cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.s265jm
title: Occurrence Download

description: A dataset containing 1321 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erysimum cuspidatum (M.Bieb.) DC."
 ]
}

 The dataset includes 1321 records from 81 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050367-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.atvdjt
title: Occurrence Download

description: A dataset containing 17460 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "Geometry POLYGON((110.852 18.25,117.784 18.25,117.784 22.172,110.852 22.172,110.852 18.25))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 17460 records from 103 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022083-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n5npdw
title: Occurrence Download

description: A dataset containing 695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "Year 2007-2017",
 "TaxonKey is Urocyon cinereoargenteus (Schreber, 1775)"
 ]
}

 The dataset includes 695 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008972-170826194755519/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vueb8f
title: Occurrence Download

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

 The dataset includes 37 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0050372-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3xuws
title: Occurrence Download

description: A dataset containing 343137 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lyrurus tetrix (Linnaeus, 1758)"
 ]
}

 The dataset includes 343137 records from 406 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027853-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.42xthv
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Impatiens polhillii Grey-Wilson"
 ]
}

 The dataset includes 17 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027854-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xnfa8z
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Antarctica",
 "TaxonKey is Parisotoma R.S.Bagnall, 1940"
 ]
}

 The dataset includes 68 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0008972-171124123535762/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wtbnjt
title: Occurrence Download

description: A dataset containing 2406450 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Apocynaceae"
 ]
}

 The dataset includes 2406450 records from 3344 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022079-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.66qdm4
title: Occurrence Download

description: A dataset listing the 5212 species recorded in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((23.77828 -29.88884,25.24445 -29.88884,25.24445 -28.70349,23.77828 -28.70349,23.77828 -29.88884))",
 "TaxonKey is Plantae"
 ]
}

 The dataset's 5212 records were derived from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045730-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13170875
title: Binary black-hole simulation SXS:BBH:0370

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170876
title: Binary black-hole simulation SXS:BBH:0370

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170909
title: Binary black-hole simulation SXS:BBH:0365

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170910
title: Binary black-hole simulation SXS:BBH:0365

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170922
title: Binary black-hole simulation SXS:BBH:0450

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170923
title: Binary black-hole simulation SXS:BBH:0450

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170939
title: Binary black-hole simulation SXS:BBH:0605

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170940
title: Binary black-hole simulation SXS:BBH:0605

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170957
title: Binary black-hole simulation SXS:BBH:0591

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170958
title: Binary black-hole simulation SXS:BBH:0591

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170965
title: Binary black-hole simulation SXS:BBH:0514

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170966
title: Binary black-hole simulation SXS:BBH:0514

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.15468/dl.jjn4ss
title: Occurrence Download

description: A dataset containing 6030 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Natator depressus (Garman, 1880)"
 ]
}

 The dataset includes 6030 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045737-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13170982
title: Binary black-hole simulation SXS:BBH:0576

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13170983
title: Binary black-hole simulation SXS:BBH:0576

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.8058667
title: Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"

description: This repository contains the replication package for the paper "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023).


The paper is deposited on arXiv, will be available later at the publisher's site (IEEE), and a copy is included in this repository.


The replication package is archived on Zenodo with DOI: 10.5281/zenodo.8058667. The data is distributed under the CC BY 4.0 license.


 


Citation


If you build on this data or code, please cite this work by referring to the paper:


@inproceedings{hort2023:sharing,
   title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code},
   author = {Max Hort and Anastasiia Grishina and Leon Moonen},
   booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)},
   year = {2023},
   publisher = {IEEE}
   note = {To appear. Pre-print on arXiv.}
}
description: The work included in this repository was financially supported by the Research Council of Norway through the secureIT project (grant #288787). Max Hort is supported through the ERCIM 'Alain Bensoussan' Fellowship Programme.
DOI: 10.5281/zenodo.8058668
title: Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"

description: This repository contains the replication package for the paper "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023).


The paper is deposited on arXiv, will be available later at the publisher's site (IEEE), and a copy is included in this repository.


