cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.31135000.v1
title: Dataset, R code and READ ME files associated to the manuscript entitled "Mitochondrial respiratory capacities are not linked to individual variation in hypoxia tolerance and standard metabolic rate in European seabass <i>Dicentrarchus labrax</i>"

description: In hypoxic environments, fish attempt to regulate their metabolic rate as oxygen levels in the water decline. Individuals that have low energy requirements for maintenance - standard metabolic rate (SMR) - are expected to have higher hypoxia tolerance, measured as the critical water oxygen saturation threshold (S<sub>crit</sub>) below which they cannot regulate SMR. These whole animal traits may, in turn, be linked to aspects of individual mitochondrial oxidative metabolism that underpin SMR. We therefore investigated, in European seabass <i>Dicentrarchus labrax</i>, whether inter-individual variation in SMR and S<sub>crit</sub>, measured by whole animal respirometry, were correlated with elements of mitochondrial metabolism in liver and heart, measured by high resolution respirometry (HRR). Among the 96 fish studied, inter-individual variation in S<sub>crit</sub> was dependent upon variation in SMR. However, there was no evidence for correlations between SMR or S<sub>crit</sub> and individual mitochondrial respiratory capacities. Our results indicate that individual variation in complex whole animal traits of respiratory metabolism cannot always be linked to variation in subcellular mitochondrial phenotypes.
DOI: 10.15468/dl.9bdt2u
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "Geometry POLYGON ((3.9258348597680164 46.61866702870583, 3.9245073861996254 46.627417656862036, 3.920726548909882 46.63582169386309, 3.9146365959238287 46.64355591770548, 3.906470867453778 46.65032272182473, 3.8965429348437715 46.65586161050386, 3.885234586142929 46.659959278327634, 3.872981114384553 46.66245787585011, 3.8602544799137233 46.663261133374434, 3.847545010532952 46.662338098687584, 3.8353423676038605 46.65972433927549, 3.8241165388008884 46.65552056086811, 3.8142996170280523 46.649888697640115, 3.806269090152869 46.64304563039754, 3.800333299749466 46.63525478314195, 3.7967196328428705 46.626815931463184, 3.7955658940143158 46.6180536248437, 3.7969151724363925 46.60930467654647, 3.800714376175364 46.600905207582215, 3.806816461083378 46.59317774451321, 3.8149862399700174 46.58641886463853, 3.824909524845208 46.58088785728113, 3.8362052352286726 46.576796827996006, 3.84844000215375 46.57430261557071, 3.8611447129196446 46.573500822109885, 3.8738323774160013 46.57442217693566, 3.8860166538838397 46.577031368306244, 3.897230350778573 46.58122838593559, 3.9070432221662568 46.58685232491718, 3.915078396806979 46.59368751084011, 3.9210268255397893 46.6014717195618, 3.9246591972659624 46.609906186147974, 3.9258348597680164 46.61866702870583))",
 "KingdomKey is one of (Animalia, Fungi, Plantae)",
 "Year is one of (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025, 2026)",
 "Modified is greater than or equal to 2026-01-13"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006922-260120142942310/datasets/export for details.
DOI: 10.15468/dl.vn8uur
title: Occurrence Download

description: A dataset containing 199 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-52.50167 47.63234,-79.03431 72.81587,-169.725 72.06636,-168.52578 51.52979,-130.45069 46.28322,-112.01274 15.10361,-76.03627 1.61244,-51.00265 2.51185,-80.08363 30.84332,-52.50167 47.63234))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Bimastos rubidus"
 ]
}

 The dataset includes 199 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006921-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21950/l4eoxq
title: Smart Rural IoT Lab - Dataset de Tráfico Benigno IoT # Fase 1 #
title: Capturas reales de tráfico de red en un entorno IoT rural inteligente (backend remoto en CPD universitario)

description: El dataset contiene exclusivamente tráfico de red benigno, capturado durante el funcionamiento normal del Smart Rural IoT Lab,
sin inyección de ataques ni generación de tráfico malicioso.

Está compuesto por 13 sesiones de captura independientes, correspondientes a diferentes jornadas de operación normal del laboratorio.
Cada sesión se almacena en una carpeta separada, siguiendo una nomenclatura basada en la fecha de captura.

