cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28559318.v6
title: Deconstructing Model Collapse in Software Engineering Tasks: A Multi-Granularity Empirical Study with Open-Source LLMs

description: <b>Large Language Models</b> (LLMs) have become indispensable for automated software engineering (SE) tasks, 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 recursive self-training, 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 first multi-granularity empirical study of model collapse in SE tasks. Using open-source LLMs — LLaMA-3 (1B, 3B, 8B, 70B) [1], LLaMA-4 Scout (17B MoE) [2], and Qwen-3 (0.6B, 1.7B, 8B, 14B, 30B) [3] — we design controlled recursive training experiments across three benchmarks:HumanEval: code generation, evaluated with Pass@1 and BLEU-4ReVeal: vulnerability detection, evaluated with F1/precision/recallCodeSearchNet: code summarization, evaluated with BLEU-4 and ROUGE-LModels are trained under three regimes — real-only, synthetic-only, and hybrid — for up to ten recursive generations. We then analyze collapse dynamics at multiple granularities: task-level degradation, data distribution drift (perplexity/entropy), and mitigation effectiveness.Our findings show that synthetic-only recursive training leads to sharp degradation, 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 domain-specific phenomenon, driven by the structural and security-critical nature of code.This study contributes:A systematic framework to reveal model collapse in SE tasksEmpirical evidence across model scales, training regimes, and tasksValidated mitigation strategies (hybrid training, filtering, and diversity preservation) with practical implications for building stable LLM pipelines in software engineering🔎 Reproducibility: All details necessary to reproduce our experiments — including preprocessing, hyperparameters, prompt templates, recursive training design, evaluation metrics, and mitigation strategies — are provided in the <b>README</b>.
DOI: 10.6084/m9.figshare.28559318.v7
title: When AI-Generated Data Dominates: Model Collapse in Software Engineering Tasks

description: Large Language Models (LLMs) have become indispensable for automated software engineering (SE) tasks, such as code generation, vulnerability detection, and code summarization. Yet, their long-term robustness is strongly shaped by the composition of training data. A particularly risky practice arises when models are extensively updated using AI-generated software artifacts. While such data reuse is often adopted to compensate for scarce human-annotated data, it introduces the risk of model collapse—a degenerative process in which output quality, diversity, and reliability progressively degrade as exposure to synthetic data increases.This paper provides the first multi-granularity empirical study of model collapse in SE tasks. Using open-source LLMs—including LLaMA-3 (1B, 3B, 8B, 70B)~\cite{Llama32}, LLaMA-4 Scout (17B MoE)~\cite{Llama4}, and Qwen-3 (0.6B, 1.7B, 8B, 14B, 30B)~\cite{Qwen3}—we design controlled model update experiments under evolving data compositions across three benchmarks:HumanEval: code generation, evaluated with Pass@1 and BLEU-4ReVeal: vulnerability detection, evaluated with F1, precision, and recallCodeSearchNet: code summarization, evaluated with BLEU-4 and ROUGE-LModels are updated under three data regimes—real-only, synthetic-only, and hybrid—over up to ten successive exposure stages. We then analyze collapse dynamics at multiple granularities, including task-level performance degradation, data distribution drift (perplexity and entropy), and mitigation effectiveness.Our findings show that exclusive reliance on AI-generated data leads to sharp performance degradation, particularly in smaller models, while hybrid data composition and quality-based filtering significantly slow collapse but cannot fully eliminate it. These results demonstrate that collapse in SE is not a simple extension of language-domain degradation, but a domain-specific phenomenon driven by the structural constraints, long-tail dependencies, and security-critical semantics of source code.This study contributes:A systematic framework for analyzing model collapse in software engineering tasksEmpirical evidence across model scales, data composition regimes, and SE task typesValidated mitigation strategies—including hybrid data anchoring, quality filtering, and diversity preservation—with practical implications for building stable LLM pipelines in software engineering🔎 Reproducibility: All details required to reproduce our experiments—including preprocessing steps, hyperparameters, prompt templates, dataset composition strategies, evaluation protocols, and mitigation mechanisms—are provided in the accompanying README.
DOI: 10.13021/orc2020/sp577t/3v6crp
title: WC3_2024-09-03_20-27-11_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/6cnudn
title: WC3_2024-09-03_21-12-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/jmvz4j
title: WC3_2024-09-03_21-35-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/s76nof
title: WC3_2024-09-03_21-54-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/d5iemc
title: WC3_2024-09-03_20-47-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/uzt9qx
title: WC3_2024-09-03_20-46-29_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/vmajjf
title: WC3_2024-09-03_21-52-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/nwxmfa
title: WC3_2024-09-03_20-49-29_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/vuig4o
title: WC3_2024-09-03_20-40-29_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/8wwnhq
title: WC3_2024-09-03_21-40-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/ikfboy
title: WC3_2024-09-03_22-11-07_chunk0001.bag
DOI: 10.15468/dl.29xnzd
title: Occurrence Download

