cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.6n23zx
title: Occurrence Download

description: A dataset containing 0 records in GBIF matching the query:

SELECT 
 -- Dimensions: 
 PRINTF('%04d-%02d', "year", "month") AS yearMonth,
 GBIF_EEARGCode(
 1000,
 decimalLatitude,
 decimalLongitude,
 COALESCE(coordinateUncertaintyInMeters, 1000)
 ) AS eeaCellCode,
 taxonKey,
 acceptedTaxonKey,
 familyKey,
 family,
 speciesKey,
 species,
 scientificname,
 -- Measurements 
 COUNT(*) AS occurrences, 
 MIN(COALESCE(coordinateUncertaintyInMeters, 1000)) AS minCoordinateUncertaintyInMeters,
 MIN(GBIF_TemporalUncertainty(eventDate, null)) AS minTemporalUncertainty
FROM
 occurrence
WHERE occurrenceStatus = 'PRESENT'
 AND basisofrecord NOT IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND countryCode = 'NL'
 AND "year" &lt; 2025
 AND "year" &gt;= 2020
 AND hasCoordinate = TRUE
 -- AND species IN (143705799)
 AND acceptedTaxonKey IN (143705799)
 AND (coordinateUncertaintyInMeters &lt;= 1000 OR coordinateUncertaintyInMeters IS NULL)
 AND NOT ARRAY_CONTAINS(issue, 'ZERO_COORDINATE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_OUT_OF_RANGE')
 AND NOT ARRAY_CONTAINS(issue, 'COORDINATE_INVALID')
 AND NOT ARRAY_CONTAINS(issue, 'COUNTRY_COORDINATE_MISMATCH')
 AND "month" IS NOT NULL
 AND ( 
 LOWER(identificationVerificationStatus) NOT IN (
 'unverified',
 'unvalidated',
 'not validated',
 'under validation',
 'not able to validate',
 'control could not be conclusive due to insufficient knowledge',
 'uncertain',
 'unconfirmed',
 'unconfirmed - not reviewed',
 'validation requested'
 )
 OR identificationVerificationStatus IS NULL
 )
GROUP BY
 yearMonth,
 eeaCellCode,
 taxonKey,
 acceptedTaxonKey,
 familyKey,
 family,
 speciesKey,
 species,
 scientificname
ORDER BY 
 yearMonth DESC,
 eeaCellCode ASC,
 speciesKey ASC;


 The dataset's 0 records were derived from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031443-251025141854904/datasets/export for details.
DOI: 10.15468/dl.vez9m4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acipenser sinensis",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031474-251025141854904/datasets/export for details.
DOI: 10.5281/zenodo.17602817
title: The DESI DR1 Peculiar Velocity Survey: growth rate measurements from the maximum likelihood fields method

description: Supplementary material to DESI's publication "The DESI DR1 Peculiar Velocity Survey: growth rate measurements from the maximum likelihood fields method" to comply with the data management plan. 

This zip file contains all data points and MCMC chains needed to replicate all plots in the paper. The data points and codes needed to replicate plot x are inside the subfolder figurex. The python and c code used to generate the analytical covaraince matrix and run the MCMC are available at https://github.com/YanxiangL/Peculiar_velocity_fitting.
DOI: 10.6084/m9.figshare.30611405
title: Landmarks from ontogenetic sample of human scapulae - Modern Americans, Poundbury Camp, Christ Church Spitalfields, St. Bride's Fleet Street crypt

description: Raw landmark files from the paper "Environment and variation in human scapular ontogenetic trajectory", currently in review at the <i>American Journal of Human Biology</i>. Landmarks are in .fscv file format and consist of 21 landmark coordinates in XYZ space.
DOI: 10.6084/m9.figshare.30611405.v1
title: Landmarks from ontogenetic sample of human scapulae - Modern Americans, Poundbury Camp, Christ Church Spitalfields, St. Bride's Fleet Street crypt

description: Raw landmark files from the paper "Environment and variation in human scapular ontogenetic trajectory", currently in review at the <i>American Journal of Human Biology</i>. Landmarks are in .fscv file format and consist of 21 landmark coordinates in XYZ space.
DOI: 10.6084/m9.figshare.30611408
title: Data of serological survey to determine seroprevalence of hepatitis B in Mexico

