cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.t3ckb9
title: Occurrence Download

description: A dataset containing 135 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "TaxonKey is Agrilus planipennis Fairmaire, 1888"
 ]
}

 The dataset includes 135 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019671-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15357536
title: umbilicus_gaditanus_spades_01.fasta.gz
DOI: 10.5281/zenodo.15357537
title: umbilicus_gaditanus_spades_01.fasta.gz
DOI: 10.15468/dl.fmq7j3
title: Occurrence Download

description: A dataset containing 4726 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "TaxonKey is Calathus rotundicollis Dejean, 1828"
 ]
}

 The dataset includes 4726 records from 160 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019672-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15357622
title: umbilicus_horizontalis_spades_01.fasta.gz
DOI: 10.5281/zenodo.15357623
title: umbilicus_horizontalis_spades_01.fasta.gz
DOI: 10.3886/e112767v1
title: Replication data for: Health, Human Capital, and Life Cycle Labor Supply

description: We use new PSID data on consumption and health, along with information on annual sick time, to estimate a structural labor supply model that incorporates a health capital stock with the traditional human capital learning-by-doing model. The estimates show strong evidence of learning by doing as well as strong persistence in health. However, the estimates reveal that time and money seem to have little effect on health consistent with "flat of the curve" medicine. We find strong evidence that consumption and leisure are direct substitutes in preferences, and consumption and leisure are each utility complements with good health.
DOI: 10.3886/e112767v1-19037
title: Replication data for: Health, Human Capital, and Life Cycle Labor Supply

description: We use new PSID data on consumption and health, along with information on annual sick time, to estimate a structural labor supply model that incorporates a health capital stock with the traditional human capital learning-by-doing model. The estimates show strong evidence of learning by doing as well as strong persistence in health. However, the estimates reveal that time and money seem to have little effect on health consistent with "flat of the curve" medicine. We find strong evidence that consumption and leisure are direct substitutes in preferences, and consumption and leisure are each utility complements with good health.
DOI: 10.3886/e112767
title: Replication data for: Health, Human Capital, and Life Cycle Labor Supply

description: We use new PSID data on consumption and health, along with information on annual sick time, to estimate a structural labor supply model that incorporates a health capital stock with the traditional human capital learning-by-doing model. The estimates show strong evidence of learning by doing as well as strong persistence in health. However, the estimates reveal that time and money seem to have little effect on health consistent with "flat of the curve" medicine. We find strong evidence that consumption and leisure are direct substitutes in preferences, and consumption and leisure are each utility complements with good health.
DOI: 10.3886/e112767v2
title: Replication data for: Health, Human Capital, and Life Cycle Labor Supply

description: We use new PSID data on consumption and health, along with information on annual sick time, to estimate a structural labor supply model that incorporates a health capital stock with the traditional human capital learning-by-doing model. The estimates show strong evidence of learning by doing as well as strong persistence in health. However, the estimates reveal that time and money seem to have little effect on health consistent with "flat of the curve" medicine. We find strong evidence that consumption and leisure are direct substitutes in preferences, and consumption and leisure are each utility complements with good health.
DOI: 10.3886/e228323v1
title: Supplementary data for: Health, Human Capital, and Life Cycle Labor Supply

description: This project contains supplementary data for Health, Human Capital, and Life Cycle Labor Supply. Publication details are below.Hokayem, Charles, and James P. Ziliak.
 2014.

 
 
 "Health, Human Capital, and Life Cycle Labor Supply."
 
 American Economic Review
 
 104 (5):
 127–31.
 
 DOI: 10.1257/aer.104.5.127<br>
DOI: 10.13120/ff73051b-b193-4ec4-9c02-cb368487eb3e
title: Micro litter - Spatial distribution of the sampling effort (period 2017-2022) - Other specific protocols
title: mlf_spatialdistribution_samplingeffort_other_30km

description: This visualization product displays the spatial distribution of the sampling effort (based on the start position of the sampling tow) over the six-years' period 2017-2022 from other specific protocols.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10).

A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter.

European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization.

A filter is applied to represent only micro-litter samplings carried out according to a very specific protocol such as the Volvo Ocean Race (VOR) or Oceaneye.

