cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6tsdgm
title: Occurrence Download

description: A dataset containing 281 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is true",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Adelges piceae (Ratzeburg, 1844), Adelges tsugae Annand, 1924, Agrilus, Agrilus Curtis, 1825, Agrilus Dahl, 1823, Agrilus Gemminger & Harold, 1869, Agrilus, Anoplophora Hope, 1839, Anoplophora F.Sandberger, 1864, Anoplophora Alberti, 1864, Bretziella fagacearum (Bretz) Z.W.de Beer, Marinc., T.A.Duong & M.J.Wingf., Choristoneura conflictana (Walker, 1863), Choristoneura fumiferana (Clemens, 1865), Choristoneura occidentalis Freeman, 1967, Choristoneura occidentalis (Walsingham, 1891), Choristoneura pinus Freeman, 1953, Dendroctonus frontalis Zimmermann, 1868, Dendroctonus ponderosae Hopkins, 1902, Dendroctonus rufipennis Kirby, 1837, Heterobasidion irregulare Garbel. & Otrosina, Ips typographus (C.Linnaeus, 1758), Lycorma delicatula (White, 1845), Lymantria dispar dispar, Lymantria dispar asiatica Wnukowsky, 1926, Malacosoma disstria Hübner, Nothophaeocryptopus gaeumannii (T.Rohde) Videira, C.Nakash., U.Braun & Crous, Ophiostoma Rudolphi, 1801, Ophiostoma Syd. & P.Syd., Ophiostoma, Phytophthora ramorum Werres, De Cock & Man)"
 ]
}

 The dataset includes 281 records from 28 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049917-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uy8v0t
title: Occurrence Download

description: A dataset containing 1608 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyperus involucratus Rottb."
 ]
}

 The dataset includes 1608 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007396-160526112335914/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eacixc
title: Occurrence Download

description: A dataset containing 2331 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crotalus lepidus Kennicott, 1861"
 ]
}

 The dataset includes 2331 records from 71 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007395-171219132708484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.47t6aw
title: Occurrence Download

description: A dataset containing 22728 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Cyperus longus L."
 ]
}

 The dataset includes 22728 records from 529 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019709-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q7n6ge
title: Occurrence Download

description: A dataset containing 165442 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is one of (Africa, Europe, North America, Oceania, South America)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is one of (Echinochloa crus-gallii L., Echinochloa crus-galli (L.) P.Beauv.)"
 ]
}

 The dataset includes 165442 records from 1222 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031260-231002084531237/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h67q2w
title: Occurrence Download

description: A dataset containing 108083 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Country is India",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 108083 records from 46 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060238-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.br4rhk
title: Occurrence Download

description: A dataset containing 9274 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Machine Observation, Specimen, Occurrence evidence)",
 "Continent is Asia",
 "Country is one of (Chinese Taipei, China, Japan, Hong Kong, Viet Nam, Thailand, Myanmar, India)",
 "TaxonKey is Callosciurus erythraeus (Pallas, 1779)"
 ]
}

 The dataset includes 9274 records from 47 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060246-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s7fb2u
title: Occurrence Download

description: A dataset containing 2453 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Atlapetes nationi (P.L.Sclater, 1881)"
 ]
}

 The dataset includes 2453 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060247-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15151/esrf-es-2185986186
title: IBS BAG

description: The IBS BAG includes teams from IBS (Grenoble) and external groups from CEA-Cadarache (St Paul lez Durance) and IRBA (Brétigny-sur-Orge). We study the structure-function relationships of proteins with a focus on human health, biotechnology and methodology. Most of the projects are a continuation of the MX-2586 proposal and can be grouped under the following themes: host-pathogen interaction, new therapeutic targets, fundamental cellular mechanisms, membrane proteins, biotechnological applications and methodology. Some of these projects use integrative approaches (X-ray crystallography, cryo-EM, SAXS and NMR) together with other biophysical and biochemical studies and, more recently, the use of AI (AlphaFold) to fully understand the complexity of the biological macromolecular systems under study.
DOI: 10.6084/m9.figshare.29087236
title: BOPIM: Bayesian Optimization for Influence Maximization on Temporal Networks

