cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.4p6gx5
title: Occurrence Download

description: A dataset containing 1329 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Enterolobium gummiferum (Mart.) J.F.Macbr., Enterolobium ellipticum Benth., Pithecellobium gummiferum Mart.)"
 ]
}

 The dataset includes 1329 records from 124 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003794-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dhurg3
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((52.49330 32.48125,77.22891 32.48125,77.22891 42.38075,52.49330 42.38075,52.49330 32.48125))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Olimarabidopsis pumila (Stephan) Al-Shehbaz, O'Kane & R.A.Price"
 ]
}

 The dataset includes 76 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003795-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wcwwf2
title: Occurrence Download

description: A dataset containing 1293 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Senegalia loretensis (J.F.Macbr.) Seigler & Ebinger, Acacia loretensis J.F.Macbr.)"
 ]
}

 The dataset includes 1293 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003796-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.973g9x
title: Occurrence Download

description: A dataset containing 914 species occurrences available in GBIF matching the query:
{
 "PublishingOrg" : [
 "is e2c03d68-4b52-46c4-96c0-44f11a25ebc5"
 ]
}

 The dataset includes 914 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003797-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rzqdj5
title: Occurrence Download

description: A dataset containing 124399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Ireland, United Kingdom of Great Britain and Northern Ireland, Portugal, Spain, Italy, Greece, Svalbard and Jan Mayen, Denmark, Norway, Sweden, Finland, Estonia, Latvia, Lithuania, Belgium, Netherlands, France, Germany, Switzerland, Austria, Türkiye, Russian Federation, Belarus, Ukraine, Romania, Bulgaria, North Macedonia, Albania, Montenegro, Serbia, Kosovo, Slovenia, Bosnia and Herzegovina, Poland, Czechia, Slovakia, Hungary)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Berula erecta (Huds.) Coville"
 ]
}

 The dataset includes 124399 records from 564 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003782-240626123714530/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.7871963
title: Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels

description: This article argues that researchers do not need to completely abandon the <i>p</i>-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of <i>α</i>, the probability of rejecting a true null hypothesis, and <i>β</i>, the probability of failing to reject a false null, instead of fixing <i>α</i> and minimizing <i>β</i>. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests.
DOI: 10.6084/m9.figshare.7871963.v2
title: Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels

description: This article argues that researchers do not need to completely abandon the <i>p</i>-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of <i>α</i>, the probability of rejecting a true null hypothesis, and <i>β</i>, the probability of failing to reject a false null, instead of fixing <i>α</i> and minimizing <i>β</i>. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests.
DOI: 10.6084/m9.figshare.7871939
title: The False Positive Risk: A Proposal Concerning What to Do About <i>p</i>-Values

description: It is widely acknowledged that the biomedical literature suffers from a surfeit of false positive results. Part of the reason for this is the persistence of the myth that observation of <i>p</i> &lt; 0.05 is sufficient justification to claim that you have made a discovery. It is hopeless to expect users to change their reliance on <i>p</i>-values unless they are offered an alternative way of judging the reliability of their conclusions. If the alternative method is to have a chance of being adopted widely, it will have to be easy to understand and to calculate. One such proposal is based on calculation of false positive risk(FPR). It is suggested that <i>p</i>-values and confidence intervals should continue to be given, but that they should be supplemented by a single additional number that conveys the strength of the evidence better than the <i>p</i>-value. This number could be the minimum FPR (that calculated on the assumption of a prior probability of 0.5, the largest value that can be assumed in the absence of hard prior data). Alternatively one could specify the prior probability that it would be necessary to believe in order to achieve an FPR of, say, 0.05.
DOI: 10.6084/m9.figshare.7871939.v1
title: The False Positive Risk: A Proposal Concerning What to Do About <i>p</i>-Values

