cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17390559
title: Base de datos para el análisis de la presencia del término "Diseño Universal para el Aprendizaje" en la normativa de currículum derivada de la LOMLOE de Primaria de las comunidades autónomas de España.

description: Base de datos para el análisis de la presencia del término "Diseño Universal para el Aprendizaje" en la normativa de currículum derivada de la LOMLOE de la etapa de Educación Primaria de las comunidades autónomas de España.

Forma parte del trabajo realizado en el marco de Proyecto "El Diseño Universal para el Aprendizaje: aportaciones a las prácticas curriculares y a las culturas inclusiva y digital de los centros".

Entidad financiadora: Ministerio de Ciencia e Innovación, España.

Convocatoria 2020 de «Proyectos de I+D+i» en el marco de los programas estatales de generación de conocimiento y fortalecimiento científico y tecnológico del sistema de I+D+i y de I+D+i orientada a los retos de la sociedad.

Cód. según financiadora:  PID2020-112530RB-I00.

Nombre investigador responsable: Carmen Alba Pastor (IP1- Universidad Complutense de Madrid, España) y Teresa González Ramírez (IP2 – Universidad de Sevilla, España)
DOI: 10.15468/dl.65n6aa
title: Occurrence Download

description: A dataset containing 3261 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "Year is 2016",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 3261 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038552-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/nvs1-3q84
title: "TikTok dance classifier training set"

description: "Dataset containing labelled TikTok dance videos as dancing or not."
DOI: 10.15468/dl.6c89yg
title: Occurrence Download

description: A dataset containing 9321 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Austria, France, Hungary, Romania, Croatia)",
 "TaxonKey is Lacerta viridis"
 ]
}

 The dataset includes 9321 records from 99 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038554-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ucnjm5
title: Occurrence Download

description: A dataset containing 5488 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "Year is 2017",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 5488 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038555-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yamd9a
title: Occurrence Download

description: A dataset containing 2895 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus kelloggii)",
 "StateProvince is one of (California, Mexico Northwest, Oregon)",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 2895 records from 132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038553-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9xsrea
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Country is Mexico",
 "OccurrenceStatus is Present",
 "TaxonKey is Odocoileus virginianus",
 "Year 1000-2025"
 ]
}

 The dataset includes 76 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038556-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ryncet
title: Occurrence Download

description: A dataset containing 6701 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Poland",
 "Year is 2018",
 "InstitutionCode is iNaturalist"
 ]
}

 The dataset includes 6701 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038558-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.m2c4ru
title: Occurrence Download

description: A dataset containing 31710 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Alouatta"
 ]
}

 The dataset includes 31710 records from 435 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038557-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8j7vqv
title: Occurrence Download

description: A dataset containing 1927 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus laevis)",
 "StateProvince is one of (Alabama, Florida, Georgia, Louisiana, Mississippi, North Carolina, South Carolina, Virginia)",
 "BasisOfRecord is Specimen"
 ]
}

 The dataset includes 1927 records from 119 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038559-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bs7u8m
title: Occurrence Download

description: A dataset containing 11 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Country is Mexico",
 "TaxonKey is Odocoileus hemionus"
 ]
}

 The dataset includes 11 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038561-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17390690
title: Base de datos para el análisis de la presencia explícita e implícita de los principios, pautas y puntos de verificación del DUA en la información sobre las situaciones de aprendizaje proporcionada en la normativa de currículum derivada de la LOMLOE de la etapa de Educación Primaria de las Comunidades Autónomas de España.

description: Base de datos para el análisis de la presencia explícita e implícita de los principios, pautas y puntos de verificación del DUA en la información sobre las situaciones de aprendizaje proporcionada en la normativa de currículum derivada de la LOMLOE de la etapa de Educación Primaria de las Comunidades Autónomas de España. 

Forma parte del trabajo realizado en el marco de Proyecto "El Diseño Universal para el Aprendizaje: aportaciones a las prácticas curriculares y a las culturas inclusiva y digital de los centros".

Entidad financiadora: Ministerio de Ciencia e Innovación, España.

Convocatoria 2020 de «Proyectos de I+D+i» en el marco de los programas estatales de generación de conocimiento y fortalecimiento científico y tecnológico del sistema de I+D+i y de I+D+i orientada a los retos de la sociedad.

Cód. según financiadora:  PID2020-112530RB-I00.

Nombre investigador responsable: Carmen Alba Pastor (IP1- Universidad Complutense de Madrid, España) y Teresa González Ramírez (IP2 – Universidad de Sevilla, España)
DOI: 10.5281/zenodo.17390691
title: Base de datos para el análisis de la presencia explícita e implícita de los principios, pautas y puntos de verificación del DUA en la información sobre las situaciones de aprendizaje proporcionada en la normativa de currículum derivada de la LOMLOE de la etapa de Educación Primaria de las Comunidades Autónomas de España.

description: Base de datos para el análisis de la presencia explícita e implícita de los principios, pautas y puntos de verificación del DUA en la información sobre las situaciones de aprendizaje proporcionada en la normativa de currículum derivada de la LOMLOE de la etapa de Educación Primaria de las Comunidades Autónomas de España. 