The replication package is archived on Zenodo with DOI: 10.5281/zenodo.8058667. The data is distributed under the CC BY 4.0 license.


 


Citation


If you build on this data or code, please cite this work by referring to the paper:


@inproceedings{hort2023:sharing,
   title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code},
   author = {Max Hort and Anastasiia Grishina and Leon Moonen},
   booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)},
   year = {2023},
   publisher = {IEEE}
   note = {To appear. Pre-print on arXiv.}
}
description: The work included in this repository was financially supported by the Research Council of Norway through the secureIT project (grant #288787). Max Hort is supported through the ERCIM 'Alain Bensoussan' Fellowship Programme.
DOI: 10.5281/zenodo.13171002
title: Binary black-hole simulation SXS:BBH:0508

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171003
title: Binary black-hole simulation SXS:BBH:0508

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171024
title: Binary black-hole simulation SXS:BBH:0419

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171025
title: Binary black-hole simulation SXS:BBH:0419

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.1040057
title: Data Set For The Paper Predicting Relevance Of Change Recommendations

description: Data set for the paper Predicting Relevance of Change Recommendations by Thomas Rolfsnes, Leon Moonen, and David Binkley, In International Conference on Automated Software Engineering (ASE), pp. 694–705. 2017, IEEE.

Please cite this work by referring to the corresponding conference publication (a preprint is included in this package).

Abstract: Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: CO-CHANGE and TARMAQ. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.
description: This work is supported by the Research Council of Norway through the EvolveIT project (#221751/F20) and the Certus SFI (#203461/030). Dr. Binkley is supported by NSF grant IIA-1360707 and a J. William Fulbright award.
DOI: 10.5281/zenodo.1040056
title: Data set for the paper "Predicting Relevance of Change Recommendations"

description: Data set for the paper Predicting Relevance of Change Recommendations by Thomas Rolfsnes, Leon Moonen, and David Binkley, In International Conference on Automated Software Engineering (ASE), pp. 694–705. 2017, IEEE.


Please cite this work by referring to the corresponding conference publication (a preprint is included in this package).


Abstract: Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: CO-CHANGE and TARMAQ. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.
description: This work is supported by the Research Council of Norway through the EvolveIT project (#221751/F20) and the Certus SFI (#203461/030). Dr. Binkley is supported by NSF grant IIA-1360707 and a J. William Fulbright award.
DOI: 10.5281/zenodo.1040118
title: Data set for the paper "Predicting Relevance of Change Recommendations"

description: Data set for the paper Predicting Relevance of Change Recommendations by Thomas Rolfsnes, Leon Moonen, and David Binkley, In International Conference on Automated Software Engineering (ASE), pp. 694–705. 2017, IEEE.


Please cite this work by referring to the corresponding conference publication (a preprint is included in this package).


Abstract: Software change recommendation seeks to suggest artifacts (e.g., files or methods) that are related to changes made by a developer, and thus identifies possible omissions or next steps. While one obvious challenge for recommender systems is to produce accurate recommendations, a complimentary challenge is to rank recommendations based on their relevance. In this paper, we address this challenge for recommendation systems that are based on evolutionary coupling. Such systems use targeted association-rule mining to identify relevant patterns in a software system's change history. Traditionally, this process involves ranking artifacts using interestingness measures such as confidence and support. However, these measures often fall short when used to assess recommendation relevance. We propose the use of random forest classification models to assess recommendation relevance. This approach improves on past use of various interestingness measures by learning from previous change recommendations. We empirically evaluate our approach on fourteen open source systems and two systems from our industry partners. Furthermore, we consider complimenting two mining algorithms: CO-CHANGE and TARMAQ. The results find that random forest classification significantly outperforms previous approaches, receives lower Brier scores, and has superior trade-off between precision and recall. The results are consistent across software system and mining algorithm.
description: This work is supported by the Research Council of Norway through the EvolveIT project (#221751/F20) and the Certus SFI (#203461/030). Dr. Binkley is supported by NSF grant IIA-1360707 and a J. William Fulbright award.
DOI: 10.5281/zenodo.13171068
title: Binary black-hole simulation SXS:BBH:0486

