cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.15087539
title: Data for "Fundamental limits on determination of photon number statistics from measurements with multiplexed on/off detectors"

description: This dataset contains results of numerical calculations that were used to create graphs in the article J. Fiurášek, Fundamental limits on determination of photon number statistics from measurements with multiplexed on/off detectors, Phys. Rev. Research 7, 033147 (2025). The data are stored as ordinary text files. The variables stored in each column of each data file are specified in the PDF file multiplex_data_specification.pdf. The Mathematica notebooks and Matlab script which were used to generate these data are also provided.
DOI: 10.5281/zenodo.15767486
title: Data for "Fundamental limits on determination of photon number statistics from measurements with multiplexed on/off detectors"

description: This dataset contains results of numerical calculations that were used to create graphs in the article J. Fiurášek, Fundamental limits on determination of photon number statistics from measurements with multiplexed on/off detectors, Phys. Rev. Research 7, 033147 (2025). The data are stored as ordinary text files. The variables stored in each column of each data file are specified in the PDF file multiplex_data_specification.pdf. The Mathematica notebooks and Matlab script which were used to generate these data are also provided.
DOI: 10.15468/dl.euzdzq
title: Occurrence Download

description: A dataset containing 423552 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Pleioblastus distichus, Pseudosasa humilis, Arthropteris orientalis, Synaptula reciprocans, Malephora purpureocrocea, Dendostrea folium, Amaranthus quitensis, Anolis aeneus, Echinochloa pyramidalis, Oryza rufipogon, Antidesma ghaesembilla, Salvinia auriculata, Chelicorophium sowinskyi, Dikerogammarus bispinosus, Jaera sarsi, Katamysis warpachowskyi, Limnomysis benedeni, Theodoxus fluviatilis fluviatilis, Humulopsis scandens, Vitis vulpina, Kappaphycus alvarezii, Dinebra retroflexa, Heliconia psittacorum, Blumea obliqua, Centratherum punctatum, Dicoma tomentosa, Gamochaeta coarctata, Gnaphalium polycaulon, Goniocaulon indicum, Lagascea mollis, Sonchus asper, Sonchus wightianus, Cardamine trichocarpa usambarensis, Sieruela monophylla, Aerva javanica, Alternanthera paronychioides, Alternanthera spinosa, Digera muricata, Cylindropuntia ramosissima, Oxygonum sinuatum, Chamaecrista rotundifolia, Indigofera linnaei, Lysiloma latisiliquum, Senna uniflora, Stylosanthes hamata, Spermacoce pusilla, Calceolaria mexicana, Torenia fournieri, Sesamum radiatum, Lippia alba)",
 "BasisOfRecord is one of (Machine Observation, Human Observation, Observation)",
 "HasCoordinate is true",
 "Year is greater than or equal to 1990"
 ]
}

 The dataset includes 423552 records from 2427 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032250-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tkmaa3
title: Occurrence Download

description: A dataset containing 247 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Mitragyna speciosa Korth."
 ]
}

 The dataset includes 247 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012049-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bk8zd9
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Agapanthinus callophilus (Cockerell, 1923)"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012050-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/xjtx-jr92
title: Public Dataset - ref

description: This is the data set used for The Third International Knowledge Discovery and Data Mining Tools Competition, which was held in conjunction with KDD-99 The Fifth International Conference on Knowledge Discovery and Data Mining. The competition task was to build a network intrusion detector, a predictive model capable of distinguishing between ``bad'' connections, called intrusions or attacks, and ``good'' normal connections. This database contains a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment
DOI: 10.15468/dl.hamn4x
title: Occurrence Download

description: A dataset containing 1607830 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Medicago L."
 ]
}

 The dataset includes 1607830 records from 4587 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0002078-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qcpv6u
title: Occurrence Download

description: A dataset containing 7103 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Dennstaedtia T.Moore"
 ]
}

 The dataset includes 7103 records from 242 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030468-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21227/z148-2d22
title: VignaBagnolo

description: The dataset was captured from a vineyard in Bagnolo San Vito, Italy. The dataset comprises sensor readings and high-resolution images collected from 24 March 2024 to 24 December 2024, using one high-resolution camera and a LoRaWAN network of 2 air sensors and 6 soil sensors. The sensors measured air temperature and humidity, soil dielectric permittivity, and soil temperature every 10 minutes. The camera captured one 4-Mpixel RGB image per day.
DOI: 10.15468/dl.tb5n39
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Elymus alashanicus (Keng & S.L.Chen) S.L.Chen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030470-250426092105405/datasets/export for details.
DOI: 10.15468/dl.b66bet
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Agropyron mongolicum Keng",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation)"
 ]
}