description: A dataset containing 85 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-74.169 11.071024, -74.240585 11.065451, -74.3104 11.048868, -74.376718 11.021688, -74.437901 10.98458, -74.492439 10.938463, -74.538988 10.884475, -74.576403 10.823951, -74.60376599999999 10.758383, -74.62041000000001 10.689388, -74.62593200000001 10.618668, -74.62020200000001 10.547963, -74.603371 10.479015, -74.57585899999999 10.413519, -74.538349 10.353085, -74.491767 10.299199, -74.437262 10.253182, -74.376175 10.216165, -74.310005 10.189056, -74.24037800000001 10.17252, -74.169 10.166963, -74.097622 10.17252, -74.027995 10.189056, -73.961825 10.216165, -73.900738 10.253182, -73.846233 10.299199, -73.799651 10.353085, -73.762141 10.413519, -73.734629 10.479015, -73.717798 10.547963, -73.712068 10.618668, -73.71759 10.689388, -73.734234 10.758383, -73.76159699999999 10.823951, -73.799012 10.884475, -73.845561 10.938463, -73.900099 10.98458, -73.961282 11.021688, -74.02760000000001 11.048868, -74.097415 11.065451, -74.169 11.071024))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 85 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001029-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13021/orc2020/sp577t/9bjvvw
title: WC3_2024-09-03_21-33-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/1p6mvm
title: WC3_2024-09-03_20-59-59_chunk0000.bag
DOI: 10.34894/jtelxs
title: ERRHS_Industry

description: Electronic Repository of Russian Historical Statistics - data on INDUSTRY, cross-sections 1795, 1858, 1897, 1959, 2002, regional-level breakdown
DOI: 10.13021/orc2020/sp577t/j3ps38
title: WC3_2024-09-03_22-03-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/znrhwd
title: WC3_2024-09-03_21-58-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/ftr1i2
title: WC3_2024-09-03_21-43-51_chunk0000.bag
DOI: 10.15468/dl.rff9cv
title: Occurrence Download

description: A dataset containing 13 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Koeleria macrantha, Koeleria gracilis, Koeleria gracilis, Koeleria cristata, Koeleria cristata, Koeleria cristata, Koeleria cristata)",
 "Geometry POLYGON ((8.643 46.99, 14.1 46.92, 14.43 50.75, 8.534 50.83, 8.643 46.99))",
 "Year is greater than or equal to 2010",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "CoordinateUncertaintyInMeters is less than or equal to 50",
 "BasisOfRecord is one of (Observation, Human Observation, Machine Observation, Material sample)"
 ]
}

 The dataset includes 13 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001028-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13021/orc2020/sp577t/dhqs0j
title: WC3_2024-09-03_22-00-21_chunk0001.bag
DOI: 10.15468/dl.ks2s8x
title: Occurrence Download

description: A dataset containing 6203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "Country is one of (United States of America, Canada, Mexico)",
 "Year is greater than or equal to 1990",
 "TaxonKey is Heterotheca villosa"
 ]
}