description: It is widely acknowledged that the biomedical literature suffers from a surfeit of false positive results. Part of the reason for this is the persistence of the myth that observation of <i>p</i> &lt; 0.05 is sufficient justification to claim that you have made a discovery. It is hopeless to expect users to change their reliance on <i>p</i>-values unless they are offered an alternative way of judging the reliability of their conclusions. If the alternative method is to have a chance of being adopted widely, it will have to be easy to understand and to calculate. One such proposal is based on calculation of false positive risk(FPR). It is suggested that <i>p</i>-values and confidence intervals should continue to be given, but that they should be supplemented by a single additional number that conveys the strength of the evidence better than the <i>p</i>-value. This number could be the minimum FPR (that calculated on the assumption of a prior probability of 0.5, the largest value that can be assumed in the absence of hard prior data). Alternatively one could specify the prior probability that it would be necessary to believe in order to achieve an FPR of, say, 0.05.
DOI: 10.6084/m9.figshare.7871957
title: Trial Size for Near-Optimal Choice Between Surveillance and Aggressive Treatment: Reconsidering MSLT-II

description: A convention in designing randomized clinical trials has been to choose sample sizes that yield specified statistical power when testing hypotheses about treatment response. Manski and Tetenov recently critiqued this convention and proposed enrollment of sufficiently many subjects to enable <i>near-optimal</i> treatment choices. This article develops a refined version of that analysis applicable to trials comparing aggressive treatment of patients with surveillance. The need for a refined analysis arises because the earlier work assumed that there is only a primary health outcome of interest, without secondary outcomes. An important aspect of choice between surveillance and aggressive treatment is that the latter may have side effects. One should then consider how the primary outcome and side effects jointly determine patient welfare. This requires new analysis of sample design. As a case study, we reconsider a trial comparing nodal observation and lymph node dissection when treating patients with cutaneous melanoma. Using a statistical power calculation, the investigators assigned 971 patients to dissection and 968 to observation. We conclude that assigning 244 patients to each option would yield findings that enable suitably near-optimal treatment choice. Thus, a much smaller sample size would have sufficed to inform clinical practice.
DOI: 10.6084/m9.figshare.7871957.v1
title: Trial Size for Near-Optimal Choice Between Surveillance and Aggressive Treatment: Reconsidering MSLT-II

description: A convention in designing randomized clinical trials has been to choose sample sizes that yield specified statistical power when testing hypotheses about treatment response. Manski and Tetenov recently critiqued this convention and proposed enrollment of sufficiently many subjects to enable <i>near-optimal</i> treatment choices. This article develops a refined version of that analysis applicable to trials comparing aggressive treatment of patients with surveillance. The need for a refined analysis arises because the earlier work assumed that there is only a primary health outcome of interest, without secondary outcomes. An important aspect of choice between surveillance and aggressive treatment is that the latter may have side effects. One should then consider how the primary outcome and side effects jointly determine patient welfare. This requires new analysis of sample design. As a case study, we reconsider a trial comparing nodal observation and lymph node dissection when treating patients with cutaneous melanoma. Using a statistical power calculation, the investigators assigned 971 patients to dissection and 968 to observation. We conclude that assigning 244 patients to each option would yield findings that enable suitably near-optimal treatment choice. Thus, a much smaller sample size would have sufficed to inform clinical practice.
DOI: 10.6084/m9.figshare.7871960
title: A Proposed Hybrid Effect Size Plus <i>p</i>-Value Criterion: Empirical Evidence Supporting its Use