description: Data from a quantitative serological study to identify opportunities for improvement in hepatitis B prevention and to identify areas susceptible to outbreaks in Mexico. Data from 4,510 serum samples from an anonymous and unrelated sample, which included demographic information from IMSS beneficiaries across the country and from all age groups.
DOI: 10.6084/m9.figshare.30611408.v1
title: Data of serological survey to determine seroprevalence of hepatitis B in Mexico

description: Data from a quantitative serological study to identify opportunities for improvement in hepatitis B prevention and to identify areas susceptible to outbreaks in Mexico. Data from 4,510 serum samples from an anonymous and unrelated sample, which included demographic information from IMSS beneficiaries across the country and from all age groups.
DOI: 10.15468/dl.f54ejp
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Acipenser stellatus",
 "HasCoordinate is true",
 "Geometry POLYGON((-78 12, -73 12, -73 0, -78 0, -78 12))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031477-251025141854904/datasets/export for details.
DOI: 10.15468/dl.55uw4t
title: Occurrence Download

description: A dataset containing 901 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Clonophis kirtlandii"
 ]
}

 The dataset includes 901 records from 35 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031479-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7934/p5886
title: Diving in the Maastrichtian of Marambio (Seymour) Island: A new member of the Neoaves in the Cretaceous Antarctic avifauna (project)
DOI: 10.15468/dl.pykxpj
title: Occurrence Download

description: A dataset containing 273 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (, , , , , , , , , , Phoebe hunanensis, , )",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Living Specimen, Machine Observation, Literature Occurrence)",
 "Geometry POLYGON((-180 -90,180 -90,180 90,-180 90,-180 -90))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 273 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0031481-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15579013
title: PATHOGENESIS OF COVID-19: NEW INSIGHTS
DOI: 10.5281/zenodo.15579014
title: PATHOGENESIS OF COVID-19: NEW INSIGHTS
DOI: 10.5281/zenodo.15579021
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250602s Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250602s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.5281/zenodo.15579022
title: Submitted Galaxy Scores to the Gravitational Wave Treasure Map for event MS250602s Preliminary

description: Attached in a .json file is the ranked galaxy information within the contour region of the EM counterpart search associated with the gravitational wave event MS250602s Preliminary. A reference to these calculations can be found here: https://ui.adsabs.harvard.edu/abs/2017ApJ...848L..33A/abstract
DOI: 10.15468/dl.hhqxvx
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Diceros bicornis (Linnaeus, 1758))",
 "Country is one of (Åland Islands, Albania, Andorra, Austria, Belarus, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Gibraltar, Greece, Guernsey, Holy See, Hungary, Iceland, Ireland, Isle of Man, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Republic of, Monaco, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Russian Federation, San Marino, Serbia, Slovakia, Slovenia, Spain, Svalbard and Jan Mayen, Sweden, Switzerland, Ukraine, United Kingdom of Great Britain and Northern Ireland)"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015949-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.utwxmr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Diceros bicornis (Linnaeus, 1758))",
 "Country is one of (American Samoa, Australia, Christmas Island, Cocos (Keeling) Islands, Cook Islands, Fiji, French Polynesia, Guam, Heard Island and McDonald Islands, Kiribati, Marshall Islands, Micronesia (Federated States of), Nauru, New Caledonia, New Zealand, Niue, Norfolk Island, Northern Mariana Islands, Palau, Papua New Guinea, Pitcairn, Samoa, Solomon Islands, Tokelau, Tonga, Tuvalu, United States Minor Outlying Islands, Vanuatu, Wallis and Futuna)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015951-250525065834625/datasets/export for details.
DOI: 10.15468/dl.rcsznv
title: Occurrence Download