Forma parte del trabajo realizado en el marco de Proyecto "El Diseño Universal para el Aprendizaje: aportaciones a las prácticas curriculares y a las culturas inclusiva y digital de los centros".

Entidad financiadora: Ministerio de Ciencia e Innovación, España.

Convocatoria 2020 de «Proyectos de I+D+i» en el marco de los programas estatales de generación de conocimiento y fortalecimiento científico y tecnológico del sistema de I+D+i y de I+D+i orientada a los retos de la sociedad.

Cód. según financiadora:  PID2020-112530RB-I00.

Nombre investigador responsable: Carmen Alba Pastor (IP1- Universidad Complutense de Madrid, España) y Teresa González Ramírez (IP2 – Universidad de Sevilla, España)
DOI: 10.15468/dl.q5nrk6
title: Occurrence Download

description: A dataset containing 49399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Spain, Portugal, France, Italy)",
 "TaxonKey is Timon lepidus"
 ]
}

 The dataset includes 49399 records from 515 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038560-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15621293
title: Business Intelligence and the export competitiveness of lambayecan SMEs in the north of Peru

description: Background: International expansion has been identified as a pivotal strategy for SMEs seeking to achieve internationalisation and growth within the global market. However, as evidenced by extant literature, SMEs encounter numerous obstacles that impede their ability to access foreign markets, including a dearth of resources and experience, fierce competition from large and sustainable global companies, logistical challenges, and regulatory disparities across different markets. Therefore, the study seeks to identify the Business Intelligence factors that affect the export competitiveness of SMEs in a region of northern Peru.

Methods: The research had a quantitative, non-experimental and explanatory approach; being basic research focusing on the generation of theoretical knowledge about the factors that affect competitiveness through business intelligence strategies, for which 51 SMEs, economic units that currently maintain trade relations with international markets, were studied and the Logit model was used to identify those factors.

Results: The findings indicate that the binary logistic regression model demonstrates the significance of the market research and business management dimensions. Conversely, the business intelligence information dimension is found to be non-significant. The assertiveness matrix demonstrates a higher capacity to predict the 'No' response (76.5%) in comparison to the 'Yes' response (23.5%). It is noteworthy that the relative probability of enhancing competitiveness is estimated at 0.98 times when market identification, target market segment analysis and market research development are requested, as opposed to 0.95 times when external advice or assistance is obtained to formulate an alternative value proposition for the exportable product.

Conclusions: The market research and business management dimensions of Business Intelligence have a significant impact on the export competitiveness of SMEs in northern Peru, with market research being the factor with the greatest influence on the export competitiveness of SMEs. This finding highlights the importance of collecting and analysing accurate information on international markets in order to identify opportunities, reduce uncertainties and adapt products to the demands of global customers.

Keywords: Business Intelligence, Competitiveness, Exports, Lambayeque, Lambayeque, Logit model
DOI: 10.6084/m9.figshare.29039305
title: Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

description: Algorithmic recommendations and decisions have become ubiquitous in today’s society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data from a unique field experiment on an algorithmic pre-trial risk assessment to investigate whether the scores and recommendations can be improved. Unfortunately, prior methods for policy learning are not applicable because they require existing policies to be stochastic. We develop a maximin robust optimization approach that partially identifies the expected utility of a policy, and then finds a policy that maximizes the worst-case expected utility. The resulting policy has a statistical safety property, limiting the probability of producing a worse policy than the existing one, under structural assumptions about the outcomes. Our analysis of data from the field experiment shows that we can safely improve certain components of the risk assessment instrument by classifying arrestees as lower risk under a wide range of utility specifications, though the analysis is not informative about several components of the instrument. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.29039305.v1
title: Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment<sup>†</sup>

description: Algorithmic recommendations and decisions have become ubiquitous in today’s society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data from a unique field experiment on an algorithmic pre-trial risk assessment to investigate whether the scores and recommendations can be improved. Unfortunately, prior methods for policy learning are not applicable because they require existing policies to be stochastic. We develop a maximin robust optimization approach that partially identifies the expected utility of a policy, and then finds a policy that maximizes the worst-case expected utility. The resulting policy has a statistical safety property, limiting the probability of producing a worse policy than the existing one, under structural assumptions about the outcomes. Our analysis of data from the field experiment shows that we can safely improve certain components of the risk assessment instrument by classifying arrestees as lower risk under a wide range of utility specifications, though the analysis is not informative about several components of the instrument.
DOI: 10.6084/m9.figshare.29039305.v2
title: Safe Policy Learning through Extrapolation: Application to Pre-trial Risk Assessment