Características principales:
- Tráfico real.
- Backend remoto (CPD universitario).
- Entorno controlado.
- Tráfico exclusivamente benigno.
- Dataset de referencia (baseline).
description: Los autores desean agradecer el apoyo del Gobierno de España (a través del Instituto Nacional de Ciberseguridad de España, INCIBE), con el Proyecto de Investigación Estratégico “Análisis de aplicaciones móviles desde la perspectiva de la protección de datos: Ciberprotección y Ciberriesgos de la información ciudadana”, así como la Cátedra Internacional “Smart Rural IoT y Entornos Seguros”, en el contexto del Plan de Recuperación, Transformación y Resiliencia financiado por la Unión Europea (NextGenerationEU/PRTR).
description: tcpdump
description: zeek
DOI: 10.15468/dl.g9ak95
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.69808999999999 24.902374, 89.62067399999999 24.896796, 89.545185 24.880204, 89.473499 24.853009, 89.407397 24.815887, 89.34851399999999 24.769762, 89.298304 24.715777, 89.258 24.655271, 89.228584 24.589742, 89.21076600000001 24.52081, 89.204966 24.450176, 89.211309 24.379581, 89.22961599999999 24.310761, 89.259421 24.245407, 89.299975 24.185122, 89.35027100000001 24.131381, 89.409068 24.0855, 89.47492099999999 24.048598, 89.546218 24.021578, 89.621217 24.005098, 89.69808999999999 23.99956, 89.774963 24.005098, 89.849962 24.021578, 89.92125900000001 24.048598, 89.987112 24.0855, 90.04590899999999 24.131381, 90.096205 24.185122, 90.136759 24.245407, 90.16656399999999 24.310761, 90.184871 24.379581, 90.191214 24.450176, 90.18541399999999 24.52081, 90.167596 24.589742, 90.13818000000001 24.655271, 90.097876 24.715777, 90.04766600000001 24.769762, 89.988783 24.815887, 89.922681 24.853009, 89.850995 24.880204, 89.77550599999999 24.896796, 89.69808999999999 24.902374))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006923-260120142942310/datasets/export for details.
DOI: 10.15468/dl.h8vgsy
title: Occurrence Download

description: A dataset containing 77906370 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 77906370 records from 3264 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006876-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3p3kgp
title: Occurrence Download

description: A dataset containing 787 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Machine Observation, Human Observation, Living Specimen)",
 "TaxonKey is Mauremys leprosa leprosa"
 ]
}

 The dataset includes 787 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005652-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5522/04/30676463
title: Data repository for paper: "Variational approach to open quantum systems with long-range competing interactions"

description: Simulation data for paper: "Variational approach to open quantum systems with long-range competing interactions". Full article accessible under https://arxiv.org/abs/2510.01543<br><b>Overview: </b>This datasets contains simulation data presented in figures 1-6 in the Main Text and figures 2-61 in the Supplementary Material. This data was obtained from t-VMC+MPO (the method introduced in the above article) and t-MPS simulations. t-VMC+MPO is a method for efficient and scalable simulation of the dynamics of open quantum lattices. It relies on solving the variational equations of motion efficiently by means of time-dependent variational Monte Carlo (t-VMC), while employing compact matrix product operator (MPO) trial states for the many-body density matrix. Among its other advantages, this method is suitable for simulating large driven-dissipative lattices with multiple long-ranged interactions.<b>Implementation: </b>A Julia implementation of t-VMC+MPO (termed t-VMPOMC), which was used to generate this dataset, is available at the public GitHub repository https://github.com/dhryniuk/t-VMPOMC. This repository contains example simulation scripts aimed to make the method accessible for first-time users. <b>Directory and file organisation:</b> For the Main text, the data is divided first by figure/subplot index, and second by simulation type (please refer to the relevant figure in the article). For the Supplementary Material, the data is divided first by the optimisation hyperparameter label, for which convergence was analysed, and second by model. The folder names specify the model via initialism: I - Ising, C - competing, XYZ - the XYZ model, e.g. the folder name "CI_N200_strong" will contain simulation data for an spin chain of N=200 sites with strong long-ranged competing Ising interactions. Relevant parameter and hyperparameter values distinguishing individual simulations are included in the folder titles. Remaining parameter values not specified can be found in tables 1 and 2 in Supplementary Note 3 on pages 57-59. Explanation on t-VMPOMC simulations data file names and structure: "obs.out" - m_x, m_y, m_z, S_2 expectation values per iteration; "corrD.out" - D_local C_xx, C_yy, C_zz correlation functions per iteration; "times.out" - recorded simulation time t per iteration, "iter_times.out" - wall time per iteration; "C.out" - cost function value per iteration; "lambda.out" - smallest eigenvalue of reduced density matrix per iteration; "Schmidt_values.out" - Schmidt singular values per iteration.<br>
DOI: 10.5522/04/30676463.v1
title: Data repository for paper: "Variational approach to open quantum systems with long-range competing interactions"