The spatial distribution was then determined by calculating the number of times each cell was sampled during the period 2017-2022, only taking into account the start position of the tows. The corresponding total distance (kms) sampled in each cell is also provided in the attribute table.

Information on data processing and calculation are detailed in the attached methodology document.

Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.

This work is based on the work presented in the following scientific article:
O. Gerigny, M. Brun, M.C. Fabri, C. Tomasino, M. Le Moigne, A. Jadaud, F. Galgani, Seafloor litter from the continental shelf and canyons in French Mediterranean Water: Distribution, typologies and trends, Marine Pollution Bulletin, Volume 146, 2019, Pages 653-666, ISSN 0025-326X, https://doi.org/10.1016/j.marpolbul.2019.07.030.
DOI: 10.5281/zenodo.15357548
title: Food Loss Percentages by Commodity and Activity Groups Across Countries by Development Level (2000–2022)

description: This dataset integrates food loss percentages across multiple commodity and activity groups for 105 countries classified by development level (developed vs. developing) and includes Turkey as a separate reference case. The data spans the period from 2000 to 2022 and combines information from the World Bank (GDP, GDP growth, health expenditure) and FAO (food loss). Commodity groups are categorized into 11 main groups (e.g., cereals, fruits, vegetables, meats), and supply chain activity groups are classified into 8 categories (e.g., harvesting, storage, consumption). Countries are clustered using a time-weighted K-means approach considering economic and health indicators. The dataset supports analyses of food loss patterns by socioeconomic status and supply chain stage, facilitating cross-national comparisons and temporal trend assessments.
DOI: 10.5281/zenodo.15357549
title: Food Loss Percentages by Commodity and Activity Groups Across Countries by Development Level (2000–2022)

description: This dataset integrates food loss percentages across multiple commodity and activity groups for 105 countries classified by development level (developed vs. developing) and includes Turkey as a separate reference case. The data spans the period from 2000 to 2022 and combines information from the World Bank (GDP, GDP growth, health expenditure) and FAO (food loss). Commodity groups are categorized into 11 main groups (e.g., cereals, fruits, vegetables, meats), and supply chain activity groups are classified into 8 categories (e.g., harvesting, storage, consumption). Countries are clustered using a time-weighted K-means approach considering economic and health indicators. The dataset supports analyses of food loss patterns by socioeconomic status and supply chain stage, facilitating cross-national comparisons and temporal trend assessments.
DOI: 10.7910/dvn/fcidrc
title: Replication Data for: What Unites the Right in the European Parliament

description: Replication files for What Unites the Right in the European Parliament
DOI: 10.15468/dl.fv2f65
title: Occurrence Download

description: A dataset listing the 1894 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepidoptera"
 ]
}

 The dataset's 1894 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0019669-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.13120/efe1b343-01b6-4083-a63c-6843ddb5cf90
title: Micro litter - Density per net normalized per m³ - Other specific protocols 2017/2021 v2025
title: mlf_density_nb_m3_other

description: This visualization product displays the density of floating micro-litter per net normalized per m³ per year from specific protocols different from research and monitoring protocols.
EMODnet Chemistry included the collection of marine litter in its 3rd phase. Before 2021, there was no coordinated effort at the regional or European scale for micro-litter. Given this situation, EMODnet Chemistry proposed to adopt the data gathering and data management approach as generally applied for marine data, i.e., populating metadata and data in the CDI Data Discovery and Access service using dedicated SeaDataNet data transport formats. EMODnet Chemistry is currently the official EU collector of micro-litter data from Marine Strategy Framework Directive (MSFD) National Monitoring activities (descriptor 10).
A series of specific standard vocabularies or standard terms related to micro-litter have been added to SeaDataNet NVS (NERC Vocabulary Server) Common Vocabularies to describe the micro-litter.
European micro-litter data are collected by the National Oceanographic Data Centres (NODCs). Micro-litter map products are generated from NODCs data after a test of the aggregated collection including data and data format checks and data harmonization.

A filter is applied to represent only micro-litter sampled according to a very specific protocol such as the Volvo Ocean Race (VOR) or Oceaneye.

