cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.30540464
title: <b>Intra-trophic microbial associations bridge metallic micronutrients and ecosystem multifunctionality</b>

description: The role of metallic micronutrients in regulating the soil microbiome and ecosystem multifunctionality is virtually unknown, limiting our ability to predict the future sustainability of terrestrial ecosystems under global change. Here we conducted a standardized field survey across China with 261 soil samples to investigate how metallic micronutrients (Fe, Mn, Cu, Zn, Mo, Ni) shape ecosystem multifunctionality. Our results revealed that bacterial communities were more sensitive to metallic microelements than eukaryotic groups (including fungi, protozoa, nematodes, and invertebrates). Although soil biodiversity exhibited taxa-specific responses to microelements, only available Zn consistently enhanced biodiversity of the overall community. Metallic micronutrients regulated ecosystem multifunctionality through dual mechanisms within microbial networks: promoting functioning by enhancing connectivity within trophic levels, yet suppressing it by reducing dominant phyla. This establishes intra-trophic networks as the key bridge linking micronutrient availability to multifunctionality. Together, these findings provide a mechanistic understanding essential for predicting and managing soil health in the face of changing micronutrient availability.
DOI: 10.6084/m9.figshare.30540464.v1
title: <b>Intra-trophic microbial associations bridge metallic micronutrients and ecosystem multifunctionality</b>

description: The role of metallic micronutrients in regulating the soil microbiome and ecosystem multifunctionality is virtually unknown, limiting our ability to predict the future sustainability of terrestrial ecosystems under global change. Here we conducted a standardized field survey across China with 261 soil samples to investigate how metallic micronutrients (Fe, Mn, Cu, Zn, Mo, Ni) shape ecosystem multifunctionality. Our results revealed that bacterial communities were more sensitive to metallic microelements than eukaryotic groups (including fungi, protozoa, nematodes, and invertebrates). Although soil biodiversity exhibited taxa-specific responses to microelements, only available Zn consistently enhanced biodiversity of the overall community. Metallic micronutrients regulated ecosystem multifunctionality through dual mechanisms within microbial networks: promoting functioning by enhancing connectivity within trophic levels, yet suppressing it by reducing dominant phyla. This establishes intra-trophic networks as the key bridge linking micronutrient availability to multifunctionality. Together, these findings provide a mechanistic understanding essential for predicting and managing soil health in the face of changing micronutrient availability.
DOI: 10.5281/zenodo.17531854
title: Replication Materials for paper "Vally Koubi (2005) : War and Economic Performance: Journal of Peace Research. DOI: 10.1177/0022343305049667"

description: Replication data and materials for "Vally Koubi (2005) : War and Economic Performance: Journal of Peace Research. DOI: 10.1177/0022343305049667"
DOI: 10.5281/zenodo.17531855
title: Replication Materials for paper "Vally Koubi (2005) : War and Economic Performance: Journal of Peace Research. DOI: 10.1177/0022343305049667"

description: Replication data and materials for "Vally Koubi (2005) : War and Economic Performance: Journal of Peace Research. DOI: 10.1177/0022343305049667"
DOI: 10.15468/dl.s2sbv6
title: Occurrence Download

description: A dataset containing 1417 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Tapinoma darioi, Tapinoma magnum, Tapinoma nigerrimum, Tapinoma ibericum)",
 "Year 1767-2025"
 ]
}

 The dataset includes 1417 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017594-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.g8czpd
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017595-251025141854904/datasets/export for details.
DOI: 10.25375/uct.30533219
title: COVID-19 vaccination and healthcare expenditure among insured South Africans, 2020–2022

description: This dataset contains de-identified demographic and insurance claims data from two large private medical schemes (n = 550,332) in South Africa between March 2020 and December 2022. It includes vaccination records, hospitalization costs, comorbidity indicators, and demographic variables used to estimate vaccination uptake and financial burden under a luck-egalitarian framework.
DOI: 10.25375/uct.30533219.v1
title: COVID-19 vaccination and healthcare expenditure among insured South Africans, 2020–2022

