cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.z8mhx8
title: Occurrence Download

description: A dataset containing 43 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Ziphius cavirostris G.Cuvier, 1823"
 ]
}

 The dataset includes 43 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038161-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zk6426
title: Occurrence Download

description: A dataset containing 1749 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oenothera fruticosa L.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 1749 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038162-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7jbyyc
title: Occurrence Download

description: A dataset containing 38 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vitex lehmbachii Gürke"
 ]
}

 The dataset includes 38 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038163-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4b4uph
title: Occurrence Download

description: A dataset containing 146 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oenothera tetragona Roth)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 146 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038164-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12971085
title: Open Access Author Contracts Public Dataset

description: This dataset includes the deidentified respondent demographic data of a survey distributed to open access journal editors from journals registered in the Directory of Open Access Journals  (DOAJ). In addition to respondent demographics, the dataset includes the qualitative analyses results conducted by the authors about author's contracts which were solicited as part of the survey. These include basic elements of the contracts provided by the respondents, other terms covered, sharing restriction, and contradictory terms. The contracts themselves or direct text from the contracts is not included in the dataset.
DOI: 10.5281/zenodo.12971086
title: Open Access Author Contracts Public Dataset

description: This dataset includes the deidentified respondent demographic data of a survey distributed to open access journal editors from journals registered in the Directory of Open Access Journals  (DOAJ). In addition to respondent demographics, the dataset includes the qualitative analyses results conducted by the authors about author's contracts which were solicited as part of the survey. These include basic elements of the contracts provided by the respondents, other terms covered, sharing restriction, and contradictory terms. The contracts themselves or direct text from the contracts is not included in the dataset.
DOI: 10.15468/dl.b7jbsy
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Stachys pseudohumifusa subsp. saxeri Y.B.Harv."
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038165-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tmdvvc
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vitex yaundensis Gürke"
 ]
}

 The dataset includes 25 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038168-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqangw
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "DatasetKey is Sistematización del Herbario Nacional Forestal Biól Luciano Vela Gálvez",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Cedrela odorata L."
 ]
}

 The dataset includes 31 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038167-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12822632
title: PyBugHive offline dump (part 2)

description: This is the part 2 of the offline version of PyBugHive. More information can be found here.

 

PyBugHive is a benchmark of 149 real, manually validated bugs from 11 Python projects.

Detailed Bugs

Each entry in our database contains the summary of the bug report, the corresponding patch, and the test cases that expose the given bug and the necessary commands to setup the environment and execute the test cases.

Rich Interface

PyBugHive features a rich command line interface for accessing the buggy and fixed versions of the programs and provides the abstraction for executing the corresponding test cases. The interface facilitates highly reproducible empirical research and tool comparisons in fields such as testing, automated program repair, or bug prediction.
DOI: 10.5281/zenodo.12822633
title: PyBugHive offline dump (part 2)

description: This is the part 2 of the offline version of PyBugHive. More information can be found here.

 

PyBugHive is a benchmark of 149 real, manually validated bugs from 11 Python projects.

Detailed Bugs

Each entry in our database contains the summary of the bug report, the corresponding patch, and the test cases that expose the given bug and the necessary commands to setup the environment and execute the test cases.

Rich Interface

PyBugHive features a rich command line interface for accessing the buggy and fixed versions of the programs and provides the abstraction for executing the corresponding test cases. The interface facilitates highly reproducible empirical research and tool comparisons in fields such as testing, automated program repair, or bug prediction.
DOI: 10.6084/m9.figshare.25752408
title: Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

description: For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.
DOI: 10.6084/m9.figshare.25752408.v1
title: Rank-based regression with bootstrapping and the least square regression for analysis of small sample interrupted time-series data: simulation studies and application to illegal organ transplants

description: For interrupted time series with small samples and outliers, the standard least squares regression for estimating intervention effects can be biased when the assumption of normality is violated. This article proposes a rank-based regression method combined with bootstrapping to address outliers and non-normality. The proposed method minimizes the Jaeckel dispersion function based on ranked residuals, offering robustness against outliers, skewed error distributions, and correlated errors. Simulation studies and a real-world example compare the performance of the proposed method with the least squares method under various conditions. Both produce unbiased estimates for the intervention effects under normality. However, with non-normal errors, outliers, and correlated errors, the rank-based bootstrapping method outperforms least squares producing narrower confidence intervals, higher efficiency, lower type I error rates, and greater power. Application to the Istanbul declaration on illegal organ activities confirms the improved robustness and precision of the rank-based method with bootstrapping over least squares.
DOI: 10.6084/m9.figshare.25621517
title: A new online learning algorithm for streaming data and decision support with a Bayesian approach