 The dataset includes 6203 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001027-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13021/orc2020/sp577t/inz1tv
title: WC3_2024-09-03_22-09-37_chunk0000-1.bag
DOI: 10.13021/orc2020/sp577t/9aunj1
title: WC3_2024-09-03_20-28-41_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/hbtey6
title: WC3_2024-09-03_20-41-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/rmrvef
title: WC3_2024-09-03_21-49-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/xei0un
title: WC3_2024-09-03_20-50-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/9rciic
title: WC3_2024-09-03_21-21-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/kwzxvb
title: WC3_2024-09-03_21-55-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/bmlcqr
title: WC3_2024-09-03_20-52-29_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/qjoi23
title: WC3_2024-09-03_20-55-29_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/xkwlav
title: WC3_2024-09-03_21-36-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/9zuh0t
title: WC3_2024-09-03_22-01-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/fizgxu
title: WC3_2024-09-03_20-43-29_chunk0001.bag
DOI: 10.15468/dl.6mdjqa
title: Occurrence Download

description: A dataset containing 4017 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Epeorus pellucidus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4017 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001031-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13021/orc2020/sp577t/oabvad
title: WC3_2024-09-03_20-33-40_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/8okci3
title: WC3_2024-09-03_20-31-40_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/cez6br
title: WC3_2024-09-03_20-44-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/0aueco
title: WC3_2024-09-03_21-32-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/h0abfh
title: WC3_2024-09-03_20-38-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/tmggij
title: WC3_2024-09-03_20-38-10_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/gipfzo
title: WC3_2024-09-03_20-36-40_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/pisxuw
title: WC3_2024-09-03_21-45-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/ltuowy
title: WC3_2024-09-03_21-23-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/lrfp09
title: WC3_2024-09-03_21-57-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/yb4ntq
title: WC3_2024-09-03_21-15-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/uqz5a4
title: WC3_2024-09-03_21-17-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/ylsqoo
title: WC3_2024-09-03_21-46-51_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/hs1vfm
title: WC3_2024-09-03_21-24-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/mo4bch
title: WC3_2024-09-03_21-27-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/o4ylod
title: WC3_2024-09-03_20-58-29_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/9ezfe7
title: WC3_2024-09-03_21-20-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/29x9dk
title: WC3_2024-09-03_22-06-37_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/9mwg8s
title: WC3_2024-09-03_20-53-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/miutpq
title: WC3_2024-09-03_20-30-10_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/pel0q4
title: WC3_2024-09-03_21-30-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/w9bo3j
title: WC3_2024-09-03_21-48-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/zsfo4l
title: WC3_2024-09-03_20-56-59_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/fud1kl
title: WC3_2024-09-03_21-26-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/gt0bge
title: WC3_2024-09-03_21-18-43_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/e88mmn
title: WC3_2024-09-03_20-35-10_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/iktirk
title: WC3_2024-09-03_21-51-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/jphluv
title: WC3_2024-09-03_21-29-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/egrn09
title: WC3_2024-09-03_21-14-13_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/nxqr08
title: MC_2024-08-25_20-11-52_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/cbhume
title: MC_2024-08-25_19-50-39_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/ex4gdi
title: MC_2024-08-25_19-49-09_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/ojc6hq
title: MC_2024-08-25_20-05-31_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/stqt4c
title: MC_2024-08-25_19-24-36_chunk0008.bag
DOI: 10.13021/orc2020/sp577t/d34lcy
title: MC_2024-08-25_20-31-37_chunk0004.bag
DOI: 10.13021/orc2020/sp577t/qwtzgu
title: MC_2024-08-25_20-02-26_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/fbubjm
title: MC_2024-08-25_20-04-46_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/hfc7ip
title: MC_2024-08-25_20-14-07_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/vp0q0w
title: MC_2024-08-25_19-34-49_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/qp02wb
title: MC_2024-08-25_19-55-31_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/9sqewu
title: MC_2024-08-25_20-00-11_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/8lwjwf
title: MC_2024-08-25_19-25-21_chunk0009.bag
DOI: 10.13021/orc2020/sp577t/tcfim7
title: MC_2024-08-25_19-23-51_chunk0007.bag
DOI: 10.13021/orc2020/sp577t/fq2ecq
title: MC_2024-08-25_20-13-22_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/hw0pos
title: MC_2024-08-25_19-51-24_chunk0004.bag
DOI: 10.13021/orc2020/sp577t/mm3bix
title: MC_2024-08-25_20-00-56_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/7h5omj
title: MC_2024-08-25_19-37-04_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/mv4ega
title: MC_2024-08-25_20-32-22_chunk0005.bag
DOI: 10.13021/orc2020/sp577t/ocd278
title: MC_2024-08-25_20-01-41_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/qfown7