description: This dataset contains de-identified demographic and insurance claims data from two large private medical schemes (n = 550,332) in South Africa between March 2020 and December 2022. It includes vaccination records, hospitalization costs, comorbidity indicators, and demographic variables used to estimate vaccination uptake and financial burden under a luck-egalitarian framework.
DOI: 10.5281/zenodo.17521936
title: IPCC AR6 Quantification Summary

description: IPCC AR6 quantification summary
DOI: 10.5281/zenodo.17521935
title: IPCC AR6 Quantification Summary

description: IPCC AR6 quantification summary
DOI: 10.21227/19md-fh44
title: "Geophysics-informed invertible operator networks for solving Bayesian geophysical inverse problem"

description: "Geophysical inverse problems are inherently ill-posed due to sparse observation and measurement noise. Classical sampling-based Bayesian inference method, such as Markov-chain Monte Carlo (MCMC), are computationally prohibitive because they requires a large number of forward simulations. Data-driven machine learning methods often struggle to yield physically consistent solutions under limited training data scenarios. To address these issues, we propose geophysics-informed invertible operator networks (GI-ION) that integrates invertible neural networks (INNs) with the deep operator network (DeepONet) framework for efficient Bayesian geophysical inversion. The key idea is to employ an INN as the branch network, which transforms the model parameters into two latent variables: the expansion coefficients representing the solution to the forward problem, and a latent noise that capture the inherent uncertainty associated with the inverse problem. By accurately learning the forward operator and enforcing statistical independence between these latent variables, GI-ION enables real-time and efficient sampling from target posterior distributions, even under sparse boundary observations. We validated GI-ION on three representative cases: 1-D surface-wave dispersion, and travel-time tomography for a 2-D synthetic random field model and overthrust field model, respectively. Numerical experiments demonstrate that GI-ION consistently produces efficient, robust, and scalable results across diverse problem settings."
DOI: 10.6084/m9.figshare.28309295
title: ARCANE - Early Detection of Interplanetary Coronal Mass Ejections

description: Material for https://github.com/hruedisser/arcane
DOI: 10.6084/m9.figshare.28309295.v6
title: ARCANE - Early Detection of Interplanetary Coronal Mass Ejections

description: Material for https://github.com/hruedisser/arcane
DOI: 10.6084/m9.figshare.28309295.v5
title: ARCANE - Early Detection of Interplanetary Coronal Mass Ejections

description: Material for https://github.com/hruedisser/arcane
DOI: 10.15468/dl.msbqps
title: Occurrence Download

description: A dataset containing 231 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "TaxonKey is Lophocladia lallemandii"
 ]
}

 The dataset includes 231 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017596-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8agbk7
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017597-251025141854904/datasets/export for details.
DOI: 10.15468/dl.8db8ks
title: Occurrence Download

description: A dataset containing 4361727 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Desmophyllum pertusum, Madrepora oculata, Enallopsammia rostrata, Solenosmilia variabilis, Dendrophyllia cornigera, Dendrophyllia ramea, Desmophyllum dianthus, Callogorgia verticillata, Paragorgia arborea, Paramuricea placomus, Primnoa resedaeformis, Acanella arbuscula, Isidella elongata, Keratoisis, Viminella flagellum, Corallium rubrum, Corallium johnsoni, Leiopathes glaberrima, Antipathella subpinnata, Stichopathes, Antipathes dichotoma)",
 "HasCoordinate is true",
 "Geometry POLYGON((-40 36, 15 36, 15 72, -40 72, -40 36))"
 ]
}

 The dataset includes 4361727 records from 92 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017584-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17493398
title: SOMAR SA – Measurement data from rolling mill vibration sensors
title: SOMAR SA - Dane pomiarowe z czujników wibracji walcarki

description: Vibration pattern during progressive operational damage of the roll stand shaft bearing in the process of rolling steel bars

The *.csv files contain raw measurement data from WS-VT1 vibration and temperature sensors manufactured by SOMAR SA, installed on the roll stand components during the steel bar rolling process. The data cover the period of progressive operational degradation of the shaft bearing.