description: With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic.
DOI: 10.6084/m9.figshare.25621517.v1
title: A new online learning algorithm for streaming data and decision support with a Bayesian approach

description: With the new revolution in data technology, many types of streaming data are automatically generated in our living environment. The vast amount of information carried by this streaming data demands decision support from new online learning algorithms. In this paper, we propose a new online learning algorithm to monitor changes in stream data/system status and provide automatic decision support using streaming data. The new online learning algorithm we propose consists of two components. Firstly, we adapt an existing Bayesian approach into an online learning algorithm for change detection, which serves as Component A. Then, we integrate this algorithm with a modified online learning method from repeated games (Play against Random Past), which forms Component B. Theoretical support for Component B is provided with a mathematical proof at the end. We demonstrate the algorithm's performance in this paper through simulations using both artificial data from a random process and data from the 2009 H1N1 pandemic.
DOI: 10.5281/zenodo.12800595
title: PyBugHive offline dump (part 1)

description: This is the part 1 of the offline version of PyBugHive. More information can be found here.

 

PyBugHive is a benchmark of 149 real, manually validated bugs from 11 Python projects.

Detailed Bugs

Each entry in our database contains the summary of the bug report, the corresponding patch, and the test cases that expose the given bug and the necessary commands to setup the environment and execute the test cases.

Rich Interface

PyBugHive features a rich command line interface for accessing the buggy and fixed versions of the programs and provides the abstraction for executing the corresponding test cases. The interface facilitates highly reproducible empirical research and tool comparisons in fields such as testing, automated program repair, or bug prediction.
DOI: 10.5281/zenodo.8339476
title: PyBugHive offline dump (part 1)

description: This is the part 1 of the offline version of PyBugHive. More information can be found here.

 

PyBugHive is a benchmark of 149 real, manually validated bugs from 11 Python projects.

Detailed Bugs

Each entry in our database contains the summary of the bug report, the corresponding patch, and the test cases that expose the given bug and the necessary commands to setup the environment and execute the test cases.

Rich Interface

PyBugHive features a rich command line interface for accessing the buggy and fixed versions of the programs and provides the abstraction for executing the corresponding test cases. The interface facilitates highly reproducible empirical research and tool comparisons in fields such as testing, automated program repair, or bug prediction.
DOI: 10.5281/zenodo.8339477
title: PyBugHive offline dump (part 1)

description: This is the part 1 of the offline version of PyBugHive. More information can be found here. <strong>PyBugHive</strong> is a benchmark of <strong>105</strong> <strong>real, manually validated bugs</strong> from <strong>10</strong> Python projects. Detailed Bugs Each entry in our database contains the summary of the <strong>bug report</strong>, the <strong>corresponding patch</strong>, and the <strong>test cases</strong> that expose the given bug and the necessary commands to <strong>setup the environment</strong> and <strong>execute the test cases</strong>. Rich Interface <strong>PyBugHive</strong> features a <strong>rich command line interface</strong> for <strong>accessing the buggy and fixed versions</strong> of the programs and <strong>provides the abstraction</strong> for executing the corresponding test cases. The interface facilitates highly reproducible <strong>empirical research and tool comparisons</strong> in fields such as <strong>testing, automated program repair, or bug prediction</strong>.
DOI: 10.15468/dl.zmakb4
title: Occurrence Download

description: A dataset containing 40965 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oenothera glazioviana Micheli)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 40965 records from 478 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038166-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gybgts
title: Occurrence Download

description: A dataset containing 291 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crateranthus talbotii Baker f."
 ]
}

 The dataset includes 291 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038169-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vhzyfs
title: Occurrence Download

description: A dataset containing 113 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oenothera podocarpa (Wooton & Standl.) Krakos & W.L.Wagner)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 113 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038170-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6z8xn
title: Occurrence Download

description: A dataset containing 264 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Brazil",
 "TaxonKey is Colobosaura modesta (Reinhardt & Lütken, 1862)"
 ]
}

