cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15023157
title: CAVEMOVE: An acoustic database for the study of voice-enabled technologies inside moving vehicles

description: We provide a collection of multichannel audio recordings obtained inside three different cars. The recording process involves (i) recordings of acoustic impulse responses, which are acquired at static conditions and provide the means for modeling the speech and car-audio components (ii) recordings of acoustic noise at a wide range of both static and in-motion conditions. Data is recorded with two different microphone configurations and particularly (i) a compact microphone array or (ii) a distributed microphone setup. A python API, that can be freely downloaded from CAVEMOVE github, is provided as the means to easily exploit the open access audio recordings to synthesize mixtures of speech and acoustic noise at different driving conditions. This way, the user can easily synthesize the microphone signals required for research on voice enabled technologies inside moving vehicles.

All audio recordings and impulse responses are provided at 16 kHz sampling rate and are in the form of 8-channel .wav files.

CAVEMOVE API is currently available in python, but a Matlab version will soon follow. Some basic principles followed in CAVEMOVE are the following. - We provide functions for retrieving speech and noise components as separate python entities (numpy arrays). Users must then add the speech and noise components to derive a mixture.- Noise recordings are derived as a function of driving conditions, specifically the speed (in km/hour) and the window aperture (3 or 4 different windows conditions are considered in each vehicle)-Apart from the basic noise components, we also provide means for adding ventilation/air-condition noise and also, interference from the built-in car audio system (e.g. radio, cd player etc)-To produce the speech components, users must provide their own dry speech recordings -To produce the car-audio components, users must provide their own audio signals.

The Documentation .pdf  file that we provide along with the audio recordings lists all the conditions that were recorded or measured inside the three cars  (the same documantation can also be found in CAVEMOVE github). This information is important for correct use of the python API, since, asking for a condition that was not recorded can potentially produce an error. Note that a list of recorded driving conditions given the car name and the microphone configuration can also be retrieved from auxiliary python functions included in the API.

For any questions with respect to CAVEMOVE dataset or API, feel free to send an email to Andreas Symiakakis at  andrysmi@ics.forth.gr orNikos Stefanakis at nstefana@ics.forth.gr

CAVEMOVE project is funded by the Institute of Computer Science of the Foundation for Research and Technology-Hellas (FORTH).
DOI: 10.5281/zenodo.15064194
title: CAVEMOVE: An acoustic database for the study of voice-enabled technologies inside moving vehicles

description: We provide a collection of multichannel audio recordings obtained inside three different cars. The recording process involves (i) recordings of acoustic impulse responses, which are acquired at static conditions and provide the means for modeling the speech and car-audio components (ii) recordings of acoustic noise at a wide range of both static and in-motion conditions. Data is recorded with two different microphone configurations and particularly (i) a compact microphone array or (ii) a distributed microphone setup. A python API, that can be freely downloaded from CAVEMOVE github, is provided as the means to easily exploit the open access audio recordings to synthesize mixtures of speech and acoustic noise at different driving conditions. This way, the user can easily synthesize the microphone signals required for research on voice enabled technologies inside moving vehicles.

All audio recordings and impulse responses are provided at 16 kHz sampling rate and are in the form of 8-channel .wav files.

CAVEMOVE API is currently available in python, but a Matlab version will soon follow. Some basic principles followed in CAVEMOVE are the following. - We provide functions for retrieving speech and noise components as separate python entities (numpy arrays). Users must then add the speech and noise components to derive a mixture.- Noise recordings are derived as a function of driving conditions, specifically the speed (in km/hour) and the window aperture (3 or 4 different windows conditions are considered in each vehicle)-Apart from the basic noise components, we also provide means for adding ventilation/air-condition noise and also, interference from the built-in car audio system (e.g. radio, cd player etc)-To produce the speech components, users must provide their own dry speech recordings -To produce the car-audio components, users must provide their own audio signals.

The Documentation .pdf  file that we provide along with the audio recordings lists all the conditions that were recorded or measured inside the three cars  (the same documantation can also be found in CAVEMOVE github). This information is important for correct use of the python API, since, asking for a condition that was not recorded can potentially produce an error. Note that a list of recorded driving conditions given the car name and the microphone configuration can also be retrieved from auxiliary python functions included in the API.

