maintain formatting.

Notes



Ensure you follow the reproduction steps precisely to achieve the same results as presented in the paper.

The files inside the folders contain the necessary test cases for evaluation.


Contact

For any questions regarding this material, please reach out to the authors via the provided email addresses.
DOI: 10.5281/zenodo.14889625
title: Experimental data LLMProphet - AIST 2025 (co-located with ICST 2025)

description: Reproducing Results for LLMProphet - AIST 2025

Paper Information



Title: From Implemented to Expected Behaviors: Leveraging Regression Oracles for Non-Regression Fault Detection Using LLMs

Authors: Stefano Ruberto, Judith Perera, Gunel Jahangirova, Valerio Terragni

Affiliations:



Stefano Ruberto, JRC European Commission, Ispra, Italy (stefano.ruberto@ec.europa.eu)

Judith Perera, University of Auckland, Auckland, New Zealand (jper120@aucklanduni.ac.nz)

Gunel Jahangirova, King’s College London, London, United Kingdom (gunel.jahangirova@kcl.ac.uk)

Valerio Terragni, University of Auckland, Auckland, New Zealand (v.terragni@auckland.ac.nz)



Published in: at the 5th International Workshop on Artificial Intelligence in Software Testing (AIST 2025) co-located with 18th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2025


Reproduction Instructions

Requirements

To reproduce the results, you will need access to OpenAI's GPT-4o model.

Steps





Access GPT-4o:



Open the OpenAI Playground: GPT-4o Playground

Set the following parameters:



Temperature: 0.0

Top-p: 1.0







Run the prompts:



Copy and paste the prompts from the respective folders into the "User" prompt field, one at a time.

Make sure to clear the input before inserting a new prompt.





Prompt Folders:



Prompts_with_test_case_combinations_evaluating_19/05/22

Prompts_with_test_case_combinations_evaluating_15/05/22




File Naming Convention

Inside the folders, files are named as follows:



15_10_wrong_0_correct.java


This indicates: - 15/05/2022: The test case being evaluated. - 10_wrong: Number of test cases with faulty assertions. - 0_correct: Number of test cases with correct assertions. - .java: The files are stored in .java format to maintain formatting.

Notes



Ensure you follow the reproduction steps precisely to achieve the same results as presented in the paper.

The files inside the folders contain the necessary test cases for evaluation.


Contact

For any questions regarding this material, please reach out to the authors via the provided email addresses.
DOI: 10.15468/dl.7qf2j9
title: Occurrence Download

description: A dataset containing 7843 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "TaxonKey is Ambystoma laterale Hallowell, 1856"
 ]
 },
 "HasCoordinate is TRUE",
 "Year is greater than or equal to 1950",
 "Year is less than or equal to 2023",
 "DecimalLatitude is greater than or equal to 31.318781918868467",
 "DecimalLatitude is less than or equal to 65.14096814997067",
 "DecimalLongitude is greater than or equal to -93.82750718620308",
 "DecimalLongitude is less than or equal to -47.23818789969097",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 7843 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000737-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25318/1810028601-eng
title: Residential renovation price indexes, by project

description: Residential renovation price indexes (RRPI) by project group and individual project type. Quarterly data are available from the first quarter of 2017. The table presents data for the most recent reference period and the last four periods. The base period for the index is (2023=100).
DOI: 10.15468/dl.jvx2wb
title: Occurrence Download

description: A dataset containing 290 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Nototriche phyllanthos (Cav.) A.W.Hill"
 ]
}

 The dataset includes 290 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000738-250218110819086/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.