cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.11583/dtu.21762848
title: Raw AI4Arctic Sea Ice Challenge Test Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the testing data for the '<em>raw'</em>-version<em>.</em> No reference data is included. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge <br> Version 2 has updated the files and now contains files with icecharts (previously absent) as the AutoICE Challenge has been finalised. <br> A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065
DOI: 10.11583/dtu.21762848.v2
title: Raw AI4Arctic Sea Ice Challenge Test Dataset

description: The AI4Arctic Sea Ice Challenge Datasets are produced for the AI4EO sea ice competition initiated by the European Space Agency (ESA) ɸ-lab. The purpose of the competition is to develop deep learning models to automatically produce sea ice charts including sea ice concentration, stage-of-development and floe size (form) information. The training datasets contain Sentinel-1 active microwave Synthetic Aperture Radar (SAR) data and corresponding passive MicroWave Radiometer (MWR) data from the AMSR2 satellite sensor. While SAR data has ambiguities between open water and sea ice, it has a high spatial resolution, whereas MWR data has good contrast between open water and ice. However, the coarse resolution of the AMSR2 MWR observations introduces a new set of obstacles, e.g. land spill-over, which can lead to erroneous sea ice predictions along the coastline adjacent to open water. Label data in the challenge datasets are ice charts, that have been produced by the Greenland ice service at the Danish Meteorological Institute (DMI) and the Canadian Ice Service (CIS) for the safety of navigation. The challenge datasets also contain other auxiliary data such as the distance to land and numerical weather prediction model data. The scenes are from the time period from January 8 2018 to December 21 2021. Two versions of the dataset exist, the '<em>raw'</em> and '<em>ready-to-train'-</em>versions with corresponding test datasets<em>. </em>The datasets each consist of the same 513 training and 20 test (without label data) scenes. The ‘<em>ready-to-train’</em>-version has been further prepared for model training, such as downsampled data from 40 to 80 m pixel spacing, standard scaled, converted ice charts (sea ice concentration, stage of development and floe size), removal of nan values, mask alignment etc. This is the testing data for the '<em>raw'</em>-version<em>.</em> No reference data is included. Further details are described in the common manual that is published together with the datasets; “AI4Arctic_challenge-dataset-manual”. Code with a get-started toolkit for the '<em>ready-to-train</em>' dataset: https://github.com/astokholm/AI4ArcticSeaIceChallenge <br> Version 2 has updated the files and now contains files with icecharts (previously absent) as the AutoICE Challenge has been finalised. <br> A quick challenge video overview of the challenge is available at: https://youtu.be/iuXIeLPyKfg This item is part of the Collection https://doi.org/10.11583/DTU.c.6244065
DOI: 10.6084/m9.figshare.23667462.v1
title: Annex 2: distribution of reasons for retraction according to the type of male-female collaboration, in whole account and in fractional account

description: Annex 2: distribution of reasons for retraction according to the type of male-female collaboration, in whole account and in fractional account
DOI: 10.11583/dtu.13011134
title: AI4Arctic / ASIP Sea Ice Dataset - version 2

description: The AI4Arctic / ASIP Sea Ice Dataset - version 2 (ASID-v2) contain 461 Sentinel-1 Synthetic Aperture Radar (SAR) scenes matched with sea ice charts produced by the Danish Meteorological Institute in 2018-2019. Ice charts contain sea ice concentration, stage of development and form of ice, provided in manual drawn polygons. The ice charts have been projected into the the S1 geometry for easy use as labels in deep learning or other machine learning algorithm training processes. The dataset also includes AMSR2 microwave radiometer sensor measurements to compliment the learning of the of sea ice concentrations although in a much lower resolution than the Sentinel-1 data. Details are described in the manual that is published together with the dataset.The manual has been revised, the latest is the 30-09-2020 version.<br><br>
DOI: 10.11583/dtu.13011134.v2
title: AI4Arctic / ASIP Sea Ice Dataset - version 2