Files: C06.csv, C07.csv, C08.csv – vibration and temperature measurement results.

Data fields are separated by tab characters.

Data structure: [sample ID] [date] [sensor ID] [–] [vibration MAX] [vibration RMS] [temperature °C] [battery V] [RSSI dBm] [date and time (datetime)]

Example: 15011090 2019-07-16 00:00:01.2+02 6 0 759 216 34 3.62 -60 2019-07-16 00:00:01.737151+02
description: Przebieg drgań w okresie postępującego uszkodzenia eksploatacyjnego łożyska wału klatki walcowniczej przy walcowaniu prętów stalowych

Pliki *.csv zawierają surowe dane pomiarowe z czujników wibracji i temperatury WS-VT1 firmy SOMAR SA, zainstalowanych na elementach klatki walcowniczej podczas walcowania prętów stalowych. Dane obejmują okres postępującego uszkodzenia eksploatacyjnego łożyska wału.

Pliki: C06.csv, C07.csv, C08.csv – wyniki pomiarów drgań i temperatury.

Pola danych są rozdzielane tabulatorem (znakiem tab).

Struktura danych: [ID próbki] [data] [ID czujnika] [–] [wibracje MAX] [wibracje RMS] [temperatura °C] [bateria V] [RSSI dBm] [data i czas (datetime)]

Przykład: 15011090 2019-07-16 00:00:01.2+02 6 0 759 216 34 3.62 -60 2019-07-16 00:00:01.737151+02
description: SOMAR SPÓŁKA AKCYJNA

ul. Karoliny 4,

40-186 Katowice, Polska 

 

KRS: 0000407623

NIP: 6340196270

REGON: 003601740 

 

www.somar.com.pl 
description: System Diagnostyki Maszyn SDM-1 był rozwijany w ramach projektu „Sprzętowo-programowy system diagnostyki maszyn i urządzeń bazujących na bezprzewodowej sieci czujników monitorujących oraz metodach inżynierii wiedzy i inteligencji obliczeniowej” współfinansowanego z Europejskiego Funduszu Rozwoju Regionalnego zgodnie z umową nr POIR.01.01.01-00-0304/19-00 z dnia 30.12.2019.
DOI: 10.5281/zenodo.17493399
title: SOMAR SA – Measurement data from rolling mill vibration sensors
title: SOMAR SA - Dane pomiarowe z czujników wibracji walcarki

description: Vibration pattern during progressive operational damage of the roll stand shaft bearing in the process of rolling steel bars

The *.csv files contain raw measurement data from WS-VT1 vibration and temperature sensors manufactured by SOMAR SA, installed on the roll stand components during the steel bar rolling process. The data cover the period of progressive operational degradation of the shaft bearing.

Files: C06.csv, C07.csv, C08.csv – vibration and temperature measurement results.

Data fields are separated by tab characters.

Data structure: [sample ID] [date] [sensor ID] [–] [vibration MAX] [vibration RMS] [temperature °C] [battery V] [RSSI dBm] [date and time (datetime)]

Example: 15011090 2019-07-16 00:00:01.2+02 6 0 759 216 34 3.62 -60 2019-07-16 00:00:01.737151+02
description: Przebieg drgań w okresie postępującego uszkodzenia eksploatacyjnego łożyska wału klatki walcowniczej przy walcowaniu prętów stalowych

Pliki *.csv zawierają surowe dane pomiarowe z czujników wibracji i temperatury WS-VT1 firmy SOMAR SA, zainstalowanych na elementach klatki walcowniczej podczas walcowania prętów stalowych. Dane obejmują okres postępującego uszkodzenia eksploatacyjnego łożyska wału.

Pliki: C06.csv, C07.csv, C08.csv – wyniki pomiarów drgań i temperatury.

Pola danych są rozdzielane tabulatorem (znakiem tab).