description: When the editors of Basic and Applied Social Psychology effectively banned the use of null hypothesis significance testing (NHST) from articles published in their journal, it set off a fire-storm of discussions both supporting the decision and defending the utility of NHST in scientific research. At the heart of NHST is the <i>p</i>-value which is the probability of obtaining an effect equal to or more extreme than the one observed in the sample data, given the null hypothesis and other model assumptions. Although this is conceptually different from the probability of the null hypothesis being true, given the sample, <i>p</i>-values nonetheless <i>can</i> provide evidential information, toward making an inference about a parameter. Applying a 10,000-case simulation described in this article, the authors found that <i>p</i>-values’ inferential signals to either reject or not reject a null hypothesis about the mean (<i>α</i> = 0.05) were consistent for almost 70% of the cases with the parameter’s true location for the sampled-from population. Success increases if a hybrid decision criterion, <i>minimum effect size plus p-value</i> (MESP), is used. Here, rejecting the null also requires the difference of the observed statistic from the exact null to be meaningfully large or practically significant, in the researcher’s judgment and experience. The simulation compares performances of several methods: from <i>p</i>-value and/or effect size-based, to confidence-interval based, under various conditions of true location of the mean, test power, and comparative sizes of the meaningful distance and population variability. For <i>any</i> inference procedure that outputs a binary indicator, like flagging whether a <i>p</i>-value is significant, the output of one single experiment is <i>not</i> sufficient evidence for a definitive conclusion. Yet, if a tool like MESP generates a relatively reliable signal and is used knowledgeably as part of a research <i>process</i>, it can provide useful information.
DOI: 10.6084/m9.figshare.7871963.v1
title: Blending Bayesian and Classical Tools to Define Optimal Sample-Size-Dependent Significance Levels

description: This article argues that researchers do not need to completely abandon the <i>p</i>-value, the best-known significance index, but should instead stop using significance levels that do not depend on sample sizes. A testing procedure is developed using a mixture of frequentist and Bayesian tools, with a significance level that is a function of sample size, obtained from a generalized form of the Neyman–Pearson Lemma that minimizes a linear combination of <i>α</i>, the probability of rejecting a true null hypothesis, and <i>β</i>, the probability of failing to reject a false null, instead of fixing <i>α</i> and minimizing <i>β</i>. The resulting hypothesis tests do not violate the Likelihood Principle and do not require any constraints on the dimensionalities of the sample space and parameter space. The procedure includes an ordering of the entire sample space and uses predictive probability (density) functions, allowing for testing of both simple and compound hypotheses. Accessible examples are presented to highlight specific characteristics of the new tests.
DOI: 10.6084/m9.figshare.7871960.v1
title: A Proposed Hybrid Effect Size Plus <i>p</i>-Value Criterion: Empirical Evidence Supporting its Use

description: When the editors of Basic and Applied Social Psychology effectively banned the use of null hypothesis significance testing (NHST) from articles published in their journal, it set off a fire-storm of discussions both supporting the decision and defending the utility of NHST in scientific research. At the heart of NHST is the <i>p</i>-value which is the probability of obtaining an effect equal to or more extreme than the one observed in the sample data, given the null hypothesis and other model assumptions. Although this is conceptually different from the probability of the null hypothesis being true, given the sample, <i>p</i>-values nonetheless <i>can</i> provide evidential information, toward making an inference about a parameter. Applying a 10,000-case simulation described in this article, the authors found that <i>p</i>-values’ inferential signals to either reject or not reject a null hypothesis about the mean (<i>α</i> = 0.05) were consistent for almost 70% of the cases with the parameter’s true location for the sampled-from population. Success increases if a hybrid decision criterion, <i>minimum effect size plus p-value</i> (MESP), is used. Here, rejecting the null also requires the difference of the observed statistic from the exact null to be meaningfully large or practically significant, in the researcher’s judgment and experience. The simulation compares performances of several methods: from <i>p</i>-value and/or effect size-based, to confidence-interval based, under various conditions of true location of the mean, test power, and comparative sizes of the meaningful distance and population variability. For <i>any</i> inference procedure that outputs a binary indicator, like flagging whether a <i>p</i>-value is significant, the output of one single experiment is <i>not</i> sufficient evidence for a definitive conclusion. Yet, if a tool like MESP generates a relatively reliable signal and is used knowledgeably as part of a research <i>process</i>, it can provide useful information.
DOI: 10.6084/m9.figshare.7871948
title: The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making