description: A dataset containing 1747 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "BasisOfRecord is one of (Human Observation, Machine Observation, Occurrence evidence, Observation)",
 "TaxonKey is one of (Diceros bicornis (Linnaeus, 1758))",
 "Country is one of (Algeria, Angola, Benin, Botswana, British Indian Ocean Territory, Burkina Faso, Burundi, Cabo Verde, Cameroon, Central African Republic, Chad, Comoros, Congo, Congo, Democratic Republic of the, Côte d’Ivoire, Djibouti, Egypt, Equatorial Guinea, Eritrea, Eswatini, Ethiopia, French Southern Territories, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Lesotho, Liberia, Libya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mayotte, Morocco, Mozambique, Namibia, Niger, Nigeria, Réunion, Rwanda, Saint Helena, Ascension and Tristan da Cunha, Sao Tome and Principe, Senegal, Seychelles, Sierra Leone, Somalia, South Africa, South Sudan, Sudan, Tanzania, United Republic of, Togo, Tunisia, Uganda, Western Sahara, Zambia, Zimbabwe)"
 ]
}

 The dataset includes 1747 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015950-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21410/7e4/0vdsuy/kutuiz
title: fr_cdsp_ddi_fairwddi_trainingdata_v1.zip

description: :unav
DOI: 10.6084/m9.figshare.28869608
title: BED files for primer trimming - amplicon based sequencing of TOSV segments L, M and S (C.ARBO.SEQ project)

description: BED files required for primer trimming during data analysis of amplicon based Illumina sequencing of Toscana virus segments L, M and S
DOI: 10.15468/dl.4fgngw
title: Occurrence Download

description: A dataset containing 576 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Spain",
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Brassica napus L., Brassica rapa L.)",
 "Year 2015-2025"
 ]
}

 The dataset includes 576 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001479-250424135625364/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.10984823
title: European database of processing factors for pesticides residues in food

description: EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2).


The database is complemented by the following publications:



	
Background Document on the EU Database of Processing Factors for Pesticide Residues
	
Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides
	
Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
description: eu; en; xlsx; idata@efsa.europa.eu
DOI: 10.5281/zenodo.12685883
title: European database of processing factors for pesticides residues in food

description: EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2).

This update fixes a problem that caused some median processing factors to display a "<" qualifier when it was not necessary. The calculated values for the processing factors were not affected by this error and remain unchanged from the previous release

The database is complemented by the following publications:



Background Document on the EU Database of Processing Factors for Pesticide Residues

Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides

Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
description: eu; en; xlsx; idata@efsa.europa.eu
DOI: 10.5281/zenodo.1488652
title: European database of processing factors for pesticides residues in food

description: EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2).

This update fixes a problem that caused some median processing factors to display a "<" qualifier when it was not necessary. The calculated values for the processing factors were not affected by this error and remain unchanged from the previous release

The database is complemented by the following publications:



Background Document on the EU Database of Processing Factors for Pesticide Residues

Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides

Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
description: eu; en; xlsx; idata@efsa.europa.eu
DOI: 10.5281/zenodo.1488653
title: European database of processing factors for pesticides in food

description: EFSA is conducting pan-European dietary exposure and risk assessments related to actual levels of pesticide residues in food commodities. These assessments use the pesticide occurrence data generated under the official monitoring programs of Member States, the consumption data from EFSA’s comprehensive food consumption database and pesticide-specific information such as processing factors. Currently no harmonised list of processing factors is available within Europe and worldwide. The overall objective of this project is to develop a database of validated processing factors, which is compatible with the EFSA food classification and description system FoodEx 2. <br> In the first part of the project, a compendium of representative processing techniques was elaborated which serves as a standard description of all relevant processes and as a basis for validation of processing studies. In the second part of the project all relevant processes and raw and processed<br> commodities were coded according to FoodEx2. In the third and last part of the project all processing studies used by EFSA in their Conclusions and Reasoned Opinions issued until 30/06/2016 were reevaluated according to uniform quality criteria and reported in a database. The database is provided<br> as a flat Excel spreadsheet. Information on processing conditions, on analytical methods, storage stability and GLP was collected from the studies and was used to judge the acceptability of each processing study. The representativeness of the processing conditions applied in the studies was<br> judged by comparing them to the compendium of representative processing techniques. Processing factors (PF) were derived from the studies according to comprehensible calculation rules and based on the residue definitions for monitoring. The individual PF were judged for their acceptability. For each<br> commodity/process/active substance combination a median PF was calculated from the individual ones, based on the trials from one or more studies. The database is a ready-to-use inventory of PF for pesticides in a variety of processed food items. <br> In a pilot project a second database was set up with processing factors based on the residue definition for dietary risk assessment. Extension of this database is recommended.
description: eu; en; xlsx; pesticides.mrl@efsa.europa.eu
DOI: 10.5281/zenodo.15363279
title: European database of processing factors for pesticides residues in food

description: EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2).