Struktura danych: [ID próbki] [data] [ID czujnika] [–] [wibracje MAX] [wibracje RMS] [temperatura °C] [bateria V] [RSSI dBm] [data i czas (datetime)]

Przykład: 15011090 2019-07-16 00:00:01.2+02 6 0 759 216 34 3.62 -60 2019-07-16 00:00:01.737151+02
description: SOMAR SPÓŁKA AKCYJNA

ul. Karoliny 4,

40-186 Katowice, Polska 

 

KRS: 0000407623

NIP: 6340196270

REGON: 003601740 

 

www.somar.com.pl 
description: System Diagnostyki Maszyn SDM-1 był rozwijany w ramach projektu „Sprzętowo-programowy system diagnostyki maszyn i urządzeń bazujących na bezprzewodowej sieci czujników monitorujących oraz metodach inżynierii wiedzy i inteligencji obliczeniowej” współfinansowanego z Europejskiego Funduszu Rozwoju Regionalnego zgodnie z umową nr POIR.01.01.01-00-0304/19-00 z dnia 30.12.2019.
DOI: 10.6084/m9.figshare.30417055
title: Simulated scenarios

description: Simulated scenarios for metric development
DOI: 10.6084/m9.figshare.30417055.v5
title: Simulated scenarios

description: Simulated scenarios for metric development
DOI: 10.6084/m9.figshare.30417055.v4
title: Simulated scenarios

description: Simulated scenarios for metric development
DOI: 10.5281/zenodo.17462479
title: SOMAR SA - Vibration sensors measurement data from industrial fans
title: SOMAR SA - Dane pomiarowe z czujników wibracji wentylatorów przemysłowych

description: Files S74.csv and S76.csv contain raw measurement data from SOMAR SA WS-VT1 vibration and temperature sensors.

The sensors were installed on the motors of industrial gas furnace fans used to preheat non-ferrous metal billets before plastic forming.

The data structure in each file is as follows:

[date and time];[temperature °C];[maximum vibration acceleration (mg)];[RMS vibration acceleration (mg)]

Example:

2024-11-07 07:00:18;18.3;55;45
description: Pliki S74.csv i S76.csv zawierają surowe dane pomiarowe z czujników wibracji i temperatury WS-VT1 firmy SOMAR SA.

Czujniki zostały zainstalowane na silnikach przemysłowych wentylatorów pieców gazowych, które służą do podgrzewania kęsów metali nieżelaznych przed obróbką plastyczną.

Struktura danych w pliku jest następująca:

[data i czas];[temperatura °C];[maksymalne przyspieszenie drgań (mg)];[wartość skuteczna przyspieszenia drgań (mg)]

Przykład:

2024-11-07 07:00:18;18.3;55;45
description: SOMAR SPÓŁKA AKCYJNA

ul. Karoliny 4,

40-186 Katowice, Polska 

 

KRS: 0000407623

NIP: 6340196270

REGON: 003601740 

 

www.somar.com.pl 
description: System Diagnostyki Maszyn SDM-1 był rozwijany w ramach projektu „Sprzętowo-programowy system diagnostyki maszyn i urządzeń bazujących na bezprzewodowej sieci czujników monitorujących oraz metodach inżynierii wiedzy i inteligencji obliczeniowej” współfinansowanego z Europejskiego Funduszu Rozwoju Regionalnego zgodnie z umową nr POIR.01.01.01-00-0304/19-00 z dnia 30.12.2019.
DOI: 10.5281/zenodo.17462480
title: SOMAR SA - Vibration sensors measurement data from industrial fans
title: SOMAR SA - Dane pomiarowe z czujników wibracji wentylatorów przemysłowych

description: Files S74.csv and S76.csv contain raw measurement data from SOMAR SA WS-VT1 vibration and temperature sensors.

The sensors were installed on the motors of industrial gas furnace fans used to preheat non-ferrous metal billets before plastic forming.

The data structure in each file is as follows:

[date and time];[temperature °C];[maximum vibration acceleration (mg)];[RMS vibration acceleration (mg)]

Example:

2024-11-07 07:00:18;18.3;55;45
description: Pliki S74.csv i S76.csv zawierają surowe dane pomiarowe z czujników wibracji i temperatury WS-VT1 firmy SOMAR SA.