description: This article resulted from our participation in the session on the “role of expert opinion and judgment in statistical inference” at the October 2017 ASA Symposium on Statistical Inference. We present a strong, unified statement on roles of expert judgment in statistics with processes for obtaining input, whether from a Bayesian or frequentist perspective. Topics include the role of subjectivity in the cycle of scientific inference and decisions, followed by a clinical trial and a greenhouse gas emissions case study that illustrate the role of judgments and the importance of basing them on objective information and a comprehensive uncertainty assessment. We close with a call for increased proactivity and involvement of statisticians in study conceptualization, design, conduct, analysis, and communication.
DOI: 10.6084/m9.figshare.7871948.v2
title: The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making

description: This article resulted from our participation in the session on the “role of expert opinion and judgment in statistical inference” at the October 2017 ASA Symposium on Statistical Inference. We present a strong, unified statement on roles of expert judgment in statistics with processes for obtaining input, whether from a Bayesian or frequentist perspective. Topics include the role of subjectivity in the cycle of scientific inference and decisions, followed by a clinical trial and a greenhouse gas emissions case study that illustrate the role of judgments and the importance of basing them on objective information and a comprehensive uncertainty assessment. We close with a call for increased proactivity and involvement of statisticians in study conceptualization, design, conduct, analysis, and communication.
DOI: 10.6084/m9.figshare.7871948.v1
title: The Role of Expert Judgment in Statistical Inference and Evidence-Based Decision-Making

description: This article resulted from our participation in the session on the “role of expert opinion and judgment in statistical inference” at the October 2017 ASA Symposium on Statistical Inference. We present a strong, unified statement on roles of expert judgment in statistics with processes for obtaining input, whether from a Bayesian or frequentist perspective. Topics include the role of subjectivity in the cycle of scientific inference and decisions, followed by a clinical trial and a greenhouse gas emissions case study that illustrate the role of judgments and the importance of basing them on objective information and a comprehensive uncertainty assessment. We close with a call for increased proactivity and involvement of statisticians in study conceptualization, design, conduct, analysis, and communication.
DOI: 10.15468/dl.n7fsfw
title: Occurrence Download

description: A dataset containing 51 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Byrsonima piresii W.R.Anderson)"
 ]
}

 The dataset includes 51 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003798-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cndbnh
title: Occurrence Download

description: A dataset containing 14852 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is VERTEBRADOS DE VALOR ESPECIAL EN ÁREAS PROTEGIDAS DE LA ARGENTINA"
 ]
}

 The dataset includes 14852 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003799-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wryu7n
title: Occurrence Download

description: A dataset containing 2379 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Alchornea discolor Poepp., Alchornea schomburgkii Klotzsch, Alchornea brachygyne Pax & K.Hoffm., Alchornea gardneri Müll.Arg., Alchornea glaziovii Pax & K.Hoffm.)"
 ]
}

 The dataset includes 2379 records from 178 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003800-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pm6wuf
title: Occurrence Download

description: A dataset containing 1358 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "PublishingCountry is Colombia",
 {
 "and" : [
 "Country is Colombia",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 }
 ]
 },
 {
 "and" : [
 "Year 2000-2024",
 "GadmGid is one of (COL.15.45_2)"
 ]
 }
 ]
}

 The dataset includes 1358 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003801-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hjua95
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alyssopsis mollis (Jacq.) O.E.Schulz"
 ]
}

 The dataset includes 79 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003802-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vjxdfv
title: Occurrence Download

description: A dataset containing 55 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Chiasmia azataria (Swinhoe, 1893)"
 ]
}

 The dataset includes 55 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003805-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzhddx
title: Occurrence Download

description: A dataset containing 1658 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Amanoa guianensis Aubl., Amanoa bracteata Rich. ex Baill., Amanoa grandiflora (Müll.Arg.) Müll.Arg., Amanoa guianensis var. genuina Müll.Arg., Amanoa guianensis var. grandiflora Müll.Arg., Amanoa guianensis var. oblonga Benth. ex Baill., Amanoa guianensis var. poeppigii Müll.Arg., Amanoa macrocarpa Cuatrec., Amanoa potamophila Croizat, Amanoa guyanensis var. poeppigii Aubl.)"
 ]
}

 The dataset includes 1658 records from 128 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0003804-240626123714530/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.