cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17095994
title: Data: Towards scalable quantum computations of atomic nuclei

description: Data and Python scripts to produce the figures of "Towards scalable quantum computations of atomic nuclei" by C. Gu, M. Heinz, O. Kiss, and T. Papenbrock.
DOI: 10.6084/m9.figshare.28559318.v2
title: The Self-Inflicted Collapse: How Recursive Training Undermines Large Language Models in Automated Software Engineering Tasks

description: Large Language Models (LLMs) have revolutionized natural language processing and are now integral to various automated software engineering tasks, such as code generation, vulnerability detection, and code summarization. However, the way these models are trained critically affects their long-term performance. In particular, recursive self-training—where models are continuously fine-tuned on data generated by their own outputs—poses a significant challenge, as it can lead to the gradual accumulation of errors and a phenomenon known as model collapse.This paper, "The Self-Inflicted Collapse: How Recursive Training Undermines Large Language Models in Automated Software Engineering Tasks," investigates the impact of recursive training on LLMs. Our study leverages three well-known datasets:<b>HumanEval</b> is used for the code generation task, providing a collection of programming problems with reference solutions to measure accuracy through the pass@1 metric<sup>1</sup>.<b>CodeSearchNet</b> serves the code summarization task, offering paired code snippets and human-written summaries, with performance evaluated using BLEU-4 scores<sup>2</sup>.<b>ReVeal Dataset</b> is employed for the vulnerability detection task, containing annotated smart contract code and detailed vulnerability reports, with performance assessed via the F1 score<sup>3</sup>.We benchmark six models—ChatGPT 4o, ChatGPT 4.5, Claude 3.5 Sonnet, Claude 3.7 Sonnet, DeepSeek R1, and Llama 3.2—across these tasks. First, baseline performance is established by fine-tuning each model exclusively on high-quality human-generated data. Then, we simulate a recursive training scenario in which the models are continuously fine-tuned on their own generated outputs over 10 generations. Performance is monitored through various metrics, including pass@1, F1 score, BLEU-4, and perplexity, to capture how recursive self-training affects each model's predictive capability.Our experimental results reveal a consistent pattern of performance degradation when models are trained solely on their own outputs. As the generations progress, key metrics decline and perplexity increases, providing quantitative evidence of model collapse. This study highlights the risks associated with recursive self-training and underscores the need for improved training paradigms to maintain the robustness of LLMs in automated software engineering applications.
DOI: 10.15468/dl.brcyxq
title: Occurrence Download

description: A dataset containing 21 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Australia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 21 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051634-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8g3hty
title: Occurrence Download

description: A dataset containing 168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Canada",
 "HasCoordinate is true"
 ]
}

 The dataset includes 168 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051635-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wc25s4
title: Occurrence Download

description: A dataset containing 1299 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Estonia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1299 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051636-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3hsdjd
title: Occurrence Download

description: A dataset containing 15168 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Coregonus lavaretus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 15168 records from 40 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051637-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.adw3fy
title: Occurrence Download

description: A dataset containing 3121 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "Country is one of (New Zealand)",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Undaria pinnatifida [GBIF Backbone Taxonomy], Theora lubrica [GBIF Backbone Taxonomy], Styela clava [GBIF Backbone Taxonomy], Charybdis japonica [GBIF Backbone Taxonomy], Tritia burchardi [GBIF Backbone Taxonomy], Didemnum vexillum [GBIF Backbone Taxonomy], Eudistoma elongatum [GBIF Backbone Taxonomy], Amathia verticillata [GBIF Backbone Taxonomy], Ciona intestinalis [GBIF Backbone Taxonomy], Celleporaria nodulosa [GBIF Backbone Taxonomy], Symplegma brakenhielmi [GBIF Backbone Taxonomy], Arcuatula senhousia [GBIF Backbone Taxonomy], Pyromaia tuberculata [GBIF Backbone Taxonomy], Clavelina lepadiformis [GBIF Backbone Taxonomy], Ciona savignyi [GBIF Backbone Taxonomy], Botrylloides diegensis [GBIF Backbone Taxonomy], Watersipora subatra [GBIF Backbone Taxonomy], Bugulina flabellata [GBIF Backbone Taxonomy], Magallana gigas [GBIF Backbone Taxonomy], Sabella spallanzanii [GBIF Backbone Taxonomy])"
 ]
}

 The dataset includes 3121 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051638-250827131500795/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.29595692
title: ...

description: ....
DOI: 10.6084/m9.figshare.29595692.v6
title: ...