For any questions with respect to CAVEMOVE dataset or API, feel free to send an email to Andreas Symiakakis at  andrysmi@ics.forth.gr orNikos Stefanakis at nstefana@ics.forth.gr

CAVEMOVE project is funded by the Institute of Computer Science of the Foundation for Research and Technology-Hellas (FORTH).
DOI: 10.48597/r53u-a393
title: Measurement of Inorganics in precipitation at Campisabalos

description: Measurement of Inorganics in precipitation at Campisabalos
DOI: 10.15468/dl.2qqgzd
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dolichopus consobrinus Zetterstedt, 1859",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020437-250310093411724/datasets/export for details.
DOI: 10.48597/5xn6-mmae
title: Measurement of Inorganics in precipitation at Campisabalos

description: Measurement of Inorganics in precipitation at Campisabalos
DOI: 10.48597/6mtp-43ja
title: Measurement of Inorganics in precipitation at Campisabalos

description: Measurement of Inorganics in precipitation at Campisabalos
DOI: 10.48597/ujmu-c83b
title: Inorganics in precipitation at Campisabalos

description: Measurement of Inorganics in precipitation at Campisabalos
DOI: 10.48597/umc3-r8v5
title: Measurement of Inorganics in precipitation at Campisabalos

description: Measurement of Inorganics in precipitation at Campisabalos
DOI: 10.48597/auhm-n2xf
title: Measurement of Particle_number_concentration at Observatoire Perenne de l'Environnement

description: Measurement of Particle_number_concentration at Observatoire Perenne de l'Environnement
DOI: 10.48597/rvx2-sjq5
title: Particle_number_concentration at Observatoire Perenne de l'Environnement

description: Measurement of Particle_number_concentration at Observatoire Perenne de l'Environnement
DOI: 10.48597/s24t-cfb2
title: Measurement of Particle_number_concentration at Observatoire Perenne de l'Environnement

description: Measurement of Particle_number_concentration at Observatoire Perenne de l'Environnement
DOI: 10.25835/w85ssyb3
title: Combined-accelerometers-for-satellite-gravity-missions-study

description: The dataset is used in simulating the satellite orbit, the noise time series of the hybrid accelerometers, and the coefficients of the recovered gravity fields for the following paper: "Combined Classical and Quantum Accelerometers for Future Satellite Gravity Missions" submitted to the ESS Journal.
DOI: 10.58132/43ilce
title: How to keep teachers in a profession in times of budget shortages?

description: The data refer to a study of teachers’ preferences regarding working conditions that affect job satisfaction using the Discrete Choice Experiment (DCE) methodology, according to which respondents make choices in hypothetical situations. The data contain the results of a survey conducted among teachers in the city of Kutno, in addition to variables defining choices, also variables regarding socio-demographic characteristics are included. Variables and their type are defined in the Codebook file. This is a unique study for Poland using DCE to study teachers’ preferences.The study shows significant heterogeneity in teachers’ tastes regarding working conditions. In addition to income, teachers value other elements that can compensate for their relatively low salaries, potentially reducing the burden on state budgets compared to wage increases.
DOI: 10.15468/dl.38fb96
title: Occurrence Download

description: A dataset containing 1147 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Denmark",
 "TaxonKey is Morchella Dill. ex Pers."
 ]
}

 The dataset includes 1147 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020438-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nq7rru
title: Occurrence Download

description: A dataset containing 16 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Alopoglossus tapajosensis Ribeiro-Júnior, Sánchez-Martínez, Moraes, Oliveira, Carvalho, Pavan, Choueri, Werneck et al., 2021"
 ]
}

 The dataset includes 16 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020439-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.j8qxsw
title: Occurrence Download

description: A dataset containing 1037802 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-197.1057 -64.34472,-80.41706 -64.34472,-80.41706 -26.01114,-197.1057 -26.01114,-197.1057 -64.34472))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Insecta"
 ]
}

 The dataset includes 1037802 records from 180 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020430-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14600018
title: Training data for the shared task Ideology and Power Identification in Parliamentary Debates (2025)

description: This dataset contains a selection of speeches from ParlaMint corpora (version 4.1) as the training set for  the shared task on "Ideology and Power Identification in Parliamentary Debates" in CLEF 2025.

All files are tab-separated text files with the following fields:



"id" is a unique (arbitrary) ID for each text.

"speaker" is a unique (arbitrary) ID for each speaker. There may be multiple speeches from the same speaker.

"sex" is the (binary/biological) sex of the speaker. This information is collected from varying sources (typically data published by the respective parliament), and in some cases it may be unspecified or unknown.

"text" is the transcribed text of the parliamentary speech. Real examples may include line breaks, and other special sequences escaped or quoted.

"text_en" is an automatic English translation of the corresponding text. This field may be empty (obviously)  for speeches in English, but the translations may be missing for a small number of non-English speeches as well.

"orientation" is the binary/numeric label ( 0 is left and 1 is right). Orientation labels are based on Wikipedia.

"power" is the binary label for power role (0 is opposition, 1 is coalition), this information is based on the information provided by the ParlaMint contributors. This value is not always present, either due to parliamentary systems with no defined coalition/opposition, or unknown orientation information for some speakers (e.g., PMs with no party affilitiation). Missing values are indicated as 'NA'.