title: MC_2024-08-25_19-23-06_chunk0006.bag
DOI: 10.13021/orc2020/sp577t/mvkym5
title: MC_2024-08-25_20-33-07_chunk0006.bag
DOI: 10.13021/orc2020/sp577t/ogtpeq
title: MC_2024-08-25_19-46-04_chunk0004.bag
DOI: 10.13021/orc2020/sp577t/geu6ze
title: MC_2024-08-25_20-28-37_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/m0tj7k
title: MC_2024-08-25_19-20-51_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/y8tasj
title: MC_2024-08-25_19-52-09_chunk0005.bag
DOI: 10.13021/orc2020/sp577t/e80mkg
title: MC_2024-08-25_19-54-01_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/8oexrk
title: MC_2024-08-25_19-35-34_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/o4isu1
title: MC_2024-08-25_20-29-22_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/8edpvy
title: MC_2024-08-25_19-22-21_chunk0005.bag
DOI: 10.13021/orc2020/sp577t/5ydgai
title: MC_2024-08-25_19-43-49_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/sd4qwx
title: MC_2024-08-25_20-00-01_chunk0008.bag
DOI: 10.13021/orc2020/sp577t/2fvafa
title: MC_2024-08-25_19-46-49_chunk0005.bag
DOI: 10.13021/orc2020/sp577t/0dj3to
title: MC_2024-08-25_20-30-52_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/uknthl
title: MC_2024-08-25_20-33-52_chunk0007.bag
DOI: 10.13021/orc2020/sp577t/0aebsr
title: MC_2024-08-25_19-48-24_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/w7k9as
title: MC_2024-08-25_19-58-31_chunk0006.bag
DOI: 10.13021/orc2020/sp577t/i14yei
title: MC_2024-08-25_19-19-21_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/gvd5m7
title: MC_2024-08-25_19-21-36_chunk0004.bag
DOI: 10.13021/orc2020/sp577t/xr1qch
title: MC_2024-08-25_19-57-46_chunk0005.bag
DOI: 10.15468/dl.by93xs
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((102.694 3.233172, 102.623626 3.227603, 102.554987 3.211033, 102.489775 3.18387, 102.429598 3.146784, 102.375937 3.10069, 102.330114 3.046722, 102.293258 2.986212, 102.266274 2.92065, 102.249826 2.851651, 102.244316 2.780914, 102.249879 2.710181, 102.266375 2.641194, 102.293396 2.57565, 102.330277 2.515164, 102.376108 2.461222, 102.42976 2.415154, 102.489914 2.378092, 102.555088 2.350948, 102.623679 2.334389, 102.694 2.328824, 102.764321 2.334389, 102.832912 2.350948, 102.898086 2.378092, 102.95824 2.415154, 103.011892 2.461222, 103.057723 2.515164, 103.094604 2.57565, 103.121625 2.641194, 103.138121 2.710181, 103.143684 2.780914, 103.138174 2.851651, 103.121726 2.92065, 103.094742 2.986212, 103.057886 3.046722, 103.012063 3.10069, 102.958402 3.146784, 102.898225 3.18387, 102.833013 3.211033, 102.764374 3.227603, 102.694 3.233172))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001032-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13021/orc2020/sp577t/qnm824
title: MC_2024-08-25_19-36-19_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/xeqpm0
title: MC_2024-08-25_19-45-19_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/ieirih
title: MC_2024-08-25_19-44-34_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/yjubj0
title: MC_2024-08-25_19-54-46_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/if69zy
title: MC_2024-08-25_19-59-16_chunk0007.bag
DOI: 10.13021/orc2020/sp577t/9yrzjl
title: MC_2024-08-25_20-34-37_chunk0008.bag
DOI: 10.13021/orc2020/sp577t/rhscld
title: MC_2024-08-25_20-30-07_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/5gi3vo
title: MC_2024-08-25_19-20-06_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/ylq5zl
title: MC_2024-08-25_19-43-04_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/nb2pyc
title: MC_2024-08-25_20-12-37_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/o2by40
title: MC_2024-08-25_19-18-36_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/7sfpkg
title: MC_2024-08-25_20-35-22_chunk0009.bag
DOI: 10.13021/orc2020/sp577t/7ocrd3
title: MC_2024-08-25_19-37-49_chunk0004.bag
DOI: 10.13021/orc2020/sp577t/kesjan
title: MC_2024-08-25_19-49-54_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/csiyie
title: MC_2024-08-25_19-56-16_chunk0003.bag
DOI: 10.13021/orc2020/sp577t/fp2j54
title: MC_2024-08-25_19-57-01_chunk0004.bag
DOI: 10.13021/orc2020/sp577t/brkykg
title: BL_2024-09-04_20-38-54_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/xklpvg
title: BL_2024-09-04_19-47-54_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/zk9b8w
title: BL_2024-09-04_19-45-36_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/w8klq9
title: BL_2024-09-04_20-50-54_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/hjrbdo
title: BL_2024-09-04_20-15-54_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/h96m9i
title: BL_2024-09-04_21-23-54_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/vhbk6t
title: BL_2024-09-04_20-37-54_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/7b6bsv
title: BL_2024-09-04_19-31-36_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/7lln3o
title: BL_2024-09-04_20-42-54_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/2bryqx
title: BL_2024-09-04_21-02-54_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/gyjdml
title: BL_2024-09-04_21-21-54_chunk0002.bag
DOI: 10.13021/orc2020/sp577t/ns0mv4
title: BL_2024-09-04_19-09-17_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/7mucfo
title: BL_2024-09-04_19-58-54_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/jvwnov
title: BL_2024-09-04_20-35-54_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/lbd4bj
title: BL_2024-09-04_19-41-36_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/fw96hd
title: BL_2024-09-04_21-31-54_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/o91qnf
title: BL_2024-09-04_19-22-16_chunk0001.bag
DOI: 10.13021/orc2020/sp577t/1slzlz
title: BL_2024-09-04_19-55-54_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/63ktgh
title: BL_2024-09-04_21-01-54_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/7jhmrz
title: BL_2024-09-04_20-31-54_chunk0000.bag
DOI: 10.15468/dl.ne7j46
title: Occurrence Download