description: The goal of influence maximization (IM) is to select a small set of seed nodes which maximizes the spread of influence on a network. In this work, we propose BOPIM, a Bayesian Optimization (BO) algorithm for IM on temporal networks. The IM task is well-suited for a BO solution due to its expensive and complicated objective function. There are at least two key challenges, however, that must be overcome, primarily due to the inputs coming from a cardinality-constrained, non-Euclidean, combinatorial space. The first is constructing the kernel function for the Gaussian Process regression. We propose two kernels, one based on the Hamming distance between seed sets and the other leveraging the Jaccard coefficient between node’s neighbors. The second challenge is the acquisition function. For this, we use the Expected Improvement function, suitably adjusting for noise in the observations, and optimize it using a greedy algorithm to account for the cardinality constraint. In numerical experiments on real-world networks, we prove that BOPIM outperforms competing methods and yields comparable influence spreads to a gold-standard greedy algorithm while being as much as 10 times faster. In addition, we find that the Hamming kernel performs favorably compared to the Jaccard kernel in nearly all settings, a somewhat surprising result as the former does not explicitly account for the graph structure. Finally, we demonstrate two ways that the proposed method can quantify uncertainty in optimal seed sets. To our knowledge, this is the first attempt to look at uncertainty in the seed sets for IM.
DOI: 10.6084/m9.figshare.29087236.v2
title: BOPIM: Bayesian Optimization for Influence Maximization on Temporal Networks

description: The goal of influence maximization (IM) is to select a small set of seed nodes which maximizes the spread of influence on a network. In this work, we propose BOPIM, a Bayesian Optimization (BO) algorithm for IM on temporal networks. The IM task is well-suited for a BO solution due to its expensive and complicated objective function. There are at least two key challenges, however, that must be overcome, primarily due to the inputs coming from a cardinality-constrained, non-Euclidean, combinatorial space. The first is constructing the kernel function for the Gaussian Process regression. We propose two kernels, one based on the Hamming distance between seed sets and the other leveraging the Jaccard coefficient between node’s neighbors. The second challenge is the acquisition function. For this, we use the Expected Improvement function, suitably adjusting for noise in the observations, and optimize it using a greedy algorithm to account for the cardinality constraint. In numerical experiments on real-world networks, we prove that BOPIM outperforms competing methods and yields comparable influence spreads to a gold-standard greedy algorithm while being as much as 10 times faster. In addition, we find that the Hamming kernel performs favorably compared to the Jaccard kernel in nearly all settings, a somewhat surprising result as the former does not explicitly account for the graph structure. Finally, we demonstrate two ways that the proposed method can quantify uncertainty in optimal seed sets. To our knowledge, this is the first attempt to look at uncertainty in the seed sets for IM.
DOI: 10.6084/m9.figshare.29087236.v1
title: BOPIM: Bayesian Optimization for influence maximization on temporal networks

description: The goal of influence maximization (IM) is to select a small set of seed nodes which maximizes the spread of influence on a network. In this work, we propose BOPIM, a Bayesian Optimization (BO) algorithm for IM on temporal networks. The IM task is well-suited for a BO solution due to its expensive and complicated objective function. There are at least two key challenges, however, that must be overcome, primarily due to the inputs coming from a cardinality-constrained, non-Euclidean, combinatorial space. The first is constructing the kernel function for the Gaussian Process regression. We propose two kernels, one based on the Hamming distance between seed sets and the other leveraging the Jaccard coefficient between node’s neighbors. The second challenge is the acquisition function. For this, we use the Expected Improvement function, suitably adjusting for noise in the observations, and optimize it using a greedy algorithm to account for the cardinality constraint. In numerical experiments on real-world networks, we prove that BOPIM outperforms competing methods and yields comparable influence spreads to a gold-standard greedy algorithm while being as much as ten times faster. In addition, we find that the Hamming kernel performs favorably compared to the Jaccard kernel in nearly all settings, a somewhat surprising result as the former does not explicitly account for the graph structure. Finally, we demonstrate two ways that the proposed method can quantify uncertainty in optimal seed sets. To our knowledge, this is the first attempt to look at uncertainty in the seed sets for IM.
DOI: 10.17882/107137
title: SATS (Santander Atlantic Time-Series) Observatory Data

