cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.cm4ft4
title: Occurrence Download

description: A dataset containing 3856 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Amblygobius phalaena"
 ]
}

 The dataset includes 3856 records from 94 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052537-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7807/microm:suf:v9
title: RWI-GEO-GRID: Socio-economic data on grid level- Scientific Use File (wave 9)
title: Sozioökonomische Daten auf Rasterebene - Scientific Use File (Welle 9)

description: Die Basis des Datensatzes bildet die Variablengruppe microm-Basis. Diese ist in die folgenden vier Unterklassen unterteilt: Anzahl der Haushalte, Anzahl der Gewerbebetriebe, Anzahl der Häuser (inklusive reiner Gewerbehäuser) und Anzahl der Wohngebäude (exklusive reiner Gewerbehäuser) (vgl. microm 2016, S. 26). Die Anzahl der Häuser wird auf Grundlage der Straßenabschnitte für alle übergeordneten Gebietsebenen ermittelt. Basierend auf Unternehmensregistern werden für jedes Haus Aussagen zu der Anzahl der Gewerbebetriebe gemacht. Neben diesen Basisvariablen beinhaltet der Datensatz die geografische Kennziffer für den Rasterpunkt, Informationen zur PKW-Struktur, Kaufkraft, Ausländeranteil, Haushaltsstruktur, Kindern, Arbeitslosenquote, Einwohner nach Geschlecht und Alter, Ethnologien und Zahlungsausfallrisiko.
description: The dataset is based on the variable gourp microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined. Moreover, the dataset contains the geographical key of the raster point als well as information on cars, purchasing power, foreigners, household structure, children, unemployment rate, population, ethnic background, credit default risk.
description: RWI-GEO-GRID
description: RWI-GEO-GRID
description: In die Datenerstellung fließen mehr als eine Milliarde Einzelinformationen ein. Diese Daten liegen anonymisiert vor und werden von mehreren Quellen bezogen. Die einfließenden Daten liegen grundsätzlich für sämtliche 40.9 Mio. Haushalte in Deutschland vor und werden für die rund 20 Mio. Häuser ausgewiesen (microm 2016, S. 8).
description: Mircom uses more than a billion individual datapoints for the aggregation of the microm dataset. These are anonymised and stem from various data sources. The datatapoints are are available for all 40.9 million households in Germany, while thefinal data product contains information on approximately 20 million houses (microm 2016, p. 8).
DOI: 10.15468/dl.d5rxv3
title: Occurrence Download

description: A dataset containing 3847 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Abelmoschus moschatus"
 ]
}

 The dataset includes 3847 records from 220 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052538-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48544/71df100e-fcad-4ed9-8b60-1cd02a6ab135
title: Experimental automatic steering control in a real-scale boat
DOI: 10.13140/rg.2.2.11279.24488
title: traditional and smart roofs
DOI: 10.6084/m9.figshare.30427849
title: VER.2: Data on arthropod incidence data from the project 'LandKlif', traits averaged over families, summarised datasets and R code

description: We compared an indicator for community homogenization, the distance decay slope between four local habitats of increasing land-use intensity, from forests to managed grasslands, to arable lands and to settlements, imbedded in near-natural, agricultural and urban regions (see more details https://www.landklif.biozentrum.uni-wuerzburg.de/). This comparison was based on 12k arthropod species from 400 families, covering an area of 70.500 km<sup>2</sup>.<br>Available data includes the used incidence data ('incidence_tables_all'), summarised datasets for different analysis types ('used datasets'), arthropod OTU information ('Metabarcoding_Landklif_Malaise_results'), and the used R code for the project ('Distancedecay_Incidence_finalSubmission').
DOI: 10.6084/m9.figshare.30427849.v1
title: VER.2: Data on arthropod incidence data from the project 'LandKlif', traits averaged over families, summarised datasets and R code

description: We compared an indicator for community homogenization, the distance decay slope between four local habitats of increasing land-use intensity, from forests to managed grasslands, to arable lands and to settlements, imbedded in near-natural, agricultural and urban regions (see more details https://www.landklif.biozentrum.uni-wuerzburg.de/). This comparison was based on 12k arthropod species from 400 families, covering an area of 70.500 km<sup>2</sup>.<br>Available data includes the used incidence data ('incidence_tables_all'), summarised datasets for different analysis types ('used datasets'), arthropod OTU information ('Metabarcoding_Landklif_Malaise_results'), and the used R code for the project ('Distancedecay_Incidence_finalSubmission').
DOI: 10.11588/portal.gsi.68fa26a62c8ea
title: Verlagsanstalt für Kunst und Wissenschaft, vormals Friedrich Bruckmann
DOI: 10.15468/dl.y3rv2j
title: Occurrence Download

description: A dataset containing 81 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Italy",
 "OccurrenceStatus is Present",
 "TaxonKey is Papilio alexanor"
 ]
}