description: A dataset containing 358 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((-74.215 11.123022, -74.286598 11.117449, -74.356425 11.100867, -74.422755 11.073686, -74.483948 11.036578, -74.538495 10.990461, -74.585052 10.936473, -74.622473 10.875949, -74.649841 10.810381, -74.666487 10.741386, -74.672009 10.670666, -74.66627800000001 10.599962, -74.649444 10.531014, -74.621927 10.465519, -74.58441000000001 10.405085, -74.53782 10.351199, -74.483306 10.305183, -74.422209 10.268166, -74.35602799999999 10.241058, -74.286389 10.224522, -74.215 10.218965, -74.14361100000001 10.224522, -74.073972 10.241058, -74.007791 10.268166, -73.94669399999999 10.305183, -73.89218 10.351199, -73.84559 10.405085, -73.80807299999999 10.465519, -73.780556 10.531014, -73.763722 10.599962, -73.757991 10.670666, -73.763513 10.741386, -73.780159 10.810381, -73.80752699999999 10.875949, -73.844948 10.936473, -73.891505 10.990461, -73.94605199999999 11.036578, -74.007245 11.073686, -74.07357500000001 11.100867, -74.14340199999999 11.117449, -74.215 11.123022))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 358 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001033-260119153935675/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13021/orc2020/sp577t/esvdii
title: BL_2024-09-04_20-13-54_chunk0000.bag
DOI: 10.13021/orc2020/sp577t/hjw3vn
title: BL_2024-09-04_20-01-54_chunk0000.bag