cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.28950767.v1
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.
DOI: 10.7910/dvn/pqzkph
title: Key Frames Generated from 3-Second Intervals

description: In Figure 2 of the main text, these frames correspond to "1 video frame per 3-second interval."
DOI: 10.15468/dl.58bb95
title: Occurrence Download

description: A dataset containing 1399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Plethodon shenandoah)"
 ]
}

 The dataset includes 1399 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007410-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r9cgm4
title: Occurrence Download

description: A dataset containing 544 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Senna pistaciifolia"
 ]
}

 The dataset includes 544 records from 57 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007411-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e3sczr
title: Occurrence Download

description: A dataset containing 1472289 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "Country is Ukraine",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1472289 records from 334 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007375-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7274/29539139
title: Exploring the Nature of Dark Matter Through Inflation, Primordial Black Holes, and Large Scale Structure

description: In this thesis I explore dark matter via three avenues: through cosmic inflation, the evolution of primordial black holes, and large scale structure. Each project studies a theoretical framework and works towards observational consequences of dark matter. This is important as a means to distinguish between the various proposed candidates of dark matter. First, I explore the paradigm of warm natural inflation (WNI), an inflationary paradigm in which the primordial field generates a thermal bath. Through this theoretically well motivated model, I am able to show that not only does it satisfy constraints from the cosmic microwave background, it is able to generate enough primordial black holes to explain dark matter. Additionally, I show that the generation of scalar induced gravitational waves (SIGWs) through this process will be detectable by future observations. Second, I explore the hierarchical merging or coagulation of primordial black holes through multiple generations. Specifically, I study the conditions necessary to trigger runaway merging. This is done via a GPU accelerated coagulation code. I find that for asteroid mass black holes, no significant merging occurs and it behaves similar to cold dark matter. Finally, I explore large scale structure finding methods via machine learning (ML) tools, with a focus on identifying cosmic voids. The identification of voids is important as a possible means to constrain the time dependence of dark energy. I use a Kmeans clustering algorithm to segment cosmic structures and find it segments voids similar to other structure finders. I then train a UNET neural network to predict void finding at higher redshifts and achieve moderate accuracy, with accuracy generally decaying at higher redshifts. I then finally build a pipeline to extract the surfaces of the void-like regions using Connected Component Labeling (CCL) and the Marching Cubes algorithm.
DOI: 10.7274/29539139.v1
title: Exploring the Nature of Dark Matter Through Inflation, Primordial Black Holes, and Large Scale Structure

description: In this thesis I explore dark matter via three avenues: through cosmic inflation, the evolution of primordial black holes, and large scale structure. Each project studies a theoretical framework and works towards observational consequences of dark matter. This is important as a means to distinguish between the various proposed candidates of dark matter. First, I explore the paradigm of warm natural inflation (WNI), an inflationary paradigm in which the primordial field generates a thermal bath. Through this theoretically well motivated model, I am able to show that not only does it satisfy constraints from the cosmic microwave background, it is able to generate enough primordial black holes to explain dark matter. Additionally, I show that the generation of scalar induced gravitational waves (SIGWs) through this process will be detectable by future observations. Second, I explore the hierarchical merging or coagulation of primordial black holes through multiple generations. Specifically, I study the conditions necessary to trigger runaway merging. This is done via a GPU accelerated coagulation code. I find that for asteroid mass black holes, no significant merging occurs and it behaves similar to cold dark matter. Finally, I explore large scale structure finding methods via machine learning (ML) tools, with a focus on identifying cosmic voids. The identification of voids is important as a possible means to constrain the time dependence of dark energy. I use a Kmeans clustering algorithm to segment cosmic structures and find it segments voids similar to other structure finders. I then train a UNET neural network to predict void finding at higher redshifts and achieve moderate accuracy, with accuracy generally decaying at higher redshifts. I then finally build a pipeline to extract the surfaces of the void-like regions using Connected Component Labeling (CCL) and the Marching Cubes algorithm.
DOI: 10.15468/dl.mryma7
title: Occurrence Download

description: A dataset containing 654 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Micropterus notius"
 ]
}