description: ....
DOI: 10.6084/m9.figshare.29595692.v5
title: C2FSM

description: C2FSM: A Tool for Active Learning of Extended Finite State Machines from C ProgramsThis is the reproduction package for C2FSM, which contains the C2FSM Docker image and experiment results.How to reproduce:Download all files and unzip them (including "docker load -i c2fsm.tar.gz").Run "sh gen_code.sh". For each test/sv-benchmarks/inputs/*/src/main.c, C2FSM instruments this source program (instrumented programs are in */code/model.c).Run "sh make_trace.sh". C2FSM generates execution traces for each benchmark program. Note that this script assumes that test cases are already present. If you want to run AFL++, please set "-m all" instead of "-m trace -i ...".Run "cd test/sv-benchmarks". Then, you can run either "sh abstract.sh" or "sh baseline.sh", where the former uses the abstraction mapper, while the latter does not. Before that, you may remove the "abstract" or "baseline" directory (the original experiment results), copy the "inputs" directory, and rename the copied directory as "abstract" or "baseline", to reproduce the original results.The "test/journal_benchmarks" directory (the Simulink benchmarks) can be processed in the same way (except for the preprocessing step).For further details, please refer to "README.md".
DOI: 10.5281/zenodo.17095978
title: Exploring Interoperability Between Local and Global Databases in Scientometrics: Lattes, CAPES, and OpenAlex

description: This is the resulting dataset of matching tables for authors and institutions for the paper described below. Brazilian researchers are matched to OpenAlex authors and Brazilian institutions are matched to OpenAlex institutions.

Abstract:

Numerous initiatives are currently underway to disambiguate databases worldwide. In this paper, we propose a methodology for disambiguating research entities using big data techniques, adopting an approach that goes from local to global databases. Our objective is to enhance the quality of data in the OpenAlex database by leveraging information from Brazilian databases, particularly data from the Lattes Platform and the Brazilian Federal Agency for Support and Evaluation of Graduate Education. We compare similar names of authors and institutions, employing Digital Object Identifiers to link entities, along with an adaptation of the Levenshtein distance algorithm. The proposed method is straightforward to implement in tabular databases and facilitates disambiguation, thereby contributing to open science practices and providing an effective solution for research information systems. The findings indicate the potential for integrating local and global databases to address issues related to ambiguous names and incomplete metadata.
DOI: 10.5281/zenodo.17095979
title: Exploring Interoperability Between Local and Global Databases in Scientometrics: Lattes, CAPES, and OpenAlex

description: This is the resulting dataset of matching tables for authors and institutions for the paper described below. Brazilian researchers are matched to OpenAlex authors and Brazilian institutions are matched to OpenAlex institutions.

Abstract:

Numerous initiatives are currently underway to disambiguate databases worldwide. In this paper, we propose a methodology for disambiguating research entities using big data techniques, adopting an approach that goes from local to global databases. Our objective is to enhance the quality of data in the OpenAlex database by leveraging information from Brazilian databases, particularly data from the Lattes Platform and the Brazilian Federal Agency for Support and Evaluation of Graduate Education. We compare similar names of authors and institutions, employing Digital Object Identifiers to link entities, along with an adaptation of the Levenshtein distance algorithm. The proposed method is straightforward to implement in tabular databases and facilitates disambiguation, thereby contributing to open science practices and providing an effective solution for research information systems. The findings indicate the potential for integrating local and global databases to address issues related to ambiguous names and incomplete metadata.
DOI: 10.15468/dl.kugpay
title: Occurrence Download

description: A dataset containing 3631 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Finland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3631 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051639-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.6jvmcp
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Iceland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051640-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nkrjf4
title: Occurrence Download

description: A dataset containing 153 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 153 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051641-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s8crn9
title: Occurrence Download

description: A dataset containing 45764 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Australia)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1951",
 "Year is less than or equal to 1951"
 ]
}

 The dataset includes 45764 records from 155 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051619-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2bwdjd
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Protomelas spilonotus)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051642-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7rcbn
title: Occurrence Download

description: A dataset containing 244317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-72.62509 11.19521,-72.65586 11.17318,-72.74979 11.11095,-72.78419 11.06688,-72.77182 11.03634,-72.85918 10.90646,-72.9075 10.86278,-72.99873 10.72169,-73.18582 10.51218,-73.24134 10.54478,-73.2827 10.53396,-73.29507 10.49994,-73.29082 10.45781,-73.35073 10.38359,-73.4725 10.26801,-73.58151 10.19611,-73.7025 10.12537,-73.77865 10.10489,-73.89771 10.11416,-73.95492 10.20268,-74.02643 10.24366,-74.02798 10.25796,-74.06199 10.28927,-74.08944 10.30628,-74.10645 10.37895,-74.1366 10.46593,-74.15747 10.52623,-74.18608 10.54826,-74.2085 10.53628,-74.20772 10.50768,-74.19303 10.48448,-74.16095 10.42379,-74.15206 10.38243,-74.13235 10.31633,-74.10722 10.26647,-74.08325 10.23399,-74.05697 10.2081,-74.07266 10.17653,-74.06986 10.08702,-73.95797 10.08781,-73.92396 10.03073,-73.78429 10.04044,-73.66527 10.06473,-73.56781 10.09854,-73.42949 10.16264,-73.36033 10.22167,-73.25575 10.27059,-73.20963 10.33243,-73.17921 10.37892,-73.15683 10.41277,-73.09428 10.49541,-73.05812 10.52525,-73.00361 10.53788,-72.98582 10.57461,-72.96401 10.6188,-72.94392 10.68766,-72.86444 10.82358,-72.82684 10.8666,-72.81419 10.87238,-72.79973 10.88757,-72.7643 10.92263,-72.74875 10.95083,-72.73682 10.98735,-72.71079 11.01482,-72.70465 11.03543,-72.72382 11.05544,-72.72956 11.0718,-72.71722 11.07811,-72.68881 11.09218,-72.65982 11.10882,-72.64633 11.12346,-72.62567 11.14326,-72.60701 11.15646,-72.59496 11.1711,-72.59381 11.18373,-72.60386 11.19635,-72.62509 11.19521))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 244317 records from 172 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051595-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17096001
title: Synchronizing climate-carbon cycle heartbeats in the Phanerozoic vegetated icehouses