description: The AI4Arctic / ASIP Sea Ice Dataset - version 2 (ASID-v2) contain 461 Sentinel-1 Synthetic Aperture Radar (SAR) scenes matched with sea ice charts produced by the Danish Meteorological Institute in 2018-2019. Ice charts contain sea ice concentration, stage of development and form of ice, provided in manual drawn polygons. The ice charts have been projected into the the S1 geometry for easy use as labels in deep learning or other machine learning algorithm training processes. The dataset also includes AMSR2 microwave radiometer sensor measurements to compliment the learning of the of sea ice concentrations although in a much lower resolution than the Sentinel-1 data. Details are described in the manual that is published together with the dataset.The manual has been revised, the latest is the 30-09-2020 version.<br><br>
DOI: 10.23708/tx5de2
title: Genetic diversity assessment of 1539 fonio (Digitaria exilis) accessions in West Africa: SSRs data

description: Neglected and under-utilized species represent important genetic resources and untapped reservoirs of diversity to address global food and nutrition challenges. Characterizing the extent of their genetic diversity and making it available is of prime importance to their wider use and to develop collaborative research networks. White fonio (Digitaria exilis (Kippist) Stapf) is a small cereal domesticated and cultivated in West Africa. It represents locally important food resources for smallholder farmers of arid and semi-arid lands. Using an unprecedented number of 1539 genotyped landraces, we assessed fonio genetic diversity and population structure across its entire area of cultivation with simple sequence repeat markers (SSRs). Results are available through an open and user-friendly R shiny application on this link. We provide here two files: (1) the passport data of the white fonio collection containing information for the 2372 accessions (ID, country, project, year of sampling, geographical coordinates and whether the accession was included or not in the genetic analyses). (2) The SSRs data for the 1539 accessions successfully genotyped, associated with passport information. The genotypes were obtained with 14 microsatellites markers. For each marker, the score of the two alleles are separated by /. Missing genotypes are coded 999/999.
DOI: 10.5281/zenodo.7871671
title: CA_SOE_PotTrial.xlsx

description: The data shows data of biomass yield in a pot trial using recycling N-fertilizers
DOI: 10.5281/zenodo.7871672
title: CA_SOE_PotTrial.xlsx

description: The data shows data of biomass yield in a pot trial using recycling N-fertilizers
DOI: 10.2908/tin00096
title: Individuals using the internet for buying goods or services
title: Personen, die Waren oder Dienstleistungen über das Internet erworben haben
title: Particuliers ayant acheté des biens ou des services sur l'internet

description: Für private Zwecke kaufen oder bestellen. In den letzten 12 Monaten vor der Erhebung. Manuell eingegebene e-mails sind ausgeschlossen.
description: Buy or order for private use. Within the last 12 months before the survey. Manually typed e-mails are excluded.
description: Acheter ou commander pour des but privés. Au cours des derniers 12 mois avant l'enquête. A l'exclusion des e-mails saisis manuellement.
DOI: 10.11583/dtu.19688989
title: Onshore wind generation time series for technology SP277 HH150 at resource grade A (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 277 W/m2 turbines at hub height (HH) of 150 m for the regions shown in the attached map. The analysed wind power plants are sited at the best 10 % of locations in each region, i.e., in resource grade (RG) A. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.19688989.v1
title: Onshore wind generation time series for technology SP277 HH150 at resource grade A (PECD 2021 update)