description: Algorithmic recommendations and decisions have become ubiquitous in today’s society. Many of these data-driven policies, especially in the realm of public policy, are based on known, deterministic rules to ensure their transparency and interpretability. We examine a particular case of algorithmic pre-trial risk assessments in the US criminal justice system, which provide deterministic classification scores and recommendations to help judges make release decisions. Our goal is to analyze data from a unique field experiment on an algorithmic pre-trial risk assessment to investigate whether the scores and recommendations can be improved. Unfortunately, prior methods for policy learning are not applicable because they require existing policies to be stochastic. We develop a maximin robust optimization approach that partially identifies the expected utility of a policy, and then finds a policy that maximizes the worst-case expected utility. The resulting policy has a statistical safety property, limiting the probability of producing a worse policy than the existing one, under structural assumptions about the outcomes. Our analysis of data from the field experiment shows that we can safely improve certain components of the risk assessment instrument by classifying arrestees as lower risk under a wide range of utility specifications, though the analysis is not informative about several components of the instrument. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28882896
title: <i>Who Are We Missing?</i>: A Principled Approach to Characterizing the Underrepresented Population

description: Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our article addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to characterize underrepresented groups. ROOT optimizes the target subpopulation distribution by minimizing the variance of the target average treatment effect estimate, ensuring more precise treatment effect estimations. Notably, ROOT generates interpretable characteristics of the underrepresented population, aiding researchers in effective communication. Our approach demonstrates improved precision and interpretability compared to alternatives, as illustrated with synthetic data experiments. We apply our methodology to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial—investigating the effectiveness of medication for opioid use disorder—to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.28882896.v1
title: <i>Who Are We Missing?</i>: A Principled Approach to Characterizing the Underrepresented Population

description: Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our article addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), to characterize underrepresented groups. ROOT optimizes the target subpopulation distribution by minimizing the variance of the target average treatment effect estimate, ensuring more precise treatment effect estimations. Notably, ROOT generates interpretable characteristics of the underrepresented population, aiding researchers in effective communication. Our approach demonstrates improved precision and interpretability compared to alternatives, as illustrated with synthetic data experiments. We apply our methodology to extend inferences from the Starting Treatment with Agonist Replacement Therapies (START) trial—investigating the effectiveness of medication for opioid use disorder—to the real-world population represented by the Treatment Episode Dataset: Admissions (TEDS-A). By refining target populations using ROOT, our framework offers a systematic approach to enhance decision-making accuracy and inform future trials in diverse populations. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.yxqcuz
title: Occurrence Download

description: A dataset containing 126 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Human Observation)",
 "TaxonKey is Bothrops erythromelas Amaral, 1923"
 ]
}

 The dataset includes 126 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067391-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15625280
title: Chimera baryons and mesons on the lattice: a spectral density analysis---Data release

description: This is the data release relative to the paper "Chimera baryons and mesons on the lattice: a spectral density analysis". It contains the data that can be analysed through the analysis code in doi:10.5281/zenodo.15625321.
description: E.B. and B.L. are supported by the EPSRC ExCALIBUR programme ExaTEPP project EP/X017168/1. E.B. is supported by the STFC Research Software Engineering Fellowship EP/V052489/1. E.B., B.L., M.P. and F.Z. are supported by the STFC Consolidated Grant No. ST/X000648/1.The work of N.F. is supported by the STFC Doctoral Training GrantNo. ST/X508834/1. A.L. is funded in part by l’Agence Nationale de la Recherche (ANR), under grant ANR-22-CE31-0011.D.K.H. is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701) and by the NRF grant 2021R1A4A5031460 funded by the Korean government (MSIT). L.D.D. and R.C.H. are supported by the STFC grant ST/P000630/1.L.D.D. is supported by the ExaTEPP project EP/X01696X/1.J.W.L. is supported by IBS under the project code, IBS-R018-D1. H.H. and C.J.D.L. acknowledge support from NSTC Taiwan, through grant number 112-2112-M-A49-021-MY3. C.J.D.L. is also supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3. C.J.D.L. is supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013-.B.L. and M.P. are supported by the STFC  Consolidated Grant No. ST/T000813/1.B.L., M.P. and L.D.D. received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. D.V. is supported by STFC under Consolidated Grant No. ST/X000680/1.

Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the UKRI Digital Research Infrastructure. The DiRAC Data Intensive service (DIaL2 / DIaL [*]) at the University of Leicester, is managed by the University of Leicester Research Computing Service on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). The DiRAC service at Leicester was funded by BEIS, UKRI and STFC capital funding and STFC operations grants.
DOI: 10.5281/zenodo.15731921
title: Mapping the Intellectual Landscape and Emerging Trends in Social Withdrawal Research: A Bibliometric Synthesis and Theoretical Integration (2013–2024)
DOI: 10.5281/zenodo.15731922
title: Mapping the Intellectual Landscape and Emerging Trends in Social Withdrawal Research: A Bibliometric Synthesis and Theoretical Integration (2013–2024)