 The dataset includes 654 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007413-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g3wjx3
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((0.0096134805216875 0.0005694684898915, 0.0093185218381205 0.0024467146088316, 0.0086654225026288 0.0042299980410499, 0.0076792596585186 0.0058507700798525, 0.0063979183880355 0.0072467204908223, 0.0048706377302871 0.0083641758462778, 0.0031561187869462 0.009160166403509, 0.0013202675688003 0.0096040814528274, -0.000566340477498 0.00967884890471, -0.0024311764080539 0.0093815932923379, -0.0042025492402665 0.0087237466086922, -0.0058123650152872 0.0077306076674729, -0.0071987468229383 0.006440367121887, -0.0083084146136622 0.0049026360300441, -0.0090987330508717 0.0031765350907348, -0.0095393486560258 0.0013284186249091, -0.0096133535313737 -0.0005706786153817, -0.0093179313575591 -0.0024477732008673, -0.0086644614140555 -0.0042307380690285, -0.0076780772658795 -0.0058510730116445, -0.0063966976868194 -0.0072465343382561, -0.0048695675481183 -0.0083635230797686, -0.0031553650362883 -0.0091591405312959, -0.0013199479882922 -0.0096028327850792, 0.0005661742476908 -0.0096775616701926, 0.0024305466879848 -0.0093804575912764, 0.0042015489121839 -0.008722929471703, 0.0058111433831483 -0.0077302276266623, 0.0071974868822384 -0.0064404761654613, 0.0083073051920206 -0.0049032116875438, 0.0090979400607442 -0.0031774838539138, 0.0095389898360394 -0.0013295901836182, 0.0096134805216875 0.0005694684898915))",
 "Year is one of (2019, 2020, 2021, 2022, 2023, 2024, 2025)",
 "KingdomKey is one of (Animalia, Fungi, Plantae)"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007416-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kspxnf
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lepidium virginicum",
 "Country is Korea (Democratic People’s Republic of)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007415-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cgee75
title: Occurrence Download

description: A dataset containing 14537 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Procambarus clarkii",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 14537 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007414-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y6p2hs
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Potentilla argentea canescens, Potentilla argentea impolita, Potentilla argentea inclinata, Potentilla arthuriana, Potentilla arthuriana, Potentilla ascendens, Potentilla ascendens, Potentilla assurgens, Potentilla assurgens inclinata, Potentilla baumgarteniana)",
 "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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007417-250717081556266/datasets/export for details.
DOI: 10.7910/dvn/q0ar94
title: Key Frames Generated from Whisper API Output

description: In Figure 2 of the main text, these frames correspond to "1 video frame per transcript segment" where the transcript segments were created using OpenAI’s LLM-based Whisper transcription model: WHISPER-LARGE-V3.
DOI: 10.7274/29429870
title: Unite Without Uniformity: Networked Church Growth via Group Tensions

description: The BoL Global Apostolic Network (BGAN) proposes an intriguing case to investigate how intra-group differences can be managed and negotiated when seeking networked church growth. Studying a transnational Evangelical network originated in Taiwan, the thesis asks, “How are intergroup tensions managed in pursuit of network expansion?”. Theoretically, the paper answers why group unity can be formed despite significant group diversities. Existing literature considers church growth as the result for competing for market niches within religious markets, whereas little literature investigates how interactions shape networked church expansion despite increasing network religious movements being formed. The unique transnational Evangelical network thus provides insights into the networked church expansion mechanisms. First, the intra-group tensions are managed by allowing BGAN branches to seek structural and cultural independence, developing the effective strategies to pursue church expansion in their respective contexts. Moreover, differences between subsystems catalyze the maintaining of network relationships because the hidden conflicting comparisons bond branches to the shared Mission. Applying an interactional aspect, I argue that the intergroup tensions mobilize BGAN expansion and sustain the relationships between branches. Aside from a rational choice assumption of illustrating church growth mechanisms, the paper contributes to the literature by showing that group tensions within network interactions enable the network expansion with a sustained relationship.
DOI: 10.7274/29429870.v1
title: Unite Without Uniformity: Networked Church Growth via Group Tensions