 The dataset includes 81 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052539-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.757aq3
title: Occurrence Download

description: A dataset listing the 32655 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is The fossil collection (F) of the Muséum national d'Histoire naturelle (MNHN - Paris)"
 ]
}

 The dataset's 32655 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052532-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48544/24dde711-0b61-4d95-bb1c-c19cd22393a8
title: Autonomous shipping technology: implications in logistics chains, and a case for adoption in Zeeland
DOI: 10.15468/dl.8a9pwx
title: Occurrence Download

description: A dataset containing 1826 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Bhutan",
 "TaxonKey is Lophophorus impejanus"
 ]
}

 The dataset includes 1826 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052541-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13012/b2idb-9547404_v1
title: Metabolome analysis of the effects of distress and sex on the liver

description: Hepatic profiling of metabolites in the liver of female and male pigs exposed to the effects of distress
DOI: 10.6084/m9.figshare.30158301
title: Solving Bayesian Inverse Problems Using Gaussian Process Regression with Goal-Oriented Active Learning

description: Sequential design focuses on designing computer experiments with limited computational budgets. It aims to create efficient surrogate models to replace complex computer codes. Some sequential design strategies can be understood within the Stepwise Uncertainty Reduction (SUR) framework. In the SUR framework, each new design point is chosen by minimizing the expectation of a metric of uncertainty with respect to the yet unknown new data point. These methods offer an accessible framework for sequential experiment design, including almost sure convergence for common uncertainty functionals. This article introduces two goal-oriented strategies tailored for surrogate models used within a Bayesian inverse problem. The Constraint Set Query (CSQ) strategy is adapted from Maximum Mean Squared Error (MMSE) designs, where the search space is constrained in a ball for the Mahalanobis distance around the maximum a posteriori. The second, known as the IP-SUR (Inverse Problem SUR) strategy, uses a posterior-weighted integrated mean squared prediction error as the uncertainty metric and is derived from SUR methods. It is tractable for Gaussian process surrogates and comes with a theoretical guarantee for the almost sure convergence of the uncertainty functional. In various test cases, both strategies are shown to outperform standard goal-oriented designs for fine-tuning surrogate models.
DOI: 10.6084/m9.figshare.30158301.v1
title: Solving Bayesian inverse problems using Gaussian Process Regression with goal-oriented active learning

description: Sequential design focuses on designing computer experiments with limited computational budgets. It aims to create efficient surrogate models to replace complex computer codes. Some sequential design strategies can be understood within the Stepwise Uncertainty Reduction (SUR) framework. In the SUR framework, each new design point is chosen by minimizing the expectation of a metric of uncertainty with respect to the yet unknown new data point. These methods offer an accessible framework for sequential experiment design, including almost sure convergence for common uncertainty functionals. This paper introduces two goal-oriented strategies tailored for surrogate models used within a Bayesian inverse problem. The Constraint Set Query (CSQ) strategy is adapted from Maximum Mean Squared Error (MMSE) designs, where the search space is constrained in a ball for the Mahalanobis distance around the maximum a posteriori. The second, known as the IP-SUR (Inverse Problem SUR) strategy, uses a posterior-weighted integrated mean squared prediction error as the uncertainty metric and is derived from SUR methods. It is tractable for Gaussian process surrogates and comes with a theoretical guarantee for the almost sure convergence of the uncertainty functional. In various test cases, both strategies are shown to outperform standard goal-oriented designs for fine-tuning surrogate models.
DOI: 10.6084/m9.figshare.30158301.v2
title: Solving Bayesian Inverse Problems Using Gaussian Process Regression with Goal-Oriented Active Learning

description: Sequential design focuses on designing computer experiments with limited computational budgets. It aims to create efficient surrogate models to replace complex computer codes. Some sequential design strategies can be understood within the Stepwise Uncertainty Reduction (SUR) framework. In the SUR framework, each new design point is chosen by minimizing the expectation of a metric of uncertainty with respect to the yet unknown new data point. These methods offer an accessible framework for sequential experiment design, including almost sure convergence for common uncertainty functionals. This article introduces two goal-oriented strategies tailored for surrogate models used within a Bayesian inverse problem. The Constraint Set Query (CSQ) strategy is adapted from Maximum Mean Squared Error (MMSE) designs, where the search space is constrained in a ball for the Mahalanobis distance around the maximum a posteriori. The second, known as the IP-SUR (Inverse Problem SUR) strategy, uses a posterior-weighted integrated mean squared prediction error as the uncertainty metric and is derived from SUR methods. It is tractable for Gaussian process surrogates and comes with a theoretical guarantee for the almost sure convergence of the uncertainty functional. In various test cases, both strategies are shown to outperform standard goal-oriented designs for fine-tuning surrogate models.
DOI: 10.15468/dl.52rt85
title: Occurrence Download

description: A dataset containing 1369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((14.472172011742465 47.93995829101244, 14.509100447908315 47.93844763565008, 14.511314106697265 47.96346866267661, 14.474367655622489 47.964980172659615, 14.472172011742465 47.93995829101244))"
 ]
}

