cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.7274/29231822
title: Landing Peace: Rural-Poor Mobilization and Land Redistribution in Civil War Political Transitions

description: Dominant theories of political transitions and redistribution suggest that negotiated settlements involve a trade-off: elites concede political incorporation to opposition actors yet resist wealth redistribution—especially when wealth is tied to land. Contrary to these expectations, 40 of 79 peace settlements addressed land reform between 1991 and 2022. Under what conditions is land redistribution achieved in civil war political transitions? More specifically, why is land redistribution enshrined in peace settlements? Why is it implemented at varying degrees?<br><br>I develop a theory of unarmed rural movement agency to explain both the negotiation and implementation of land redistribution. I argue that Indigenous, Afro-descendant, and peasant movements shape redistribution when they deploy mobilization strength: the ability to engage in intense political contention—organizational capacity—and credibly assert autonomy from warring factions—distancing capacity. By projecting legitimacy and urgency, movements forge consensus across partisan and warring divides. Where insurgents set the agenda, movements bridge divides between warring parties through a revolutionary coalition anchored to insurgent-led negotiation. Where insurgents are weak, movements expand agreement across partisan divides via a constituent coalition with reformist elites. After settlements, land delivery hinges on whether movements pair mobilization strength with political incorporation—their recognition as legitimate actors entitled to rights and state access. Movements activate land claims and shape policy from within, triggering property rights allocation to dispossessed communities.<br><br>I test this argument through a comparative historical analysis of Colombia’s two major peace processes (1988–1991, 2012–2016). In the 1990s, Indigenous and Black movements expanded cross-partisan consensus to secure collective land ownership, later driving allocation via grassroots organizing and bureaucratic posts. In the 2010s, peasant movements narrowed the gap between signatories’ preferences to shape individual land ownership, yet delivery has remained uneven. To assess generalizability, I conduct a cross-national regression analysis and a paired-case comparison of El Salvador and Guatemala. I employ process-tracing, NLP, network analysis, and regression, drawing on archives, 136 interviews, participant observation, and original datasets collected from 16 months of fieldwork in Colombia and 1 month in Central America. These findings underscore the overlooked role of unarmed movements in reshaping property rights in transitioning contexts.
DOI: 10.7274/29231822.v1
title: Landing Peace: Rural-Poor Mobilization and Land Redistribution in Civil War Political Transitions

description: Dominant theories of political transitions and redistribution suggest that negotiated settlements involve a trade-off: elites concede political incorporation to opposition actors yet resist wealth redistribution—especially when wealth is tied to land. Contrary to these expectations, 40 of 79 peace settlements addressed land reform between 1991 and 2022. Under what conditions is land redistribution achieved in civil war political transitions? More specifically, why is land redistribution enshrined in peace settlements? Why is it implemented at varying degrees?<br><br>I develop a theory of unarmed rural movement agency to explain both the negotiation and implementation of land redistribution. I argue that Indigenous, Afro-descendant, and peasant movements shape redistribution when they deploy mobilization strength: the ability to engage in intense political contention—organizational capacity—and credibly assert autonomy from warring factions—distancing capacity. By projecting legitimacy and urgency, movements forge consensus across partisan and warring divides. Where insurgents set the agenda, movements bridge divides between warring parties through a revolutionary coalition anchored to insurgent-led negotiation. Where insurgents are weak, movements expand agreement across partisan divides via a constituent coalition with reformist elites. After settlements, land delivery hinges on whether movements pair mobilization strength with political incorporation—their recognition as legitimate actors entitled to rights and state access. Movements activate land claims and shape policy from within, triggering property rights allocation to dispossessed communities.<br><br>I test this argument through a comparative historical analysis of Colombia’s two major peace processes (1988–1991, 2012–2016). In the 1990s, Indigenous and Black movements expanded cross-partisan consensus to secure collective land ownership, later driving allocation via grassroots organizing and bureaucratic posts. In the 2010s, peasant movements narrowed the gap between signatories’ preferences to shape individual land ownership, yet delivery has remained uneven. To assess generalizability, I conduct a cross-national regression analysis and a paired-case comparison of El Salvador and Guatemala. I employ process-tracing, NLP, network analysis, and regression, drawing on archives, 136 interviews, participant observation, and original datasets collected from 16 months of fieldwork in Colombia and 1 month in Central America. These findings underscore the overlooked role of unarmed movements in reshaping property rights in transitioning contexts.
DOI: 10.15468/dl.6u5s5e
title: Occurrence Download

description: A dataset containing 1783732 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Amphibia)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil)"
 ]
 }
 },
 "Country is Australia"
 ]
}

 The dataset includes 1783732 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007374-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wh4rjv
title: Occurrence Download

description: A dataset containing 60257 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Material sample, Material citation, Specimen)",
 "Country is Colombia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Mammalia"
 ]
}