 The dataset includes 264 records from 20 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038172-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.va6sn7
title: Occurrence Download

description: A dataset containing 336 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Oenothera humifusa Nutt.)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "HasGeospatialIssue is false",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 336 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038173-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25345/c5jh3dd7h
title: MassIVE MSV000095454 - P. fluorescens in Succinate Minimal Media, Iron Infusion
title: Pseudomonas fluorescens FW300-N2C3 was cultured in succinate minimal media (SMM) and analyzed for metabolites on LCMS with iron infusions of either 2 mM or 500 micromolar iron.
DOI: 10.25574/01122
title: Chandra X-ray Observatory ObsId 01122
DOI: 10.18739/a23n20g53
title: Ice core methanesulfonic acid from the Begguya (Mount Hunter) plateau, Denali National Park, Alaska, 2013

description: An industrial-era drop in Greenland ice core methanesulfonic acid is thought to herald a collapse in North Atlantic marine phytoplankton stocks related to a weakening of the Atlantic meridional overturning circulation. In contrast, stable levels of marine biogenic sulfur production contradict this interpretation, and point to changes in atmospheric oxidation as a potential cause of the methanesulfonic acid decline. However, the impact of oxidation on methanesulfonic acid production has not been quantified, nor has this hypothesis been rigorously tested. Here we present a multi-century methanesulfonic acid record from the Denali, Alaska, ice core, which shows a methanesulfonic acid decline similar in magnitude but delayed by 93 years relative to the Greenland record. Box model results using updated dimethyl sulfide oxidation pathways indicate that oxidation by pollution-driven nitrate radicals has suppressed atmospheric methanesulfonic acid production, explaining most, if not all, of Denali’s and Greenland’s methanesulfonic acid declines without requiring a change in phytoplankton production. The delayed timing of the North Pacific methanesulfonic acid decline, relative to the North Atlantic, reflects the distinct history of industrialization in upwind regions and is consistent with the Denali and Greenland ice core nitrate records. These results demonstrate that multidecadal trends in industrial-era Arctic ice core methanesulfonic acid reflect rising anthropogenic pollution rather than declining marine primary production.
DOI: 10.25574/01131
title: Chandra X-ray Observatory ObsId 01131
DOI: 10.15468/dl.uc5hb2
title: Occurrence Download

description: A dataset containing 2697521 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Crambidae",
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 }
 ]
}

 The dataset includes 2697521 records from 1854 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038142-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zfx4qz
title: Occurrence Download

description: A dataset listing the 118096 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "BasisOfRecord is one of (Human Observation, Observation, Occurrence evidence, Machine Observation, Material sample, Specimen)"
 ]
}

 The dataset's 118096 records were derived from 2265 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0355262-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f6q43e
title: Occurrence Download

description: A dataset containing 203 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Liliopsida)",
 "Year is greater than or equal to 2012",
 "Year is less than or equal to 2022",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((143.6593032 -21.879376, 143.5963257 -22.145281, 143.7572681 -22.313221, 143.6942907 -22.5231459, 143.5123557 -22.7470659, 143.2464508 -22.8730208, 142.9595533 -22.8380334, 142.7846159 -22.6630959, 142.6376684 -22.6980834, 142.5956834 -22.97098579999999, 142.6516634 -23.2648808, 142.7566259 -23.5307857, 142.7916133 -23.6287507, 142.7356334 -23.8876581, 142.8615883 -24.0206106, 142.6376684 -24.2305356, 142.6306709 -24.4264655, 142.5187109 -24.629393, 142.5501996 -24.9862654, 142.8930771 -24.9582754, 142.8580896 -25.1751979, 143.3898995 -25.2171829, 143.6837944 -25.3641303, 144.0266718 -25.7349978, 144.3695493 -25.46909279999999, 144.5514842 -25.2451729, 144.6214592 -24.71336299999999, 144.8243867 -24.9302854, 145.1322766 -25.1542054, 145.4121766 -25.3151478, 145.671084 -25.1751979, 145.8040365 -25.00026039999999, 146.2238864 -24.9722704, 146.5247788 -24.95127789999999, 146.6717263 -24.7903354, 146.7277063 -24.643388, 146.6297413 -24.52443049999999, 146.5886749 -24.4313019, 146.4583026 -24.4376615, 146.3883276 -24.1927491, 146.4163176 -23.8708641, 146.2973601 -23.5419817, 146.1574102 -23.0451593, 146.0454502 -22.6253094, 146.1224227 -22.3174195, 146.0664427 -22.1774695, 145.7445577 -22.1074945, 145.8075352 -21.8625821, 145.6256003 -21.8135996, 145.5276353 -21.6316646, 145.4086778 -21.45672709999999, 145.1707629 -21.0438747, 145.1799372 -20.77781999999999, 145.1287779 -20.7569773, 144.8768679 -20.5190623, 144.7719054 -20.6408188, 144.582973 -20.7457813, 144.4220305 -20.8717363, 144.457018 -21.0886587, 144.2680855 -21.1796262, 144.2400955 -21.4035461, 144.0791531 -21.4525286, 143.9601956 -21.3615612, 143.8202456 -21.3755562, 143.5963257 -21.4875161, 143.6593032 -21.879376))"
 ]
}

 The dataset includes 203 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0171557-220831081235567/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.