description: This data (csv file) provides simulated hourly time series of onshore wind generation with specific power (SP) 277 W/m2 turbines at hub height (HH) of 150 m for the regions shown in the attached map. The analysed wind power plants are sited at the best 10 % of locations in each region, i.e., in resource grade (RG) A. The map shows the resulting capacity factors (annual mean). The Excel file gives a rough indication if this wind technology is suitable for the different regions for this RG or not. The available land considers all onshore land area of a region, except lakes, cities, and very high elevation locations. The possible impact of any existing onshore wind installations in the region is not considered. Wake losses are modeled, with additional 5 % of other losses and unavailability considered. The time stamps are in GMT; the variable (column) names relate to the region names shown in the maps. The data include also country-level aggregations, e.g., UK00 is the aggregated onshore wind generation of all the UK regions (weighted by regional installed capacities). The data are part of the variable renewable energy generation time series created for ENTSO-E in the 2021 update of the Pan-European Climate Database (PECD) dataset. ENTSO-E has used the data in ERAA 2021 and Winter Outlook 2021-2022 assessments, and they are used in TYNDP 2022. The simulations are carried out by DTU Wind Energy, with the future technology selection and data validation discussed and agreed with ENTSO-E and its members. The linked journal paper (1st link) describes the simulation methodology (combination of ERA5 and GWA data is used). It is requested that the paper is cited when the data are used. The linked related journal paper (2nd link) describes the concept of resource grades and how they can be applied in energy system analyses. This item is part of a larger collection of wind and solar data: https://doi.org/10.11583/DTU.c.5939581
DOI: 10.11583/dtu.18319475.v2
title: Supporting information for scientific article: Inclusion of multiple climate tipping as a new impact category in life cycle assessment of polyhydroxyalkanoate (PHA)-based plastics, resulting from the EU's Horizon project BioBarr.

description: Supporting information for the article:<br>Vea, E.B., Fabbri, S., Spierling, S., Owsianiak, M., 2021. Inclusion of multiple climate tipping as a new impact category in life cycle assessment of polyhydroxyalkanoate (PHA)-based plastics. Sci. Total Environ. 788, 147544.<br><br>It contains: <br><br>Details of literature review, data underlying LCA model, details of life cycle impact assessment, and additional results (pdf file), and Simpro model processes (xlsx file). To open the files, use Adobe Acrobat and Microsoft Excel.
DOI: 10.11583/dtu.18319475
title: Supporting information for scientific article: Inclusion of multiple climate tipping as a new impact category in life cycle assessment of polyhydroxyalkanoate (PHA)-based plastics, resulting from the EU's Horizon project BioBarr.

description: Supporting information for the article:<br>Vea, E.B., Fabbri, S., Spierling, S., Owsianiak, M., 2021. Inclusion of multiple climate tipping as a new impact category in life cycle assessment of polyhydroxyalkanoate (PHA)-based plastics. Sci. Total Environ. 788, 147544.<br><br>It contains: <br><br>Details of literature review, data underlying LCA model, details of life cycle impact assessment, and additional results (pdf file), and Simpro model processes (xlsx file). To open the files, use Adobe Acrobat and Microsoft Excel.
DOI: 10.11583/dtu.18319475.v1
title: Supplementary information for the article: Inclusion of multiple climate tipping as a new impact category in life cycle assessment of polyhydroxyalkanoate (PHA)-based plastics, resulting from the EU's Horizon project BioBarr.

description: Supporting information for the article:<br>Vea, E.B., Fabbri, S., Spierling, S., Owsianiak, M., 2021. Inclusion of multiple climate tipping as a new impact category in life cycle assessment of polyhydroxyalkanoate (PHA)-based plastics. Sci. Total Environ. 788, 147544.<br><br>It contains: <br><br>Details of literature review, data underlying LCA model, details of life cycle impact assessment, and additional results (pdf file), and Simpro model processes (xlsx file). To open the files, use Adobe Acrobat and Microsoft Excel.
DOI: 10.5281/zenodo.7258047
title: Removal of antibiotics by oxidoreductases_Literature data_systematic search_May 2023