"populism" is the populism index based on Global Party Survey (GPS). This is a 4-point ordinal scale (1: Strongly Pluralist, 2: Moderately Pluralist 3: Moderately Populist, 4: Strongly Populist). Not all values are present in all parliaments. Many parties/speakers are not covered by GPS data, and some values are missing due to failure to match the GPS and ParlaMint identifiers. Missing values are indicated as 'NA'.


Small samples of the data files are provided in the shared task GitHub repository at https://github.com/coltekin/ideology-power-st-baseline.

File names include a code for the parliament. We provide data from the following national and regional parliaments.



Austria (at)

Bosnia and Herzegovina (ba)

Belgium (be)

Bulgaria (bg)

Czechia (cz)

Denmark (dk)

Estonia (ee)

Spain (es)

Catalonia (es-ct)

Galicia (es-ga)

Basque Country (es-pv)

Finland (fi)

France (fr)

Great Britain (gb)

Greece (gr)

Croatia (hr)

Hungary (hu)

Iceland (is)

Italy (it)

Latvia (lv)

The Netherlands (nl)

Norway (no)

Poland (pl)

Portugal (pt)

Serbia (rs)

Sweden (se)

Slovenia (si)

Turkey (tr)

Ukraine (ua)


The number of training instances and the class imbalance differs for each training set. We do not provide a fixed validation split. Please see the shared task website and the GitHub repository for further description of the data set and the sampling process.
DOI: 10.15468/dl.hgrwvs
title: Occurrence Download

description: A dataset containing 208 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-63.88172 -24.05925,-67.80262 -34.14156,-59.8488 -35.26182,-56.62806 -25.85166,-63.57365 -23.49912,-63.88172 -24.05925))",
 "OccurrenceStatus is Present",
 "TaxonKey is Acromyrmex Mayr, 1865"
 ]
}

 The dataset includes 208 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020440-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21242/12211.2022.00.00.1.1.0
title: Mikrozensus 2022, On-Site, Version 0

description: Der Mikrozensus ist eine seit 1957 jährlich durchgeführte Befragung von einem Prozent aller Haushalte in Deutschland. Insgesamt nehmen jährlich etwa 370.000 Haushalte mit 810.000 Personen an der Erhebung teil. Als Mehrthemenumfrage konzipiert, liefert der Mikrozensus wichtige statistische Angaben in tiefer fachlicher Gliederung über die Bevölkerungsstruktur, die wirtschaftliche und soziale Lage der Bevölkerung, der Familien und der Haushalte, den Arbeitsmarkt, die berufliche Gliederung und die Ausbildung der Erwerbsbevölkerung sowie die Wohnverhältnisse.
Für den Großteil der Fragen besteht Auskunftspflicht.
Aufgrund des breiten Spektrums an Erhebungsmerkmalen und des großen Stichprobenumfangs eignet sich der Mikrozensus für die Analyse kleinerer Subpopulationen wie einzelner Migrations- oder Berufsgruppen. Die Bedeutung von (tief) regionalisierten Analysen, beispielsweise im Hinblick auf die Lebenschancen unterschiedlicher sozialer Gruppen, rückt zunehmend in den Vordergrund wissenschaftlicher Untersuchungen. Regionalisierte Ergebnisse lassen sich zum Beispiel auf Ebene der sog. regionalen Anpassungsschichten – räumliche Einheiten von durchschnittlich 500.000 Einwohnerinnen und Einwohnern – darstellen. Darüber hinaus ermöglicht die hohe Kontinuität des Erhebungsdesigns neben Querschnittsanalysen auch Untersuchungen im Zeitverlauf (Trendanalysen), mit denen sich historische Entwicklungen aufzeigen lassen. Dabei können einige Jahre des Mikrozensus, der als rotierendes Panel angelegt ist, ebenfalls für Panelanalysen genutzt werden.
Seit 2020 umfasst der Mikrozensus zudem die zuvor eigenständigen, EU-weiten Befragungen zu Einkommen und Lebensbedingungen (EU-SILC) sowie seit 2021 zur Nutzung von Informations- und Kommunikationstechnologien (IKT). In den FDZ-On-Site-Daten sind diese Erhebungen ebenfalls vorhanden. Für den Scientific Use File (SUF) gibt es einige Einschränkungen der Verfügbarkeit. Details finden sich in den jahresspezifischen Metadatenreports der Produkte. Der Mikrozensus eignet sich auch für Vergleiche im internationalen Kontext, da für verschiedene Themenbereiche internationale Standards (z.B. Labour-Force-Konzept) existieren.
Der Mikrozensus stellt mit seinem hohen Auswahlsatz, der thematischen Breite und zeitlichen Kontinuität eine wichtige Datenquelle für die Sozialwissenschaft dar.
DOI: 10.15468/dl.f3hvp9
title: Occurrence Download

description: A dataset containing 467 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is South Africa",
 "OccurrenceStatus is Present",
 "TaxonKey is Redunca arundinum (Boddaert, 1785)"
 ]
}