Czujniki zostały zainstalowane na silnikach przemysłowych wentylatorów pieców gazowych, które służą do podgrzewania kęsów metali nieżelaznych przed obróbką plastyczną.

Struktura danych w pliku jest następująca:

[data i czas];[temperatura °C];[maksymalne przyspieszenie drgań (mg)];[wartość skuteczna przyspieszenia drgań (mg)]

Przykład:

2024-11-07 07:00:18;18.3;55;45
description: SOMAR SPÓŁKA AKCYJNA

ul. Karoliny 4,

40-186 Katowice, Polska 

 

KRS: 0000407623

NIP: 6340196270

REGON: 003601740 

 

www.somar.com.pl 
description: System Diagnostyki Maszyn SDM-1 był rozwijany w ramach projektu „Sprzętowo-programowy system diagnostyki maszyn i urządzeń bazujących na bezprzewodowej sieci czujników monitorujących oraz metodach inżynierii wiedzy i inteligencji obliczeniowej” współfinansowanego z Europejskiego Funduszu Rozwoju Regionalnego zgodnie z umową nr POIR.01.01.01-00-0304/19-00 z dnia 30.12.2019.
DOI: 10.15468/dl.rg6gg7
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Panthera leo krugeri",
 "HasCoordinate is true"
 ]
}

 The dataset includes 31 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017598-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hyzvuy
title: Occurrence Download

description: A dataset containing 691590 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Populus tremula",
 "HasCoordinate is true"
 ]
}

 The dataset includes 691590 records from 2535 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017563-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5m9zrd
title: Occurrence Download

description: A dataset containing 130017 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Columba livia",
 "Country is Argentina",
 "HasCoordinate is true"
 ]
}

 The dataset includes 130017 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017692-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kembgh
title: Occurrence Download

description: A dataset containing 1091289 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Halcyon smyrnensis"
 ]
}

 The dataset includes 1091289 records from 165 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017612-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xxar3s
title: Occurrence Download

description: A dataset containing 15 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Bromus thominii, Bromus tunetanus, Bromus tunetanus, Festuca mollis, Festuca mollis hordeaceus, Serrafalcus hordeaceus, Serrafalcus mollis contractus, Serrafalcus mollis hordeaceus, Serrafalcus tinei, Trimen hordeaceus)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Machine Observation)"
 ]
 }
 },
 "Continent is Europe",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2024"
 ]
}

 The dataset includes 15 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017729-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.c478hw
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Balanus improvisus",
 "Country is Latvia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017727-250711103210423/datasets/export for details.
DOI: 10.15468/dl.qkhu2x
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is BOLD:AAA8492"
 ]
}

 The dataset includes 9 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017728-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.e9upxf
title: Occurrence Download

description: A dataset containing 2961 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lavandula dentata",
 "HasCoordinate is true",
 "Geometry POLYGON ((-17.66889 -35.11691, 51.34702 -35.11691, 51.34702 37.62, -17.66889 37.62, -17.66889 -35.11691))"
 ]
}

 The dataset includes 2961 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017730-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21410/7e4/axmigo/3bimna
title: Corruption cases in Zhejiang and Hangzhou-1.tab

description: This dataset gathers the corruption cases of local officials in Zhejiang and Hangzhou from 2015 to 2025.
DOI: 10.21410/7e4/axmigo
title: Corruption cases of local officials in Hangzhou and Zhejiang

description: This dataset gathers 279 corruption cases of local officials in Hangzhou and Zhejiang from 2015 to 2025. Involved officials range from street-level bureaucrats to provincial-level officials. This dataset is intended to help study the characteristics of private firms that have been preyed upon of local officials in Hangzhou city and Zhejiang province.
DOI: 10.15468/dl.7e8wwp
title: Occurrence Download

description: A dataset containing 417 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pectenia plumbea",
 "Geometry POLYGON((-8.00174339329464 54.1998526673513, 0.202120938926891 54.1998526673513, 0.202120938926891 61.2628565887411, -8.00174339329464 61.2628565887411, -8.00174339329464 54.1998526673513))",
 "CoordinateUncertaintyInMeters is 0,101",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 417 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017731-250711103210423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.