description: Simulation data for paper: "Variational approach to open quantum systems with long-range competing interactions". Full article accessible under https://arxiv.org/abs/2510.01543<br><b>Overview: </b>This datasets contains simulation data presented in figures 1-6 in the Main Text and figures 2-61 in the Supplementary Material. This data was obtained from t-VMC+MPO (the method introduced in the above article) and t-MPS simulations. t-VMC+MPO is a method for efficient and scalable simulation of the dynamics of open quantum lattices. It relies on solving the variational equations of motion efficiently by means of time-dependent variational Monte Carlo (t-VMC), while employing compact matrix product operator (MPO) trial states for the many-body density matrix. Among its other advantages, this method is suitable for simulating large driven-dissipative lattices with multiple long-ranged interactions.<b>Implementation: </b>A Julia implementation of t-VMC+MPO (termed t-VMPOMC), which was used to generate this dataset, is available at the public GitHub repository https://github.com/dhryniuk/t-VMPOMC. This repository contains example simulation scripts aimed to make the method accessible for first-time users. <b>Directory and file organisation:</b> For the Main text, the data is divided first by figure/subplot index, and second by simulation type (please refer to the relevant figure in the article). For the Supplementary Material, the data is divided first by the optimisation hyperparameter label, for which convergence was analysed, and second by model. The folder names specify the model via initialism: I - Ising, C - competing, XYZ - the XYZ model, e.g. the folder name "CI_N200_strong" will contain simulation data for an spin chain of N=200 sites with strong long-ranged competing Ising interactions. Relevant parameter and hyperparameter values distinguishing individual simulations are included in the folder titles. Remaining parameter values not specified can be found in tables 1 and 2 in Supplementary Note 3 on pages 57-59. Explanation on t-VMPOMC simulations data file names and structure: "obs.out" - m_x, m_y, m_z, S_2 expectation values per iteration; "corrD.out" - D_local C_xx, C_yy, C_zz correlation functions per iteration; "times.out" - recorded simulation time t per iteration, "iter_times.out" - wall time per iteration; "C.out" - cost function value per iteration; "lambda.out" - smallest eigenvalue of reduced density matrix per iteration; "Schmidt_values.out" - Schmidt singular values per iteration.<br>
DOI: 10.17026/ar/8tcv6v
title: Hofstede Elsenburg anno 1870
title: Historische studie in het kader van de herbestemming

description: Landschap, frans-classicistische tuin tot 1744, vroeg-landschapspark, stichting van de boerderij 1870, boeren bij zwaar weer. Ook bouwkundige details
description: Opdrachtgever toenmalig eigenaar Kees Spelt
DOI: 10.15468/dl.exzacp
title: Occurrence Download

description: A dataset containing 23 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Coccinula quatuordecimpustulata",
 "BasisOfRecord is Specimen",
 "LifeStage is Adult",
 "MediaType is Image"
 ]
}

 The dataset includes 23 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005653-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25921/502b-2s30
title: Sensory and gene expression impacts of laboratory experimental ocean acidification conditions in juvenile Arctic cod (Boreogadus saida) from 2019-04-05 to 2019-07-16 (NCEI Accession 0292280)

description: This dataset contains data from manipulated experimental seawater chemistry conditions and juvenile Arctic cod (Boreogadus saida) sensory and gene impacts. The experiment took place from April 5-July 16, 2019 in the Alaska Fisheries Science Center laboratory research facilities at Hatfield Marine Science Center in Newport, Oregon. Juveniles were reared in the laboratory, and were the offspring of naturally spawning, laboratory-acclimated broodstock adults. Experiments occurred on juveniles for up to 16 weeks at ambient (~616 µatm) and elevated (~2565 µatm) CO2 levels. This effort was conducted in support of the research objectives of the NOAA Ocean Acidification Program (OAP).
DOI: 10.15468/dl.anb84u
title: Occurrence Download

description: A dataset containing 173254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Thunnus albacares"
 ]
}

 The dataset includes 173254 records from 137 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005617-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13849272
title: IndicSTR12: Indic Scene Text Recognition
DOI: 10.48420/30666095
title: <b>Intensive care decision-making, survival and dying well:</b><b>How do the experiences of intensive care patients and their end-of-life wishes affect their willingness to accept intensive care treatment at different chances of survival?</b>

