cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.25549/fame-c109-1873
title: First African Methodist Episcopal Church of Los Angeles Collection

description: The First African Methodist Episcopal Church of Los Angeles Collection currently consists of approximately 400 sermons given by Reverend Cecil “Chip” Murray. These recordings capture the evolution of Reverend Murray’s thinking during his time leading the church in addition to the messages he gave his congregation after pivotal moments in U.S. life, such as the acquittal of the LAPD officers captured on video beating Rodney King. These sermons, delivered with passion, great humor, and insights into the structural inequalities that led to events like the 1992 L.A. civil unrest, are a record of important events interpreted through the lens of Rev. Murray’s socially engaged African-American Christianity. Originally captured on VHS, a selection of these sermons have been digitized by the USC Digital Repository and uploaded and transcribed for optimum access in the Digital Library. Founded by Biddy Mason in 1872, the First African Methodist Episcopal Church of Los Angeles (also known as the First AME, or FAME Church) is the oldest African-American church in Southern California. During his 27 years as pastor, Reverend Murray transformed a small congregation of 250 people into an 18,000-member institution that brought jobs, housing, and corporate investment into South L.A. neighborhoods. After the 1992 civil unrest, the economic development arm of the church brought more than $400 million in investments to L.A.’s minority and low-income neighborhoods. After retiring from his ministry in 2004, Reverend Murray was named a senior fellow of the USC Center for Religion and Civic Culture (CRCC). He chairs the USC Cecil Murray Center for Community Engagement, a leadership training and capacity-building organization that equips clergy and lay leaders to guide their communities into full partnership in the development of underserved neighborhoods in Greater Los Angeles. Materials from the First African Methodist Episcopal Church Collection are part of the L. A. as Subject Community Histories Digitization Project. Thanks to generous support from the National Endowment for the Humanities (https://www.neh.gov/), the USC Libraries are digitizing this collection for public access.
DOI: 10.25549/fame-c109-1912
title: First African Methodist Episcopal Church of Los Angeles Collection

description: The First African Methodist Episcopal Church of Los Angeles Collection currently consists of approximately 400 sermons given by Reverend Cecil “Chip” Murray. These recordings capture the evolution of Reverend Murray’s thinking during his time leading the church in addition to the messages he gave his congregation after pivotal moments in U.S. life, such as the acquittal of the LAPD officers captured on video beating Rodney King. These sermons, delivered with passion, great humor, and insights into the structural inequalities that led to events like the 1992 L.A. civil unrest, are a record of important events interpreted through the lens of Rev. Murray’s socially engaged African-American Christianity. Originally captured on VHS, a selection of these sermons have been digitized by the USC Digital Repository and uploaded and transcribed for optimum access in the Digital Library. Founded by Biddy Mason in 1872, the First African Methodist Episcopal Church of Los Angeles (also known as the First AME, or FAME Church) is the oldest African-American church in Southern California. During his 27 years as pastor, Reverend Murray transformed a small congregation of 250 people into an 18,000-member institution that brought jobs, housing, and corporate investment into South L.A. neighborhoods. After the 1992 civil unrest, the economic development arm of the church brought more than $400 million in investments to L.A.’s minority and low-income neighborhoods. After retiring from his ministry in 2004, Reverend Murray was named a senior fellow of the USC Center for Religion and Civic Culture (CRCC). He chairs the USC Cecil Murray Center for Community Engagement, a leadership training and capacity-building organization that equips clergy and lay leaders to guide their communities into full partnership in the development of underserved neighborhoods in Greater Los Angeles. Materials from the First African Methodist Episcopal Church Collection are part of the L. A. as Subject Community Histories Digitization Project. Thanks to generous support from the National Endowment for the Humanities (https://www.neh.gov/), the USC Libraries are digitizing this collection for public access.
DOI: 10.15468/dl.ymscew
title: Occurrence Download