Densities were calculated for each net using the following calculation:
Density (number of particles per m³) = Micro-litter count / Sampling effort (m³) 
When the number of micro-litters was not filled, it was not possible to calculate the density.

Percentiles 50, 75, 95 & 99 have been calculated taking into account data for all years.

Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the National Oceanographic Data Centre (NODC) for this area.
DOI: 10.13120/ef82b847-44f9-47a7-b9e7-d8bde3f2dc2d
title: Seafloor litter - Density per trawl normalized per km² 2011/2024 v2025
title: sl_totalabundance

description: This visualization product displays the density of seafloor litter per trawl.

EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). 
The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during bottom trawl surveys.

In cases where the wingspread and/or the number of items were/was unknown, it was not possible to use the data because these fields are needed to calculate the density. Data collected before 2011 are concerned by this filter.

When the distance reported in the data was null, it was calculated from:
- the ground speed and the haul duration using the following formula: Distance (km) = Haul duration (h) * Ground speed (km/h);
- the trawl coordinates if the ground speed and the haul duration were not filled in.

The swept area was calculated from the wingspread (which depends on the fishing gear type) and the distance trawled:
Swept area (km²) = Distance (km) * Wingspread (km)

Densities were calculated on each trawl and year using the following computation:
Density (number of items per km²) = ∑Number of items / Swept area (km²)

Percentiles 50, 75, 95 & 99 were calculated taking into account data for all years.

More information on data processing and calculation are detailed in the attached document.

Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.
DOI: 10.13120/ef8b672a-a118-4fa5-8095-2bdf24ccbfb4
title: Beach litter - Median number of single use plastics (SUP) related items normalized per 100m & to 1 survey - EU-MSFD monitoring 2001/2023 v2025
title: bl_singleuse_monitoring

description: This visualization product displays the single use plastics (SUP) related plastic items abundance of marine macro-litter (&gt; 2.5cm) per beach per year from Marine Strategy Framework Directive (MSFD) monitoring surveys.


EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB).

The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale.


Preliminary processings were necessary to harmonize all the data:

- Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring;

- Selection of MSFD surveys only (exclusion of other monitoring, cleaning and research operations);

- Exclusion of beaches without coordinates;

- Selection of SUP related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata). The selection was adapted to the Joint list of litter categories SUP identification and therefore contains some differences with the selection made for previously published versions of this product;

- Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not exactly 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula:

Number of SUP items of the survey (normalized by 100 m) = Number of SUP related items of the survey x (100 / survey length)

Then, this normalized number of¨SUP related items is summed to obtain the total normalized number of SUP related items for each survey. Finally, the median abundance of SUP related items for each beach and year is calculated from these normalized abundances of SUP related items per survey.

Sometimes the survey length was null or equal to 0. Assuming that the MSFD protocol has been applied, the length has been set at 100m in these cases.


Percentiles 50, 75, 95 & 99 have been calculated taking into account SUP related plastic items from MSFD data for all years.


More information is available in the attached documents.


Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.
DOI: 10.13120/ee1fe4b7-6a20-4e70-b626-6dbf91974d5d
title: Beach litter - Median number of plastic bags related items normalized per 100m & to 1 survey - Other sources 2001/2024 v2025
title: bl_plasticbags_other

description: This visualization product displays the cigarette related items abundance of marine macro-litter (&gt; 2.5cm) per beach per year from non-MSFD monitoring surveys, research & cleaning operations without UNEP-MARLIN data.


EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of beach litter have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB).

The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols and reference lists used on a European scale.