description: An overview on reported removal efficiencies of antibiotics achieved with oxidoreductases (laccases, peroxidases and peroxygenases) within the last 15 years. Important operational parameters as pH, temperature and concentrations of enzyme, pollutant and cofactors are listed. Bibliometric data obtained by a keyword search in the SCOPUS database in May 2023 is presented, differentiating between selected and non selected studies. The dataset is structured as follows: Removal_Database: contains all relevant experimental conditions, the achieved removal efficencies and additional information on immobilization strategy or applied bioassays to assess theeffluent toxicity Bibliography_included_articles contains bibliographic information of included articles , assigning a code to each individual document after duplicate identification Bibliography_beta_lactams contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY (( beta-lactam OR amoxicillin OR ampicillin OR penicillin ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_fluoroquinolones contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY (( ciprofloxacin* OR fluoroquinolone* ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_macrolides contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY ( ( erythromycin* OR roxithromycin* OR azithromycin* OR macrolide* ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase Bibliography_sulfonamides contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY (( sulfamethoxazole OR sulfonamide ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_tetracyclines contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY ( ( tetracycline* ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_trimethoprim contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY ( ( trimethoprim ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliograpy_Additional Additional references found by screening of reference list of included articles antibiotic_susceptibility experimental conditions and results of antibiotic susceptibility tests which were applied in the selected literature to evaluate the removal of antibiotic effect
DOI: 10.5281/zenodo.7258046
title: Removal of antibiotics by oxidoreductases_Literature data_systematic search_May 2023

description: An overview on reported removal efficiencies of antibiotics achieved with oxidoreductases (laccases, peroxidases and peroxygenases) within the last 15 years. Important operational parameters as pH, temperature and concentrations of enzyme, pollutant and cofactors are listed. Bibliometric data obtained by a keyword search in the SCOPUS database in May 2023 is presented, differentiating between selected and non selected studies. The dataset is structured as follows: Removal_Database: contains all relevant experimental conditions, the achieved removal efficencies and additional information on immobilization strategy or applied bioassays to assess theeffluent toxicity Bibliography_included_articles contains bibliographic information of included articles , assigning a code to each individual document after duplicate identification Bibliography_beta_lactams contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY (( beta-lactam OR amoxicillin OR ampicillin OR penicillin ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_fluoroquinolones contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY (( ciprofloxacin* OR fluoroquinolone* ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_macrolides contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY ( ( erythromycin* OR roxithromycin* OR azithromycin* OR macrolide* ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase Bibliography_sulfonamides contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY (( sulfamethoxazole OR sulfonamide ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_tetracyclines contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY ( ( tetracycline* ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliography_trimethoprim contains bibliographic information of selected articles based on the keyword search string TITLE-ABS-KEY ( ( trimethoprim ) AND ( removal OR treatment OR degradation ) AND ( laccase OR peroxidase OR oxidoreductase OR tyrosinase OR peroxygenase OR oxidoreductase ) ) Bibliograpy_Additional Additional references found by screening of reference list of included articles antibiotic_susceptibility experimental conditions and results of antibiotic susceptibility tests which were applied in the selected literature to evaluate the removal of antibiotic effect
DOI: 10.14457/tu.the.2021.1187
title: Policies and regulations to promote electric vehicles in Thailand

description: Despite the effects of global warming and pollution in Thailand, which mostly result from the transportation sector, the amount of Internal Combustion Engine Vehicles has been dramatically rising and resulting in air pollution. Electric Vehicle adoption is the best modern solution chosen by many civilised countries. However, Thailand's adoption rate is considered low due to several factors related to the high price, limited choices of models, and insufficient charging stations, causing the range anxiety for a long road trip. With no coincidence or natural act, the adoption rates are building up from well-planed and supportive policies and regulations for EVs. This research provides a solution with precedent policies and regulations, which were chosen worldwide, and focuses on the supply-side regulation of the United States as seen in the large tax incentives for Tesla, Inc. to establish the Gigafactory in Nevada. Secondly, the demand-side policy and regulation of Norway was also analysed regarding its poor automobile industry where tax exemption and other non-fiscal incentives are, nevertheless, granted to make the cost of electric vehicle ownership cheaper. Lastly, the countermeasure for the reserved Nickel regulation of Indonesia was studied. Therefore, to promote Electric Vehicles in Thailand, adapting to the policies and regulations mentioned above to enhance domestic manufacturing and consumer demand will lead to the economies of scale for Electric Vehicles and thus remove all obstructions to the EV adoption in Thailand.