 The dataset includes 1369 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052543-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ut2rtj
title: Occurrence Download

description: A dataset containing 264 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Geometry POLYGON((-12.45459 39.43913,4.39235 39.43913,4.39235 46.05673,-12.45459 46.05673,-12.45459 39.43913))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Agriades pyrenaica",
 "Year 2000-2025"
 ]
}

 The dataset includes 264 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052545-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17425446
title: NICAM simulation data used in a manuscript entitled "Impact of Aggregational Growth Modeling on the Intensification of Extreme Tropical Cyclones through Radiative Feedbacks"

description: This dataset includes outputs simulated in NICAM, targeting extremely intense tropical cyclones (TCs): BOLAVEN (2023), MAWAR (2023), NANMADOL (2022), RAI (2021), and SURIGAE (2021). Files in "TCcenter" include center location, center sea level pressure, and its 12 hourly mean value at each time step for observation data and each simulation data. Directories in "CTL" and "SIMPLE" include output variables in each simulation. These outputs are azimuthally averaged around the TC center.
DOI: 10.15468/dl.d49d96
title: Occurrence Download

description: A dataset containing 39205 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fragaria virginiana",
 "BasisOfRecord is one of (Specimen, Human Observation, Occurrence evidence, Material sample)",
 "HasCoordinate is true",
 "Country is one of (Canada, United States of America, Mexico)"
 ]
}

 The dataset includes 39205 records from 228 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052544-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7807/microm:v10
title: RWI-GEO-GRID: Socio-economic data on grid level(wave 10)
title: Sozioökonomische Daten auf Rasterebene(Welle 10)

description: Die Basis des Datensatzes bildet die Variablengruppe microm-Basis. Diese ist in die folgenden vier Unterklassen unterteilt: Anzahl der Haushalte, Anzahl der Gewerbebetriebe, Anzahl der Häuser (inklusive reiner Gewerbehäuser) und Anzahl der Wohngebäude (exklusive reiner Gewerbehäuser) (vgl. microm 2016, S. 26). Die Anzahl der Häuser wird auf Grundlage der Straßenabschnitte für alle übergeordneten Gebietsebenen ermittelt. Basierend auf Unternehmensregistern werden für jedes Haus Aussagen zu der Anzahl der Gewerbebetriebe gemacht. Neben diesen Basisvariablen beinhaltet der Datensatz die geografische Kennziffer für den Rasterpunkt, Informationen zur PKW-Struktur, Kaufkraft, Ausländeranteil, Haushaltsstruktur, Kindern, Arbeitslosenquote, Einwohner nach Geschlecht und Alter, Ethnologien und Zahlungsausfallrisiko.
description: The dataset is based on the variable gourp microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined. Moreover, the dataset contains the geographical key of the raster point als well as information on cars, purchasing power, foreigners, household structure, children, unemployment rate, population, ethnic background, credit default risk.
description: RWI-GEO-GRID
description: RWI-GEO-GRID
description: In die Datenerstellung fließen mehr als eine Milliarde Einzelinformationen ein. Diese Daten liegen anonymisiert vor und werden von mehreren Quellen bezogen. Die einfließenden Daten liegen grundsätzlich für sämtliche 40.9 Mio. Haushalte in Deutschland vor und werden für die rund 20 Mio. Häuser ausgewiesen (microm 2016, S. 8).
description: Mircom uses more than a billion individual datapoints for the aggregation of the microm dataset. These are anonymised and stem from various data sources. The datatapoints are are available for all 40.9 million households in Germany, while thefinal data product contains information on approximately 20 million houses (microm 2016, p. 8).
DOI: 10.15468/dl.rzhmjp
title: Occurrence Download

description: A dataset containing 3069 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Asterropteryx semipunctata"
 ]
}

 The dataset includes 3069 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052547-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qeaxms
title: Occurrence Download

description: A dataset containing 18446 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Fragaria vesca",
 "BasisOfRecord is one of (Specimen, Human Observation, Occurrence evidence, Material sample)",
 "HasCoordinate is true",
 "Country is one of (Canada, United States of America, Mexico)"
 ]
}