description: Many critically unwell patients who are faced with the decision of whether or not to accept intensive care treatment have no previous experiences of what it is like to be an intensive care patient. The experiences of previous intensive care patients are, therefore, a valuable source on information for patients facing this important life or death decision. This study investigated how the experiences of intensive care patients and their end-of-life wishes affect their willingness to accept intensive care treatment at different chances of survival.This is a mixed method study (utilising qualitative and quantitative methods), based on a pragmatic approach focussing on the best methodological approach to answering my research questions, rather than based on a commitment to any particular metaphysical concern or paradigm. A purposeful sample of patients was recruited through ICU recovery clinics and ICU steps groups. The mixed methods approach involves triangulation of: · Thematic analysis of semi-structured interviews. · Quantification of patient’s ICU experiences through the ICU Memory Tool, their functional status before and after their critical illness through the EQ-5D-5L tool, their preferences for end of life using the Concept of a Good Death Measure, and whether their ICU experience would have been a good death using the QODD questionnaire. · Integration of quantitative and qualitative data by asking “what chance of survival would make you willing to go through that experience again?” The use of quantitative data allows for a “thick description” to support the transferability of the findings of this study and analytic generalisations resulting from it.<br>Research QuestionsWhat can ICU survivors’ experiences of ICU treatment tell us about what it would be like to die whilst receiving ICU treatment?How do ICU survivors’ reflections about what it might be like to die on ICU relate to their own preferences for their end-of-life care?What chance of survival would make ICU survivors willing to go through ICU treatment again, in light of the fact that the alternative chance is dying whilst experiencing ICU treatment?How does the possibility of reduction in health-related quality of life and functional decline as a result of critical illness impact ICU survivors’ willingness to accept ICU treatment again?
DOI: 10.48420/30666095.v1
title: <b>Intensive care decision-making, survival and dying well:</b><b>How do the experiences of intensive care patients and their end-of-life wishes affect their willingness to accept intensive care treatment at different chances of survival?</b>

description: Many critically unwell patients who are faced with the decision of whether or not to accept intensive care treatment have no previous experiences of what it is like to be an intensive care patient. The experiences of previous intensive care patients are, therefore, a valuable source on information for patients facing this important life or death decision. This study investigated how the experiences of intensive care patients and their end-of-life wishes affect their willingness to accept intensive care treatment at different chances of survival.This is a mixed method study (utilising qualitative and quantitative methods), based on a pragmatic approach focussing on the best methodological approach to answering my research questions, rather than based on a commitment to any particular metaphysical concern or paradigm. A purposeful sample of patients was recruited through ICU recovery clinics and ICU steps groups. The mixed methods approach involves triangulation of: · Thematic analysis of semi-structured interviews. · Quantification of patient’s ICU experiences through the ICU Memory Tool, their functional status before and after their critical illness through the EQ-5D-5L tool, their preferences for end of life using the Concept of a Good Death Measure, and whether their ICU experience would have been a good death using the QODD questionnaire. · Integration of quantitative and qualitative data by asking “what chance of survival would make you willing to go through that experience again?” The use of quantitative data allows for a “thick description” to support the transferability of the findings of this study and analytic generalisations resulting from it.<br>Research QuestionsWhat can ICU survivors’ experiences of ICU treatment tell us about what it would be like to die whilst receiving ICU treatment?How do ICU survivors’ reflections about what it might be like to die on ICU relate to their own preferences for their end-of-life care?What chance of survival would make ICU survivors willing to go through ICU treatment again, in light of the fact that the alternative chance is dying whilst experiencing ICU treatment?How does the possibility of reduction in health-related quality of life and functional decline as a result of critical illness impact ICU survivors’ willingness to accept ICU treatment again?
DOI: 10.15468/dl.5pkbkh
title: Occurrence Download

description: A dataset containing 7158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Xenodacnis parina"
 ]
}

 The dataset includes 7158 records from 24 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005654-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25921/x7ga-4g25
title: pH and other trajectory profile observations made from Slocum glider um_242 in the Gulf of Maine, using a Seabird Electronics ISFET-based pH/ctd instrument from 2021-11-05 to 2021-12-09 (NCEI Accession 0295428)

description: This dataset contains the trajectory profile data from Slocum glider um_242 from one seasonal deployment (2021-11-05 to 2021-12-09) to measure pH and other parameters in the Gulf of Maine. The glider was equipped with a Seabird Electronics CTD modified to include an Ion Sensitive Field Effect Transistor (ISFET) pH sensor within the pumped sample stream. Also included on this glider were a WETLabs ECO puck measuring chlorophyll fluorescence and optical backscatter, and an Aanderaa Optode for measuring dissolved oxygen. This deployment began near Mount Desert Isle, made transects of some of the deeper basins of Gulf of Maine, and finished near Monhegan Island.
DOI: 10.25921/g0bm-hz91
title: pH and other trajectory profile observations made from Slocum glider um_242 in the Gulf of Maine using a Seabird Electronics ISFET-based pH/ctd instrument, from 2022-05-13 to 2022-06-19 (NCEI Accession 0295429)

description: This dataset contains the trajectory profile data from Slocum glider um_242 from one seasonal deployment (2022-05-13 to 2022-06-19) to measure pH and other parameters in the Gulf of Maine. The glider was equipped with a Seabird Electronics CTD modified to include an Ion Sensitive Field Effect Transistor (ISFET) pH sensor within the pumped sample stream. Also included on this glider were a WETLabs ECO puck measuring chlorophyll fluorescence and optical backscatter, and an Aanderaa Optode for measuring dissolved oxygen. This deployment began near Mount Desert Isle, made transects of some of the deeper basins of Gulf of Maine, and finished near Monhegan Island.