This update fixes a problem that caused some median processing factors to display a "<" qualifier when it was not necessary. The calculated values for the processing factors were not affected by this error and remain unchanged from the previous release

The database is complemented by the following publications:



Background Document on the EU Database of Processing Factors for Pesticide Residues

Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides

Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
description: eu; en; xlsx; idata@efsa.europa.eu
DOI: 10.5281/zenodo.6564209
title: Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System

description: This excel file complements the EU database of processing factors for pesticide residues by providing the mapping between processed food/feed listed in the database and the FoodEx2 coding system.
description: eu; en; xlsx; idata@efsa.europa.eu
DOI: 10.5281/zenodo.6827098
title: European database of processing factors for pesticides residues in food

description: EFSA is regularly evaluating pesticide occurrence data in food generated under the official monitoring programs of Member States with respect to consumer exposure and risk assessment. Most of these data refer to raw commodities (RAC) because maximum residue levels established under European legislation reflect pesticide residues only in the RAC. However, food processing operations can have decisive effects on pesticide residue levels and therefore consumer exposure. This database has been developed to compile validated processing factors for pesticide residues in food in line with the EFSA food classification and description system (FoodEx2). The database is complemented by the following publications: Background Document on the EU Database of Processing Factors for Pesticide Residues Compendium of Representative Processing Techniques Investigated in Regulatory Studies for Pesticides Linking Processed Foods and Processing Techniques to the FoodEx2 Coding System
description: eu; en; xlsx; idata@efsa.europa.eu
DOI: 10.5281/zenodo.15470089
title: Dataset & code for : "A machine learning approach for estimating forage maize yield and quality in NW Spain"

description: Data and Code for article: "A machine learning approach for estimating forage maize yield and quality in NW Spain".



Code: Jupyter Notebooks for forage maize yields (Dry Matter, Crude Protein and UFL)




📙 Dry Matter forage maize yield predictions using Machine Learning ensemble methods for NW of Spain

📙 UFL(Net energy of forage maize) yield predictions using Machine Learning ensemble methods for NW of Spain

📙 Crude Protein forage maize yield predictions using Machine Learning ensemble methods for NW of Spain

📙 Dry Matter forage maize yield predictions using Machine Learning ensemble methods and Bootstrapping for NW of Spain


Dataset:  (MS Excel File)

The data file contains 1449 data for forage maize production covering 23 years from 2000 to 2022 and corresponds to 7 locations in NW of Spain (Grado, Barcia and Villaviciosa) in Asturias and (Ordes, Ribadeo, Sarria and Deza) in Galicia region. The production variables are:



kg DM/ha, kilograms of Dry Matter per hectare

UFL / ha, Net energy of forage maize per hectare

kg CP / ha, kilograms of Crude Protein per hectare


The input variables are 14:




Location
Year
Cultivar
Sowing date(Julian day)


Elevation (m)
WHC(mm)
C(%)
pH


Tmax(ºC)
Tmin(ºC)
Radiation(MJ/m2 day) 
Precipitation(mm)


Anthesis date(Julian day)
Harvest date(Julian day)
DOI: 10.7910/dvn/iadv2o
title: Replication Data for: Signatures of Chiral Superconductivity in Rhombohedral Graphene

description: Replication Data for: Signatures of Chiral Superconductivity in Rhombohedral Graphene
DOI: 10.6084/m9.figshare.29946479
title: Extended Data Fig. 2f - 2'3' cA2 (ORB67693)

description: Extended Data Fig. 2f - 2'3' cA2 (ORB67693)
DOI: 10.6084/m9.figshare.29946479.v1
title: Extended Data Fig. 2f - 2'3' cA2 (ORB67693)

description: Extended Data Fig. 2f - 2'3' cA2 (ORB67693)