Preliminary processings were necessary to harmonize all the data:

- Exclusion of OSPAR 1000 protocol: in order to follow the approach of OSPAR that it is not including these data anymore in the monitoring;

- Selection of surveys from non-MSFD monitoring, cleaning and research operations;

- Exclusion of beaches without coordinates;

- Selection of plastic bags related items only. The list of selected items is attached to this metadata. This list was created using EU Marine Beach Litter Baselines, the European Threshold Value for Macro Litter on Coastlines and the Joint list of litter categories for marine macro-litter monitoring from JRC (these three documents are attached to this metadata);

- Exclusion of surveys without associated length;

- Normalization of survey lengths to 100m & 1 survey / year: in some case, the survey length was not 100m, so in order to be able to compare the abundance of litter from different beaches a normalization is applied using this formula:

Number of plastic bags related items of the survey (normalized by 100 m) = Number of plastic bags related items of the survey x (100 / survey length)

Then, this normalized number of plastic bags related items is summed to obtain the total normalized number of plastic bags related items for each survey. Finally, the median abundance of plastic bags related items for each beach and year is calculated from these normalized abundances of plastic bags related items per survey.


Percentiles 50, 75, 95 & 99 have been calculated taking into account plastic bags related items from other sources data for all years.


More information is available in the attached documents.


Warning: the absence of data on the map does not necessarily mean that they do not exist, but that no information has been entered in the Marine Litter Database for this area.
DOI: 10.25934/pr00011346
title: AMR Data Challenge 2025: AMR Data Challenge 2025: Identifying patterns and drivers of AMR among pathogens in India

description: Our data analytics project will leverage datasets from the GASAR and PLEA studies, encompassing over 3,876 non-duplicate gram-negative bacterial isolates collected across India between 2011 and 2023. The data includes detailed phenotypic susceptibility profiles and genotypic information on key resistance genes such as blaTEM, blaSHV, blaCTX-M, blaNDM, blaVIM, and blaOXA.
The project will focus on identifying patterns and drivers of antimicrobial resistance among pathogens including E. coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, Escherichia coli, Pseudomonas aeruginosa, and Acinetobacter baumannii. The sources of the samples are from blood, skin, urine, respiratory channels. Using statistical modeling and machine learning approaches, the project will:
•	Analyze temporal and spatial trends in resistance profiles across the three study phases.
•	Identify correlations between resistance phenotypes and the presence of specific resistance genes.
•	Use clustering and dimensionality reduction techniques to group isolates by resistance mechanisms and antibiotic susceptibility profiles.
•	Evaluate the effectiveness of key antibiotics over time, particularly carbapenems and polymyxins.
•	Explore the potential emergence and spread of multidrug-resistant strains by source and region.
The outputs will include heatmaps, resistance trend plots, gene-phenotype correlation matrices, and interactive dashboards to visualize and interpret key findings. These insights can support the optimization of empirical therapy, guide infection control strategies, and contribute to national and regional antibiotic stewardship efforts.
The project will be structured in phases: data cleaning and preprocessing (Week 1–2), exploratory and statistical analysis (Week 3–5), advanced modeling (Week 6–7), and final visualization/reporting (Week 8).
DOI: 10.5281/zenodo.15357662
title: Mereview Buku Argonauts of The Western Pacific

description: Argonauts of the Western Pacific adalah karya etnografi klasik yang mendokumentasikan sistem pertukaran Kula di Kepulauan Trobriand, Papua Nugini. Bronislaw Malinowski memperkenalkan pendekatan observasi partisipatif dengan tinggal langsung di tengah masyarakat yang ditelitinya. Buku ini mengungkap bagaimana praktik pertukaran yang tampaknya ekonomis ternyata sarat dengan nilai sosial, magis, dan simbolik. Melalui deskripsi yang detail dan refleksi teoritis, Malinowski merumuskan kritik terhadap teori ekonomi klasik dan memperkenalkan cara pandang baru dalam memahami relasi sosial di masyarakat tradisional.
description: Argonauts of the Western Pacific adalah karya etnografi klasik yang mendokumentasikan sistem pertukaran Kula di Kepulauan Trobriand, Papua Nugini. Bronislaw Malinowski memperkenalkan pendekatan observasi partisipatif dengan tinggal langsung di tengah masyarakat yang ditelitinya. Buku ini mengungkap bagaimana praktik pertukaran yang tampaknya ekonomis ternyata sarat dengan nilai sosial, magis, dan simbolik. Melalui deskripsi yang detail dan refleksi teoritis, Malinowski merumuskan kritik terhadap teori ekonomi klasik dan memperkenalkan cara pandang baru dalam memahami relasi sosial di masyarakat tradisional.