description: The BoL Global Apostolic Network (BGAN) proposes an intriguing case to investigate how intra-group differences can be managed and negotiated when seeking networked church growth. Studying a transnational Evangelical network originated in Taiwan, the thesis asks, “How are intergroup tensions managed in pursuit of network expansion?”. Theoretically, the paper answers why group unity can be formed despite significant group diversities. Existing literature considers church growth as the result for competing for market niches within religious markets, whereas little literature investigates how interactions shape networked church expansion despite increasing network religious movements being formed. The unique transnational Evangelical network thus provides insights into the networked church expansion mechanisms. First, the intra-group tensions are managed by allowing BGAN branches to seek structural and cultural independence, developing the effective strategies to pursue church expansion in their respective contexts. Moreover, differences between subsystems catalyze the maintaining of network relationships because the hidden conflicting comparisons bond branches to the shared Mission. Applying an interactional aspect, I argue that the intergroup tensions mobilize BGAN expansion and sustain the relationships between branches. Aside from a rational choice assumption of illustrating church growth mechanisms, the paper contributes to the literature by showing that group tensions within network interactions enable the network expansion with a sustained relationship.
DOI: 10.15468/dl.f3r29h
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007418-250717081556266/datasets/export for details.
DOI: 10.15468/dl.4yqkgd
title: Occurrence Download

description: A dataset containing 160873 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Impatiens parviflora)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000"
 ]
}

 The dataset includes 160873 records from 636 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007388-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15156/bio/sh1400376.10fu
title: SH1400376.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400379.10fu
title: SH1400379.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400380.10fu
title: SH1400380.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400381.10fu
title: SH1400381.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.15156/bio/sh1400384.10fu
title: SH1400384.10FU

description: UNITE provides a unified way for delimiting, identifying, communicating, and working with DNA-based Species Hypotheses (SH). All fungal ITS sequences in the international nucleotide sequence databases are clustered to approximately the species level by applying a set of dynamic distance values (<0.5 - 3.0%). All species hypotheses are given a unique, stable name in the form of a DOI, and their taxonomic and ecological annotations are verified through distributed, web-based third-party annotation efforts. SHs are connected to a taxon name and its classification as far as possible (phylum, class, order, etc.) by taking into account identifications for all sequences in the SH. An automatically or manually designated sequence is chosen to represent each such SH. These sequences are released (https://unite.ut.ee/repository.php) for use by the scientific community in, for example, local sequence similarity searches and next-generation sequencing analysis pipelines. The system and the data are updated automatically as the number of public fungal ITS sequences grows.
DOI: 10.5281/zenodo.18231268
title: TABLE 1 in Contribution to the knowledge of the apterous genus Zubovskya Dovnar-Zapolskij, 1932 (Orthoptera: Acrididae: Melanoplinae). Taxonomic composition, notes on Zubovskya koeppeni (Zubovsky, 1900), and entomogeographic studies on Z. mongolica Storozhenko, 1986

description: TABLE 1. Altitudinal distribution of Zubovskya mongolica and Z. koeppeni in the Altai-Sayan Mts. (1995–2018).
Altitude, mLocalityLatitude (N)Longitude (E)Zubovskya mongolicaZubovskya koeppeni289–292Ermakovskoje53.2892.45—+697–727Idzhim52.3493.17—+1432–1440Oyskoe Lake52.8693.26—+1470–1630Oyskoe Lake52.8593.27—+1540–1815Olenja Rechka52.7893.25—+1737–1780Koptu River51.9495.50+—1869–1945Koptu River51.9595.52+—2005Koptu River51.9495.55+—2079Sayanskij Pass51.7289.89+—
description: Published as part of Sergeev, Michael G. & Molodtsov, Vladimir V., 2025, Contribution to the knowledge of the apterous genus Zubovskya Dovnar-Zapolskij, 1932 (Orthoptera: Acrididae: Melanoplinae). Taxonomic composition, notes on Zubovskya koeppeni (Zubovsky, 1900), and entomogeographic studies on Z. mongolica Storozhenko, 1986, pp. 421 in Zootaxa 5715 (1) on page 421, DOI: 10.11646/zootaxa.5715.1.35, http://zenodo.org/record/17888281