description: A dataset containing 860415 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is one of (Hoffmannseggia prostrata, Bentleya diminuta, Astrotricha crassifolia, Astrotricha roddii, Luzula africana, Luzula australasica, Drakaea gracilis, Dipyrena spartioides, Erythranthe howaldiae, Zygophyllum decumbens, Talinum aurantiacum, Pintoa chilensis, Bryonia multiflora, Lessertia margaritacea, Neurachne muelleri, Mimosa tricephala, Nymphaea candida, Erythranthe erubescens, Erythranthe dentiloba, Erythranthe taylorii, Sicyos peninsularis, Luzula caricina, Juniperus pinchotii, Kallstroemia rosei, Thapsia cinerea, Macrostylis hirta, Mimosa affinis, Thomasia pauciflora, Schizanthus pinnatus, Acrotriche halmaturina, Urochloa panicoides, Thomasia macrocalyx, Thomasia glabripetala, Erythranthe flammea, Geigeria obtusifolia, Piper palmeri, Luzula atlantica, Lessertia kensitii, Zygophyllum chrysopteron, Stenosemis angustifolia, Talinum polygaloides, Bryonia alba, Gymnema pleiadenium, Lessertia pauciflora, Pteronia anisata, Pteronia bolusii, Pteronia succulenta, Glycine pullenii, Pteronia adenocarpa, Thapsia tenuifolia, Prionosciadium madrense, Piper jaliscanum, Erythranthe austrolatidens, Craspedia gracilis, Pteronia onobromoides, Glycine clandestina, Glycine microphylla, Juniperus flaccida, Juniperus navicularis, Juniperus procera, Lewisia cantelovii, Macrostylis ramulosa, Thomasia rhynchocarpa, Persicaria figertii, Urochloa deflexa, Echeveria kimnachii, Echeveria juliana, Lessertia harveyana, Mimosa quadrivalvis, Mimosa mollis, Porlieria chilensis, Pteronia acuminata, Lolium perenne, Mimosa distachya, Santolina paui, Umbilicus albidoopacus, Enekbatus longistylus, Craspedia costiniana, Craspedia crocata, Luzula elegans, Luzula danica, Pterostegia drymarioides, Hoffmannseggia erecta, Camissonia sierrae, Drakaea isolata, Briza humilis, Polymeria pusilla, Kallstroemia hageri, Berteroa orbiculata, Rhaponticum pygmaeum, Pteronia diosmifolia, Pteronia glabrata, Pteronia viscosa, Santolina ericoides, Quinchamalium chilense, Kalanchoe faustii, Lessertia benguellensis)",
 "Year is greater than or equal to 1950"
 ]
}

 The dataset includes 860415 records from 3419 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0022300-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5dfpw8
title: Occurrence Download

description: A dataset containing 277968 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "Year is greater than or equal to 1930",
 "Year is less than 1940",
 {
 "or" : [
 "TaxonKey is Amphibia",
 "TaxonKey is Aves",
 "TaxonKey is Insecta",
 "TaxonKey is Mammalia",
 "TaxonKey is Fungi",
 "TaxonKey is Plantae",
 "TaxonKey is Chordata"
 ]
 },
 "HasCoordinate is true"
 ]
}

 The dataset includes 277968 records from 406 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024901-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/iw73uu/exkenm
title: NSE_day.csv

description: Uploaded with pyDataverse 2023-11-06 15:44
DOI: 10.22008/fk2/iw73uu/olqqbg
title: NSE_hour.csv

description: Uploaded with pyDataverse 2023-11-06 15:44
DOI: 10.22008/fk2/iw73uu/usygra
title: NSE_month.csv

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.22008/fk2/iw73uu/wukr5i
title: NSE_hour.nc

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.6084/m9.figshare.30809451
title: Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior

description: The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this article, we propose a novel Bayesian transfer learning method named “CONCERT” to allow robust partial information transfer for high-dimensional data analysis. A conditional spike-and-slab prior is introduced in the joint distribution of target and source parameters for information transfer. By incorporating covariate-specific priors, we can characterize partial similarities and integrate source information collaboratively to improve the performance on the target. In contrast to existing work, the CONCERT is a one-step procedure which achieves variable selection and information transfer simultaneously. We establish variable selection consistency, as well as estimation and prediction error bounds for CONCERT. Our theory demonstrates the covariate-specific benefit of transfer learning. To ensure the scalability of the algorithm, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantages of CONCERT over existing cutting-edge transfer learning methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/iw73uu/r3mfjd
title: NSE_day.nc

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.6084/m9.figshare.30809451.v1
title: Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior

description: The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this paper, we propose a novel Bayesian transfer learning method named “CONCERT” to allow robust partial information transfer for high-dimensional data analysis. A conditional spike-and-slab prior is introduced in the joint distribution of target and source parameters for information transfer. By incorporating covariate-specific priors, we can characterize partial similarities and integrate source information collaboratively to improve the performance on the target. In contrast to existing work, the CONCERT is a one-step procedure which achieves variable selection and information transfer simultaneously. We establish variable selection consistency, as well as estimation and prediction error bounds for CONCERT. Our theory demonstrates the covariate-specific benefit of transfer learning. To ensure the scalability of the algorithm, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantages of CONCERT over existing cutting-edge transfer learning methods.
DOI: 10.6084/m9.figshare.30809451.v2
title: Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior

description: The popularity of transfer learning stems from the fact that it can borrow information from useful auxiliary datasets. Existing statistical transfer learning methods usually adopt a global similarity measure between the source data and the target data, which may lead to inefficiency when only partial information is shared. In this article, we propose a novel Bayesian transfer learning method named “CONCERT” to allow robust partial information transfer for high-dimensional data analysis. A conditional spike-and-slab prior is introduced in the joint distribution of target and source parameters for information transfer. By incorporating covariate-specific priors, we can characterize partial similarities and integrate source information collaboratively to improve the performance on the target. In contrast to existing work, the CONCERT is a one-step procedure which achieves variable selection and information transfer simultaneously. We establish variable selection consistency, as well as estimation and prediction error bounds for CONCERT. Our theory demonstrates the covariate-specific benefit of transfer learning. To ensure the scalability of the algorithm, we adopt the variational Bayes framework to facilitate implementation. Extensive experiments and two real data applications showcase the validity and advantages of CONCERT over existing cutting-edge transfer learning methods. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/iw73uu/9cbidb
title: NSE_month.nc

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.22008/fk2/iw73uu/vaulws
title: NUK_K_day.csv

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.22008/fk2/iw73uu/rsam3n
title: NUK_K_hour.csv

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.22008/fk2/iw73uu/9untcq
title: NUK_K_month.csv

description: Uploaded with pyDataverse 2023-11-06 15:45
DOI: 10.22008/fk2/iw73uu/vveeeg
title: NUK_K_hour.nc

description: Uploaded with pyDataverse 2023-11-06 15:46
DOI: 10.22008/fk2/iw73uu/7wbe8g
title: NUK_K_day.nc

description: Uploaded with pyDataverse 2023-11-06 15:46
DOI: 10.22008/fk2/iw73uu/smqtx4
title: NUK_K_month.nc

description: Uploaded with pyDataverse 2023-11-06 15:46
DOI: 10.6084/m9.figshare.30764567.v1
title: Adversarial Estimation of Riesz Representers

description: Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as leading cases. Our estimators are highly compatible with targeted and debiased machine learning with sample splitting; our guarantees directly verify general conditions for inference that allow mis-specification. We also use our guarantees to prove inference without sample splitting, based on stability or complexity. Our estimators may achieve nominal coverage in highly nonlinear simulations where some previous methods may not. They shed new light on the heterogeneous effects of matching grants. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.wzbhwz
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Phlomis tuberosa)",
 "Country is one of (Norway)",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "CoordinateUncertaintyInMeters is less than or equal to 1000",
 "Year is greater than or equal to 1986",
 "Year is less than or equal to 2025",
 {
 "not" : {
 "BasisOfRecord" : [
 "is Fossil"
 ]
 }
 },
 {
 "not" : {
 "BasisOfRecord" : [
 "is Machine Observation"
 ]
 }
 }
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0024947-260208012135463/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.22008/fk2/iw73uu/kxomvs
title: NUK_L_hour.csv

description: Uploaded with pyDataverse 2023-11-06 15:46
DOI: 10.22008/fk2/iw73uu/tfqntp
title: NUK_L_day.csv

description: Uploaded with pyDataverse 2023-11-06 15:46
DOI: 10.6084/m9.figshare.30764567
title: Adversarial Estimation of Riesz Representers

description: Many causal parameters are linear functionals of an underlying regression. The Riesz representer is a key component in the asymptotic variance of a semiparametrically estimated linear functional. We propose an adversarial framework to estimate the Riesz representer using general function spaces. We prove a nonasymptotic mean square rate in terms of an abstract quantity called the critical radius, then specialize it for neural networks, random forests, and reproducing kernel Hilbert spaces as leading cases. Our estimators are highly compatible with targeted and debiased machine learning with sample splitting; our guarantees directly verify general conditions for inference that allow mis-specification. We also use our guarantees to prove inference without sample splitting, based on stability or complexity. Our estimators may achieve nominal coverage in highly nonlinear simulations where some previous methods may not. They shed new light on the heterogeneous effects of matching grants. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.22008/fk2/iw73uu/8cidqz
title: NUK_L_month.csv

description: Uploaded with pyDataverse 2023-11-06 15:46
DOI: 10.5281/zenodo.18670856
title: IMPLANT ATROFIDAGI INFEKSIYA VA SUYAK YO'QOLISHINI OLDINI OLISH, ANTIBAKTERIAL STRATEGIYALAR VA LAZER TERAPIYASI