 The dataset includes 60257 records from 237 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007378-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t8494k
title: Occurrence Download

description: A dataset containing 90 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cuprella homalocarpa"
 ]
}

 The dataset includes 90 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007398-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16100243
title: PBCONSET
DOI: 10.15468/dl.3hg9tb
title: Occurrence Download

description: A dataset containing 551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eutypella parasitica",
 "Country is Slovenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 551 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007400-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ffkbpm
title: Occurrence Download

description: A dataset containing 379 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Streptanthus hispidus"
 ]
}

 The dataset includes 379 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007397-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wvyxx4
title: Occurrence Download

description: A dataset containing 260 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Potentilla adscendens fallax, Potentilla adscendens laciniosa, Potentilla adscendens oligotricha, Potentilla adscendens polytricha, Potentilla adscendens sadleri, Potentilla adscendens subbiserrata, Potentilla argentea impolita, Potentilla argentea impolita, Potentilla argentea impolita, Potentilla argentea inclinata)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 260 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007399-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16100704
title: example
DOI: 10.5281/zenodo.16100705
title: example
DOI: 10.7274/29510333
title: Interpreting Without Identity: QAnon's Formation in an Anonymous Online Space

description: This thesis examines the formation of the conspiracist group, QAnon through an analysis of its emergence on the website, 4chan. Through an analysis of interactions between users, this research shows how the anonymous platform complicated deliberation and agreement but encouraged participatory speculation. As anonymity threatened participants’ ability to interpret and understand each other, participants began to identify other users as outside saboteurs. Without histories of participation, any anonymous user could be identified as an outsider. This process helped the emerging group to legitimize its interpretive activity, while discouraging competing understandings of the group. This research shows how anonymity undermines traditional supports for interaction while also encouraging conspiracist speculation
DOI: 10.7274/29510333.v1
title: Interpreting Without Identity: QAnon's Formation in an Anonymous Online Space

description: This thesis examines the formation of the conspiracist group, QAnon through an analysis of its emergence on the website, 4chan. Through an analysis of interactions between users, this research shows how the anonymous platform complicated deliberation and agreement but encouraged participatory speculation. As anonymity threatened participants’ ability to interpret and understand each other, participants began to identify other users as outside saboteurs. Without histories of participation, any anonymous user could be identified as an outsider. This process helped the emerging group to legitimize its interpretive activity, while discouraging competing understandings of the group. This research shows how anonymity undermines traditional supports for interaction while also encouraging conspiracist speculation
DOI: 10.15468/dl.rsvqf4
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Procambarus clarkii",
 "Country is Malta",
 "HasCoordinate is true"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007401-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tg3nrd
title: Occurrence Download

description: A dataset containing 13318 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Vigna luteola"
 ]
}

 The dataset includes 13318 records from 399 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007395-250717081556266/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.29598815
title: <b>FinTech innovations with Islamic and Catholic ventures</b>

description: <b>Accordance of green FinTech innovations with Islamic and Catholic ventures: An efficient Bayesian Markov-switching framework with forward-backward algorithm</b>
DOI: 10.6084/m9.figshare.29598815.v1
title: <b>FinTech innovations with Islamic and Catholic ventures</b>

description: <b>Accordance of green FinTech innovations with Islamic and Catholic ventures: An efficient Bayesian Markov-switching framework with forward-backward algorithm</b>
DOI: 10.15468/dl.qfrtyt
title: Occurrence Download

description: A dataset containing 1308 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidium virginicum",
 "Country is Japan",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1308 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007402-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b85xgf
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aethionema kilicii"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007403-250717081556266/datasets/export for details.
DOI: 10.7274/29566097
title: Nonparametric Distribution Regression and Change Point Detection in High-Dimensions

description: This dissertation investigates two high-dimensional problems—change-point analysis in functional linear regression and nonparametric density estimation using modern generative models. 
 
 First, we introduce the Functional Regression Binary Segmentation (FRBS) algorithm for change-point detection in the slope function when predictors are functions and responses are scalars. This algorithm utilizes the predictive power of piece-wise constant functional linear regression models in the reproducing kernel Hilbert space framework. We further propose a refinement step that improves the localization rate of the initial estimator output by FRBS, and derive asymptotic distributions of the refined estimators for two different regimes determined by the magnitude of a change. 
 
 Second, we study the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. 
 
 Third, we provide the first finite-sample theoretical analysis for flow matching—a simulation-free generative modeling approach. The velocity field is estimated via empirical risk minimization over a suitably designed ReLU network class. A switching argument is used to transfer the estimation error from the learned velocity field and time discretization to the resulting transport map, yielding an overall Wasserstein-2 error bound.
DOI: 10.7274/29566097.v1
title: Nonparametric Distribution Regression and Change Point Detection in High-Dimensions

description: This dissertation investigates two high-dimensional problems—change-point analysis in functional linear regression and nonparametric density estimation using modern generative models. 
 