 The dataset includes 467 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020441-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48522/d3rp4w
title: Florida Coral Disturbance Response Monitoring (DRM) Program 2020-2023

description: The Disturbance Response Monitoring (DRM) program was developed to annually assess coral condition during the months of peak thermal stress along Florida’s Coral Reef (FCR). The 2005 severe coral bleaching event in the Florida Keys prompted the establishment of the DRM program, which has continued to implement these surveys every year. The DRM program surveys coral populations using a probabilistic sampling design based on how corals are distributed spatially within and across different regions, sub-regions, and zones of FCR. The DRM program provides an annual demographic and condition assessment for all corals in &lt;60ft, including all SCTLD susceptible coral species found in Florida.DRM is a collaborative effort among local, state, and federal environmental managers, scientists, conservation organizations, and coral restoration practitioners, all of whom are driven by a common goal of providing valuable and timely coral condition data to all reef stakeholders. The primary goal of the DRM program has always been to provide a condition report on the annual status of coral bleaching along the reef tract. This information is used to identify resilient areas of the reef, promote appropriate management or conservation strategies for reef areas based on resilience, and aid management in research and restoration decisions. Although historically focused on bleaching, the DRM program has been very responsive in adapting its experimental design to account for emerging stressors impacting the reef including disease.
DOI: 10.6084/m9.figshare.28639455
title: S1: <i>In situ</i> garnet Lu–Hf geochronology and constraints on metamorphism in the northernmost Norwegian Caledonides

description: Supplementary material 1, EPMA
DOI: 10.6084/m9.figshare.28639455.v1
title: S1: <i>In situ</i> garnet Lu–Hf geochronology and constraints on metamorphism in the northernmost Norwegian Caledonides

description: Supplementary material 1, EPMA
DOI: 10.6084/m9.figshare.28639458
title: S3: <i>In situ</i> garnet Lu–Hf geochronology and constraints on metamorphism in the northernmost Norwegian Caledonides

description: Supplementary material 3, Monazite U–Pb CK003
DOI: 10.6084/m9.figshare.28639458.v1
title: S3: <i>In situ</i> garnet Lu–Hf geochronology and constraints on metamorphism in the northernmost Norwegian Caledonides

description: Supplementary material 3, Monazite U–Pb CK003
DOI: 10.6084/m9.figshare.28639461
title: S2: <i>In situ</i> garnet Lu–Hf geochronology and constraints on metamorphism in the northernmost Norwegian Caledonides

description: Supplementary material 2, Garnet Lu–Hf
DOI: 10.6084/m9.figshare.28639461.v1
title: S2: <i>In situ</i> garnet Lu–Hf geochronology and constraints on metamorphism in the northernmost Norwegian Caledonides

description: Supplementary material 2, Garnet Lu–Hf
DOI: 10.15468/dl.mc36em
title: Occurrence Download

description: A dataset containing 1629 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diplolepis mayri (Schlechtendal, 1877)",
 "HasGeospatialIssue is false",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1629 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020442-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hwcejt
title: Occurrence Download

description: A dataset containing 93557 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 Phyteuma orbiculare L."
 ]
}

 The dataset includes 93557 records from 473 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0020435-250310093411724/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.469mx5
title: Occurrence Download

description: A dataset containing 24196 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Phyteuma hemisphaericum L.",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 24196 records from 218 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004947-240130105604617/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zqvbsf
title: Occurrence Download

description: A dataset containing 10 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-76.52 -2.074,-75.99 -2.074,-75.99 2.83,-76.52 2.83,-76.52 -2.074))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Pentacalia trichopus (Benth.) Cuatrec."
 ]
}

 The dataset includes 10 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0113421-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.lvtaid
title: Occurrence Download

description: A dataset containing 3203 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lemna aequinoctialis Welw."
 ]
}

 The dataset includes 3203 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0045475-181108115102211/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vsnwkw
title: Occurrence Download

description: A dataset containing 274 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is New Caledonia",
 "DatasetKey is Campagnes REMMOA (REcensement des Mammifères marins et autre Mégafaune pélagique par Observation Aérienne) - Données d'occurrence de mammifères marins des campagnes REMMOA - Observatoire PELAGIS UMS 3462 et Agence des aires marines protégées",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 274 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0081915-231120084113126/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.