 The dataset includes 29 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030469-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cqhmyd
title: Occurrence Download

description: A dataset containing 1 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 PRINTF('%04d-%02d-%02d', "year", "month", "day") yearmonthday,
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, PRINTF('%04d-%02d-%02d', "year", "month", "day"))) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.datasetkey = '72a17f09-ceaa-4420-aff3-3bb99564774e'
 AND GBIF_WITHIN('POLYGON((7.34268 43.68258,7.34894 43.68258,7.34894 43.68508,7.34268 43.68508,7.34268 43.68258))', occurrence.decimallatitude, occurrence.decimallongitude) = TRUE
 AND occurrence.hascoordinate = TRUE
 AND occurrence.hasgeospatialissues = FALSE
 AND occurrence.occurrencestatus = 'PRESENT'
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL AND occurrence."month" IS NOT NULL AND occurrence."day" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 PRINTF('%04d-%02d-%02d', "year", "month", "day")

 The dataset's 1 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0030454-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.11043791
title: Bosnia and Herzegovina results from the monitoring of pesticide residues in food

description: This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. 

A sample is considered free of quantifiable residues if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy.

If a sample contains quantifiable residues but within the legally permitted limit (maximum residue level, MRL), it is described as a sample  with quantified residue levels within the legal limits (below or at the MRL)

A sample is considered non-compliant with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation.

 

REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:

MOPER_2022 - Food Safety Agency of Bosnia and Herzegovina

MOPER_2021 - Food Safety Agency of Bosnia and Herzegovina

MOPER_2020 - Food Safety Agency of Bosnia and Herzegovina

MOPER_2019 - Food Safety Agency of Bosnia and Herzegovina

MOPER_2018 - Food Safety Agency of Bosnia and Herzegovina

MOPER_2017 - Food Safety Agency of Bosnia and Herzegovina

We are seeking feedback on our open data please complete the survey at the link below:https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14
description: BA; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.4458657
title: Bosnia and Herzegovina results from the monitoring of pesticide residues in food

description: This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL) A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation. <strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong> MOPER_2017 - Food Safety Agency of Bosnia and Herzegovina <strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong>
description: BA; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.4461868
title: Bosnia and Herzegovina results from the monitoring of pesticide residues in food

description: This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL) A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation. <strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong> MOPER_2018 - Food Safety Agency of Bosnia and Herzegovina MOPER_2017 - Food Safety Agency of Bosnia and Herzegovina <strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong>
description: BA; CSV; data.collection@efsa.europa.eu
DOI: 10.5281/zenodo.5021402
title: Bosnia and Herzegovina results from the monitoring of pesticide residues in food

description: This dataset contains the analytical results of pesticide residues measured in the food products analysed by the national competent authorities. Pesticide residues resulting from the use of plant protection products on crops that are used for food or feed production may pose a risk factor for public health. For this reason, a comprehensive legislative framework has been established in the European Union (EU), which defines rules for the approval of active substances used in plant protection products, the use of plant protection products and for pesticide residues in food. In order to ensure a high level of consumer protection, legal limits, so called “maximum residue levels” or briefly “MRLs”, are established in Regulation (EC) No 396/2005. EU-harmonised MRLs are set for all pesticides covering all types of food products. A default MRL of 0.01 mg/kg is applicable for pesticides not explicitly mentioned in the MRL legislation. Regulation (EC) No 396/2005 imposes on Member States the obligation to carry out controls to ensure that food placed on the market is compliant with the legal limits. The chemical monitoring data collected and published by EFSA include the analytical results provided by EU Member States, Iceland, Norway and three pre-accession countries: Bosnia-Herzegovina, Montenegro and North Macedonia. A sample is considered <strong>free of quantifiable residues</strong> if the analytes were not present in concentrations at or above the limit of quantification (LOQ). The LOQ is the smallest concentration of an analyte that can be quantified with the analytical method used to analyse the sample. It is commonly defined as the minimum concentration of the analyte in the test sample that can be determined with acceptable precision and accuracy. If a sample <strong>contains quantifiable residues</strong> but within the legally permitted limit (maximum residue level, MRL), it is described as a sample with quantified residue levels within the legal limits (below or at the MRL) A sample is considered <strong>non-compliant</strong> with the legal limit (MRL), if the measured residue concentrations clearly exceed the legal limits, taking into account the measurement uncertainty. It is current practice that the uncertainty of the analytical measurement is taken into account before legal or administrative sanctions are imposed on food business operators for infringement of the MRL legislation. <strong>REPORTING AUTHORITIES CONTRIBUTING TO EACH DATA COLLECTION:</strong> MOPER_2019 - Food Safety Agency of Bosnia and Herzegovina MOPER_2018 - Food Safety Agency of Bosnia and Herzegovina MOPER_2017 - Food Safety Agency of Bosnia and Herzegovina <strong>We are seeking feedback on our open data please complete the survey at the link below:<br> https://ec.europa.eu/eusurvey/runner/9344dfa0-f384-cb72-65f6-6c187a6d0f14</strong>
description: BA; CSV; data.collection@efsa.europa.eu