 The dataset includes 18446 records from 206 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052548-251009101135966/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7807/microm:suf:v10
title: RWI-GEO-GRID: Socio-economic data on grid level - Scientific Use File(wave 10)
title: Sozioökonomische Daten auf Rasterebene - Scientific Use File(Welle 10)

description: Die Basis des Datensatzes bildet die Variablengruppe microm-Basis. Diese ist in die folgenden vier Unterklassen unterteilt: Anzahl der Haushalte, Anzahl der Gewerbebetriebe, Anzahl der Häuser (inklusive reiner Gewerbehäuser) und Anzahl der Wohngebäude (exklusive reiner Gewerbehäuser) (vgl. microm 2016, S. 26). Die Anzahl der Häuser wird auf Grundlage der Straßenabschnitte für alle übergeordneten Gebietsebenen ermittelt. Basierend auf Unternehmensregistern werden für jedes Haus Aussagen zu der Anzahl der Gewerbebetriebe gemacht. Neben diesen Basisvariablen beinhaltet der Datensatz die geografische Kennziffer für den Rasterpunkt, Informationen zur PKW-Struktur, Kaufkraft, Ausländeranteil, Haushaltsstruktur, Kindern, Arbeitslosenquote, Einwohner nach Geschlecht und Alter, Ethnologien und Zahlungsausfallrisiko.
description: The dataset is based on the variable gourp microm-Basis which is comprised of four categories: number of households, number of business enterprises, number of houses (incl. those purely used for business), and number of residential houses (excl. those purely used for business) (cf. microm 2016, p. 26). The number of houses on the street segment level is the basis for all aggregations to other regional levels. Based on business registers, the number of enterprises in each house is determined. Moreover, the dataset contains the geographical key of the raster point als well as information on cars, purchasing power, foreigners, household structure, children, unemployment rate, population, ethnic background, credit default risk.
description: RWI-GEO-GRID
description: RWI-GEO-GRID
description: In die Datenerstellung fließen mehr als eine Milliarde Einzelinformationen ein. Diese Daten liegen anonymisiert vor und werden von mehreren Quellen bezogen. Die einfließenden Daten liegen grundsätzlich für sämtliche 40.9 Mio. Haushalte in Deutschland vor und werden für die rund 20 Mio. Häuser ausgewiesen (microm 2016, S. 8).
description: Mircom uses more than a billion individual datapoints for the aggregation of the microm dataset. These are anonymised and stem from various data sources. The datatapoints are are available for all 40.9 million households in Germany, while thefinal data product contains information on approximately 20 million houses (microm 2016, p. 8).
DOI: 10.17877/tudodata-2025-me6wl02r
title: Replication Data for: "Bridging the Attitude-Behaviour Gap. An explanation of travel mode choice using analytical sociology"

description: The dataset contains a selection of raw, processed and aggregated survey data as supplementary material for the publication "Bridging the Attitude-Behaviour Gap. An explanation of travel mode choice using analytical sociology". 

The data was collected in spring 2021 through a large-scale mobility survey among members of three major universities in the Ruhr district (Bochum, Dortmund and Duisburg-Essen). The survey was part of the InnaMoRuhr project ('Concept of an integrated, sustainable mobility for the University Alliance Ruhr'). A total of 10,782 students and employees participated in the survey.

The data includes both attitudinal and behavioural data (e.g. subjective expected utility of different modes of transport, and main mode of transport).
description: New Version created, added details of published related text publication
DOI: 10.48544/789f479b-165f-43d3-a8ff-8d01e3efc75b
title: The business case for autonomous transport in the port of Vlissingen
DOI: 10.5281/zenodo.17425640
title: Sentiment analysis of the medical literature on MACS 2016 to 2025

description: PubMed search: "Cushing AND ((mild AND autonomous) OR subclinical) AND (treatment OR therapy OR management OR surgery) AND human".Scope: 431 abstracts identified; 200 analyzed (published 2016-2025).Sentiment scoring: AI tools (ChatGPT and Grok) classified abstracts regarding surgical management.+1: Positive (favors surgery).0: Neutral (no clear stance or balanced).-1: Negative (does not favor surgery, e.g., highlights risks or prefers non-surgical options).Irrelevant abstracts: Excluded from scoring.Inter-rater reliability: Cohen's kappa = 0.26 (fair agreement between AI tools).Trend analysis: Abstracts ordered chronologically (reverse: 2025 to 2016).

Total scored abstracts: 152/200 (48 irrelevant).Distribution:Positive (+1, favors surgery): 65 (43%).Neutral (0): 78 (51%).Negative (-1, does not favor surgery): 9 (6%).

Negative sentiments (-1): Absent pre-2019; emerged thereafterTrend interpretation: No linear increase in negative sentiments, but appearance post-2019 suggests growing scrutiny of surgery or interest in non-surgical