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171069
title: Binary black-hole simulation SXS:BBH:0486

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171116
title: Binary black-hole simulation SXS:BBH:0391

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171117
title: Binary black-hole simulation SXS:BBH:0391

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171142
title: Binary black-hole simulation SXS:BBH:0500

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.13171143
title: Binary black-hole simulation SXS:BBH:0500

description: Simulation of a black-hole binary system evolved by the SpEC code.
DOI: 10.5281/zenodo.1083963
title: Data set for the paper "What are the Effects of History Length and Age on Mining Software Change Impact?"

description: Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact?
by Leon Moonen, Thomas Rolfsnes, David Binkley and Stefano di Alesio.
In Journal of Empirical Software Engineering (EMSE), 2018, Springer. https://doi.org/10.1007/s10664-017-9588-z
Available from https://evolveit.bitbucket.io/publications/emse2018/


Please cite this work by referring to the corresponding journal publication (a preprint is included in this package).


The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files or individual methods therein) potentially affected by a change. Recently, there has been increased interest in mining software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system’s change history. Two main considerations when using this approach are the history length, the number of transactions from the change history used to identify the impact of a change, and history age, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters.


In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary coupling. Specifically, we report on a series of systematic experiments using three state-of-the-art mining algorithms that involve the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis. 
description: This work is supported by the Research Council of Norway through the EvolveIT project (#221751/F20) and the Certus SFI (#203461/030). Dr. Binkley is supported by NSF grant IIA-1360707 and a J. William Fulbright award.
DOI: 10.5281/zenodo.1083964
title: Data set for the paper "What are the Effects of History Length and Age on Mining Software Change Impact?"

description: Data set for the paper What are the Effects of History Length and Age on Mining Software Change Impact?
by Leon Moonen, Thomas Rolfsnes, David Binkley and Stefano di Alesio.
In Journal of Empirical Software Engineering (EMSE), 2018, Springer. https://doi.org/10.1007/s10664-017-9588-z
Available from https://evolveit.bitbucket.io/publications/emse2018/


Please cite this work by referring to the corresponding journal publication (a preprint is included in this package).


The goal of Software Change Impact Analysis is to identify artifacts (typically source-code files or individual methods therein) potentially affected by a change. Recently, there has been increased interest in mining software change impact based on evolutionary coupling. A particularly promising approach uses association rule mining to uncover potentially affected artifacts from patterns in the system’s change history. Two main considerations when using this approach are the history length, the number of transactions from the change history used to identify the impact of a change, and history age, the number of transactions that have occurred since patterns were last mined from the history. Although history length and age can significantly affect the quality of mining results, few guidelines exist on how to best select appropriate values for these two parameters.


In this paper, we empirically investigate the effects of history length and age on the quality of change impact analysis using mined evolutionary coupling. Specifically, we report on a series of systematic experiments using three state-of-the-art mining algorithms that involve the change histories of two large industrial systems and 17 large open source systems. In these experiments, we vary the length and age of the history used to mine software change impact, and assess how this affects precision and applicability. Results from the study are used to derive practical guidelines for choosing history length and age when applying association rule mining to conduct software change impact analysis. 
description: This work is supported by the Research Council of Norway through the EvolveIT project (#221751/F20) and the Certus SFI (#203461/030). Dr. Binkley is supported by NSF grant IIA-1360707 and a J. William Fulbright award.
DOI: 10.26138/sxs:bbh:0752v2.0
title: Binary black-hole simulation SXS:BBH:0752
DOI: 10.26138/sxs:bbh:0657v2.0
title: Binary black-hole simulation SXS:BBH:0657
DOI: 10.15468/dl.x4rfns
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is North America",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Halophila johnsonii Eiseman"
 ]
}

 The dataset includes 8 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045738-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.26138/sxs:bbh:0685v2.0
title: Binary black-hole simulation SXS:BBH:0685