 First, we introduce the Functional Regression Binary Segmentation (FRBS) algorithm for change-point detection in the slope function when predictors are functions and responses are scalars. This algorithm utilizes the predictive power of piece-wise constant functional linear regression models in the reproducing kernel Hilbert space framework. We further propose a refinement step that improves the localization rate of the initial estimator output by FRBS, and derive asymptotic distributions of the refined estimators for two different regimes determined by the magnitude of a change. 
 
 Second, we study the theoretical properties of conditional deep generative models under the statistical framework of distribution regression where the response variable lies in a high-dimensional ambient space but concentrates around a potentially lower-dimensional manifold. Our results lead to the convergence rate of a sieve maximum likelihood estimator (MLE) for estimating the conditional distribution (and its devolved counterpart) of the response given predictors in the Hellinger (Wasserstein) metric. 
 
 Third, we provide the first finite-sample theoretical analysis for flow matching—a simulation-free generative modeling approach. The velocity field is estimated via empirical risk minimization over a suitably designed ReLU network class. A switching argument is used to transfer the estimation error from the learned velocity field and time discretization to the resulting transport map, yielding an overall Wasserstein-2 error bound.
DOI: 10.15468/dl.p2ebve
title: Occurrence Download

description: A dataset containing 31865 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Desmognathus fuscus)"
 ]
}

 The dataset includes 31865 records from 53 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007404-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t4dskk
title: Occurrence Download

description: A dataset containing 2539 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Bombycilla garrulus)",
 "Country is one of (Afghanistan, Armenia, Azerbaijan, Bahrain, Bangladesh, Bhutan, Brunei Darussalam, Cambodia, China, Cyprus, Georgia, Hong Kong, India, Indonesia, Iran (Islamic Republic of), Iraq, Israel, Japan, Jordan, Kazakhstan, Korea (Democratic People’s Republic of), Korea, Republic of, Kuwait, Kyrgyzstan, Lao People’s Democratic Republic, Lebanon, Macao, Malaysia, Maldives, Mongolia, Myanmar, Nepal, Oman, Pakistan, Palestine, State of, Philippines, Qatar, Saudi Arabia, Singapore, Sri Lanka, Syrian Arab Republic, Tajikistan, Thailand, Timor-Leste, Türkiye, Turkmenistan, United Arab Emirates, Uzbekistan, Viet Nam, Yemen)"
 ]
}

 The dataset includes 2539 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007408-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.unyntp
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Bombycilla garrulus)",
 "Country is one of (Anguilla, Antigua and Barbuda, Argentina, Aruba, Bahamas, Barbados, Belize, Bolivia (Plurinational State of), Bonaire, Sint Eustatius and Saba, Bouvet Island, Brazil, Cayman Islands, Chile, Colombia, Costa Rica, Cuba, Curaçao, Dominica, Dominican Republic, Ecuador, El Salvador, Falkland Islands (Malvinas), French Guiana, Grenada, Guadeloupe, Guatemala, Guyana, Haiti, Honduras, Jamaica, Martinique, Mexico, Montserrat, Nicaragua, Panama, Paraguay, Peru, Puerto Rico, Saint Barthélemy, Saint Kitts and Nevis, Saint Lucia, Saint Martin (French part), Saint Vincent and the Grenadines, Sint Maarten (Dutch part), South Georgia and the South Sandwich Islands, Suriname, Trinidad and Tobago, Turks and Caicos Islands, Uruguay, Venezuela (Bolivarian Republic of), Virgin Islands (British), Virgin Islands (U.S.))"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007409-250717081556266/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.28950767
title: Suplementary material: Enhancing and Advancements in Deep Learning for Melanoma Detection: A Comprehensive Review

description: Melanoma, although not the most common skin cancer, poses a significant global health challenge, particularly in Europe, where incidence rates are high. Traditional melanoma diagnosis through biopsies can be invasive, but advancements in artificial intelligence (AI), particularly deep learning (DL), have shown promising potential for early and accurate melanoma detection through image analysis. In this systematic review, we explore the trends and gaps in the application of DL for melanoma detection, focusing on the replicability and generalization of existing models. While models trained on image databases from Europe and North America demonstrate high accuracy, their applicability to populations with different skin phototypes, such as those in Africa, Asia, and Latin America, remains limited. Since 2019, the role of DL in melanoma detection and diagnosis has gained traction, with public databases often used, such as ISIC and HAM10000. However, many studies suffer from a lack of transparency in data partitioning, leading to concerns about model overfitting and reproducibility. Usual practices, including the use of 224×224 pixel resolution for image segmentation and employing architectures like ResNet and Inception, frequently lack detailed methodological transparency, further limiting reproducibility. This review underscores the need for integrating more diverse and high-quality data to enhance the global effectiveness of DL models in melanoma diagnoses. Also key challenges, including variability in image quality and the opacity of DL models, which hinder broader clinical adoption were discussed. Finally, we recommend standardizing the databases and developing more robust and explainable models to guide future research.