description:    Peri-implant infections and subsequent bone loss represent significant challenges in dental implantology. Effective prevention strategies, including the use of antibacterial agents, surface modifications, and laser therapy, are crucial for maintaining implant stability and long-term success. Recent studies highlight that combining conventional antimicrobial protocols with advanced laser treatment can reduce bacterial colonization, enhance tissue healing, and preserve peri-implant bone. This review focuses on evidence-based approaches to prevent peri-implant infections, emphasizing personalized care, minimally invasive interventions, and the integration of modern technologies for optimal clinical outcomes [1, 2].
DOI: 10.5281/zenodo.18670855
title: IMPLANT ATROFIDAGI INFEKSIYA VA SUYAK YO'QOLISHINI OLDINI OLISH, ANTIBAKTERIAL STRATEGIYALAR VA LAZER TERAPIYASI

description:    Peri-implant infections and subsequent bone loss represent significant challenges in dental implantology. Effective prevention strategies, including the use of antibacterial agents, surface modifications, and laser therapy, are crucial for maintaining implant stability and long-term success. Recent studies highlight that combining conventional antimicrobial protocols with advanced laser treatment can reduce bacterial colonization, enhance tissue healing, and preserve peri-implant bone. This review focuses on evidence-based approaches to prevent peri-implant infections, emphasizing personalized care, minimally invasive interventions, and the integration of modern technologies for optimal clinical outcomes [1, 2].
DOI: 10.5281/zenodo.17164833
title: Fictional Forensic Scene (FFS) : Part3 (Th_0041-0060 scenes)

description: Fictional Forensic Scene (FFS) Dataset description.

 This dataset is derived from fictional forensic scenes (crime scenes and accident scenes, mainly inside). The aim is to be as close to reality as possible. As such, some scenes may be shocking to an uninformed audience (some mannequins have a physical appearance similar to bodies found at real crime scenes).  This dataset is licensed under CC BY-NC-SA 4.0.

The data has been classified and named in a uniform manner. All crime scenes are named in the format “th_X,” where “X” represents the scene number. The data is organized as follows: a “description” folder containing two spreadsheets describing the scenes in English and French, an ‘img’ folder containing documentation photographs for each scene, an “IPC” “ (Inventory of Evidence) containing spreadsheets listing the evidence from each scene (in English and French), a ‘pano’ folder containing black and white panoramic images, and a ”scan" folder containing data from the laser scanner (point clouds and panoramic images, in black and white and color). The scenes are available in zip packages (th_0001-th_0020, th_0021-40, th_0041-0060, th_0061-0080, th_0081-0100). When new scenes are acquired, they will be integrated into linked repositories. Currently, 100 scenes are available (Part1, Part2, Part3, Part4, Part5).

For more information about the data, you can consult the readme file.

Text data is provided in two languages (extension _ENG for English, _FR for French).
DOI: 10.5281/zenodo.18623134
title: Fictional Forensic Scene (FFS) : Part3 (Th_0041-0060 scenes)

description: Fictional Forensic Scene (FFS) Dataset description.

 This dataset is derived from fictional forensic scenes (crime scenes and accident scenes, mainly inside). The aim is to be as close to reality as possible. As such, some scenes may be shocking to an uninformed audience (some mannequins have a physical appearance similar to bodies found at real crime scenes).  This dataset is licensed under CC BY-NC-SA 4.0.

The data has been classified and named in a uniform manner. All crime scenes are named in the format “th_X,” where “X” represents the scene number. The data is organized as follows: a “description” folder containing two spreadsheets describing the scenes in English and French, an ‘img’ folder containing documentation photographs for each scene, an “IPC” “ (Inventory of Evidence) containing spreadsheets listing the evidence from each scene (in English and French), a ‘pano’ folder containing black and white panoramic images, and a ”scan" folder containing data from the laser scanner (point clouds and panoramic images, in black and white and color). The scenes are available in zip packages (th_0001-th_0020, th_0021-40, th_0041-0060, th_0061-0080, th_0081-0100). When new scenes are acquired, they will be integrated into linked repositories. Currently, 100 scenes are available (Part1, Part2, Part3, Part4, Part5).

For more information about the data, you can consult the readme file.

Text data is provided in two languages (extension _ENG for English, _FR for French).
DOI: 10.15468/dl.da6t23
title: Occurrence Download

description: A dataset containing 41443 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-103.57718 25.44725,-103.35699 25.44249,-103.28183 25.5722,-103.41368 25.70059,-103.61015 25.57458,-103.57718 25.44725))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 41443 records from 163 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004106-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ebjam4
title: Occurrence Download

description: A dataset containing 5809 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anachis",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 5809 records from 129 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004107-260221153910048/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.35099/qs52-mr68
title: Neural correlates of suppressed spatial learning in zebra finches exposed to methylmercury DATA