description: As part of the long-term ocean observation program of the Spanish Institute of Oceanography (IEO-CSIC), the SATS (Santander Atlantic Time-Series) Eulerian time series has conducted monthly multidisciplinary monitoring at a deep-ocean station since 1994. In addition to regular CTD profiles, an oceanographic-meteorological buoy—equipped with atmospheric, oceanographic, and biogeochemical sensors—has been deployed at a depth of 2850 m near this deep station. Together, these datasets form the SATS long-term ocean observatory, which is designed to monitor:
The evolution of water mass properties (Eastern North Atlantic Central Water – ENACW, Mediterranean Water – MW, and Labrador Sea Water – LSW), The vertical structure of the upper ocean (mixing depth and stratification), Changes in oxygen content,  And the variability of ocean climate and biogeochemistry in response to air–sea interactions, mixing processes, and circulation dynamics.
DOI: 10.15468/dl.8ce3kq
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Luticola D.G.Mann, 1990",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((20.2855 -34.7533,20.509 -34.5676,20.509 -32.9007,17.7723 -32.9007,17.8156 -33.0786,18.3471 -33.7176,18.3748 -33.8287,18.2181 -34.0492,18.3197 -34.3408,18.4452 -34.429,18.5435 -34.4213,18.5872 -34.3667,18.5363 -34.167,18.7345 -34.1508,18.7216 -34.3637,18.7808 -34.4521,19.0289 -34.4206,19.0831 -34.4835,19.2655 -34.5017,19.2032 -34.6157,19.2394 -34.6926,19.375 -34.6963,19.6091 -34.8524,19.8251 -34.8396,19.9798 -34.9064,20.1081 -34.8731,20.1517 -34.7847,20.2855 -34.7533))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060249-250525065834625/datasets/export for details.
DOI: 10.15468/dl.54xqu5
title: Occurrence Download

description: A dataset containing 2820 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Material sample)"
 ]
 }
 },
 "TaxonKey is Campethera maculosa (Valenciennes, 1826)",
 "Continent is one of (Africa)",
 "Year is greater than or equal to 1900"
 ]
}

 The dataset includes 2820 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060250-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7910/dvn/oj1jdv
title: WxEM Wave 7

description: This report describes the results of a regularly distributed survey of nationwide Emergency Managers (EMs) as part of The Extreme Weather and Emergency Management Survey (WxEM) series. This project aims to send surveys to EMs across the United States three to four times a year, although that frequency may change based on EM and research needs. The Extreme Weather and Emergency Management Survey, Wave 7 (WxEM Wave 7) was designed by the Institute for Public Policy Research and Analysis (IPPRA) at the University of Oklahoma and collaborators from the Global System Laboratory and the Cooperative Institute for Research in the Atmosphere. It is the seventh survey in the series. WxEM Wave 7 opened on February 19, 2025, using an online questionnaire and has been completed by 292 EM personnel that were recruited from an IPPRA built database of EMs from across the country (see Wanless et al. 2023a for more info on recruitment). WxEM Wave 7 follows surveys that covered a variety of topics, working to better understand Emergency Management operations, especially in the weather space. Wave 7 investigated Emergency Manager operations during wildfire events, especially their reception, use, and sharing of fire weather forecast information. This report presents an overview of the methodology of the survey data collection, and a reproduction of the survey instrument with frequencies for the questions that elicited numeric responses.
DOI: 10.34740/kaggle/ds/7663960
title: asl-68287-clean-28x28
DOI: 10.6084/m9.figshare.29371472
title: Dataset: <b>Intervenciones de enfermería en Atención Primaria en Salud para la reducción de daños asociados al consumo de drogas: Scoping review</b>

description: El presente dataset corresponde a los datos obtenidos en el estudio producto del trabajo de grado titulado “Intervenciones de enfermería en APS para la reducción de daños asociados al consumo de drogas. Scoping review” de la Facultad de Enfermería de la Fundación Universitaria Sanitas y no requirió de financiación.<b>Objetivo:</b> Describir las intervenciones de enfermería que se pueden realizar desde la Atención Primaria en Salud en personas consumidoras de drogas, para la reducción los daños asociados al consumo reportadas en la literatura.<b>Método:</b> Revisión de alcance que se desarrolló de acuerdo con las etapas establecidas por Arksey y O'Malley para la elaboración de una scoping review.<b>Resultados:</b> Se realizó búsqueda de literatura en las bases de datos PubMed, BVS, ProQuest y el metabuscador de Google Académico, en las que se identificaron 1.518 estudios que luego de una fase de cribado se seleccionaron 12 publicaciones para el análisis, las cuales permitieron determinar las intervenciones realizadas por los profesionales de enfermería<b> </b>para la reducción de daños asociados al consumo de drogas. A partir del análisis de contenido surgieron cinco categorías: intervenciones educativas/psicoeducativas, intervenciones clínicas, intervenciones de apoyo emocional y motivacional, intervenciones preventivas e intervenciones prácticas para la mitigación de riesgos.
DOI: 10.6084/m9.figshare.29371472.v1
title: Dataset: <b>Intervenciones de enfermería en Atención Primaria en Salud para la reducción de daños asociados al consumo de drogas: Scoping review</b>