description: This is the newly measured gamma ray and carbon isotope data in the manuscript 'Synchronizing climate-carbon cycle heartbeats in the Phanerozoic vegetated icehouses'.
DOI: 10.5281/zenodo.17096002
title: Synchronizing climate-carbon cycle heartbeats in the Phanerozoic vegetated icehouses

description: This is the newly measured gamma ray and carbon isotope data in the manuscript 'Synchronizing climate-carbon cycle heartbeats in the Phanerozoic vegetated icehouses'.
DOI: 10.6084/m9.figshare.28559318.v3
title: Deconstructing Model Collapse in Software Engineering Tasks: A Multi-Granularity Empirical Study with Open-Source LLMs

description: <b>Large Language Models (LLMs)</b> have become indispensable for <b>automated software engineering (SE) tasks</b>, such as code generation, vulnerability detection, and code summarization. Yet, their long-term robustness is strongly shaped by training methodology. A particularly risky practice is <b>recursive self-training</b>, where models are repeatedly fine-tuned on their own generated outputs. While this strategy is often adopted to compensate for scarce human-annotated data, it carries the danger of <b>model collapse</b>—a degenerative process in which output quality, diversity, and reliability degrade across generations.This paper provides the <b>first multi-granularity empirical study</b> of model collapse in SE tasks. Using <b>open-source LLMs</b>—LLaMA-3 (1B, 3B, 8B, 70B), LLaMA-4 Scout (17B MoE), and Qwen-3 (0.5B, 1.8B, 7B, 14B, 72B)—we design controlled recursive training experiments across three benchmarks:<b>HumanEval</b> (code generation, evaluated with pass@1 and BLEU-4),<b>ReVeal</b> (vulnerability detection, evaluated with F1/precision/recall),<b>CodeSearchNet</b> (code summarization, evaluated with BLEU-4 and ROUGE-L).Models are trained under three regimes—<b>real-only</b>, <b>synthetic-only</b>, and <b>hybrid</b>—for up to <b>ten recursive generations</b>. We then analyze collapse dynamics at multiple granularities: <b>task-level degradation, data distribution drift (perplexity/entropy), and mitigation effectiveness</b>.Our findings show that <b>synthetic-only recursive training leads to sharp degradation</b>, especially in smaller models, while hybrid strategies and quality filtering significantly slow collapse but cannot eliminate it entirely. These results demonstrate that collapse in SE is not a simple extension of language collapse but a <b>domain-specific phenomenon</b>, driven by the structural and security-critical nature of code.This study contributes:A <b>systematic framework</b> to reveal model collapse in SE tasks.<b>Empirical evidence</b> across model scales, training regimes, and tasks.<b>Validated mitigation strategies</b> (hybrid training, filtering, and diversity preservation) with practical implications for building stable LLM pipelines in software engineering.
DOI: 10.15468/dl.2z4pbj
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 16 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051644-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.62tetq
title: Occurrence Download

description: A dataset containing 19 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Brachycephalus pernix"
 ]
}

 The dataset includes 19 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051643-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.djkr43
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Lithuania",
 "HasCoordinate is true"
 ]
}

 The dataset includes 63 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051647-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.62j27m
title: Occurrence Download

description: A dataset containing 39 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ribes uva-crispa",
 "Country is Latvia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 39 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051646-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zec545
title: Occurrence Download

description: A dataset containing 97797 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Malus sylvestris"
 ]
}

 The dataset includes 97797 records from 1135 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0051597-250827131500795/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13145/bacdive9604.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9605.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9606.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9607.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9608.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9609.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9610.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9611.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9612.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9613.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9614.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive9615.20240510.9
title: Myxococcus fulvus (Cohn 1875) Jahn 1911

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.