description: El presente dataset corresponde a los datos obtenidos en el estudio producto del trabajo de grado titulado “Intervenciones de enfermería en APS para la reducción de daños asociados al consumo de drogas. Scoping review” de la Facultad de Enfermería de la Fundación Universitaria Sanitas y no requirió de financiación.<b>Objetivo:</b> Describir las intervenciones de enfermería que se pueden realizar desde la Atención Primaria en Salud en personas consumidoras de drogas, para la reducción los daños asociados al consumo reportadas en la literatura.<b>Método:</b> Revisión de alcance que se desarrolló de acuerdo con las etapas establecidas por Arksey y O'Malley para la elaboración de una scoping review.<b>Resultados:</b> Se realizó búsqueda de literatura en las bases de datos PubMed, BVS, ProQuest y el metabuscador de Google Académico, en las que se identificaron 1.518 estudios que luego de una fase de cribado se seleccionaron 12 publicaciones para el análisis, las cuales permitieron determinar las intervenciones realizadas por los profesionales de enfermería<b> </b>para la reducción de daños asociados al consumo de drogas. A partir del análisis de contenido surgieron cinco categorías: intervenciones educativas/psicoeducativas, intervenciones clínicas, intervenciones de apoyo emocional y motivacional, intervenciones preventivas e intervenciones prácticas para la mitigación de riesgos.
DOI: 10.34973/4t8w-hg78
title: Constructing others’ beliefs from one’s own using medial frontal cortex

description: Many daily choices are based on one’s own knowledge. However, when predicting other people’s behavior, we need to consider the differences between our knowledge and other people’s presumed knowledge. Social agents need a mechanism to use privileged information for their own behavior but exclude it from predictions of others. Using fMRI, we investigated the neural implementation of such social and personal  predictions by manipulating privileged and shared information. The medial frontal cortex appeared to have an important role in flexibly making decisions using privileged information for oneself or predicting others behavior. Specifically, we show that ventromedial prefrontal cortex tracked the state of the world independent of the type of decision (personal, social), whereas dorsomedial regions adjusted their frame of reference to the use of privileged or shared information. Sampling privileged evidence not available to the confederate also relied on specific interactions between TPJ and frontal pole.
DOI: 10.34973/cesj-dk54
title: Measuring inter-individual differences in stress sensitivity during MR-guided prostate biopsy

description: People often experience high level of distress during invasive interventions, which may exceed their coping abilities. This may be in particular evident when confronted with the suspicion of cancer. Taking the example of prostate biopsy sampling, we aimed at investigating the impact of an MRI guided prostate biopsy on the acute stress response and its mechanistic basis.
We recruited 20 men with a clinical suspicion of prostate cancer. Next to an MRI guided biopsy procedure, we simultaneously conducted fMRI to assess resting-state brain connectivity. Physiological and hormonal stress measures were taken during the procedure and associated with questionnaires, hair cortisol levels and brain measures to elucidate mechanistic factors for elevated stress. 
As expected, patients reported a stress-related change in affect. Decreased positive affect was associated with higher hair but not saliva cortisol concentration. Stronger use of maladaptive emotion regulation techniques, elevated depression scores and higher within-salience-network connectivity was associated with stronger increase in negative affect and/or decrease of positive affect during the procedure.
While being limited in its generalization due to age, sample size and gender, our proof of concept study demonstrates the utility of real-life stressors and large-scale brain network measures in stress regulation research with potential impact in clinical practice.
DOI: 10.15468/dl.b8be7z
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Luticola D.G.Mann, 1990",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Living Specimen, Observation, Human Observation, Machine Observation, Material sample, Literature Occurrence, Material citation, Occurrence evidence)",
 "Geometry POLYGON ((35.628 -23.023,35.6007 -22.9702,35.6007 -23.165,35.628 -23.023))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060251-250525065834625/datasets/export for details.
DOI: 10.34973/6xvp-qp82
title: How does choice affect curiosity?

description: In our connected era, we spend significant time and effort pursuing our curiosity. Often, we choose which information we seek, but sometimes, the selection is made for us. We hypothesized that humans exhibit enhanced curiosity in the context of choice. We designed a task in which healthy participants saw two lotteries on each trial. On some trials, participants chose which lottery to play. On other trials, the lottery was selected for them. Participants then indicated their curiosity about the outcome of the lottery to be played via self-report ratings (Experiment 1, N=34) or willingness to wait decisions (Experiment 2, N=34). We found that participants exhibited higher curiosity ratings and higher willingness to wait for the outcome of lotteries they had chosen than for lotteries that had been selected for them (controlling for initial preference). This demonstrates that choice boosts curiosity, which may have implications to boost learning, memory, and motivation.
DOI: 10.15468/dl.bderbn
title: Occurrence Download

description: A dataset containing 6822 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Amaranthus palmeri S.Watson"
 ]
}

 The dataset includes 6822 records from 294 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0060253-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.