cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.2908/tai01
title: Sales of fertilisers by type of nutrient (source: Fertilizers Europe)
title: Ventes d'engrais par type de nutriment (source: Fertilizers Europe)
title: Verkauf von Düngemitteln nach Art des Nährstoffs (Quelle: Fertilizers Europe)

description: Geschätzte Menge an Dünger verkauft, pro Nährstoff.
description: Estimated quantity of fertilizer sold, per nutrient
description: Quantité estimée d'engrais vendu, par nutriment
DOI: 10.2908/tet00018
title: Trade in goods, by main world traders
title: Commerce de biens, par principaux négociants mondiaux
title: Warenhandel, nach den Weltweiten wichtigsten Händler

description: Die Tabelle zeigt den Warenhandel mit dem Rest der Welt der Hauptakteure auf dem Weltmarkt. Die Importe werden wertmäßig und in % auf der Basis "cif " (Kosten, Versicherung, Fracht) angegeben. Die Exporte sind wertmäßig und in % auf der Basis "fob" (frei an Bord) angegeben. Die Berechnung der Prozentsätze bezieht sich auf den Welthandel abzüglich des innergemeinschaftlichen EU Handels.
description: The table shows the trade exchanges with the rest of the world of the main players in the world market. Imports are mesured cif (cost, insurance, freight) and expressed in value and %. Exports are measured fob (free on board) and expressed in value and %. The calculation of shares is based on a world trade excluding the intra-EU trade.
description: Le tableau montre les échanges commerciaux avec le reste du monde des principaux acteurs sur le marché mondial. Les importations sont mesurées caf (coût, assurance et fret) et exprimées en valeur et %. Les exportations sont mesurées fab (franco à bord) et exprimées en valeur et %. Le calcul des pourcentages se base sur un commerce mondial évalué en excluant le commerce intra-communautaire.
DOI: 10.2908/tet00029
title: Extra-EU28 trade of other manufactured goods (SITC 6+8), by main partners
DOI: 10.2908/tet00030
title: Extra-EU28 trade of machinery and transport equipment (SITC 7), by main partners
DOI: 10.2908/tet00031
title: Extra-EU28 trade of chemicals and related products (SITC 5), by main partners
DOI: 10.2908/tet00032
title: Extra-EU28 trade of mineral fuels, lubricants and related materials (SITC 3), by main partners
DOI: 10.2908/tet00033
title: Extra-EU28 trade of raw materials (SITC 2+4), by main partners
DOI: 10.2908/tet00034
title: Extra-EU28 trade of food, drinks and tobacco (SITC 0+1), by main partners
DOI: 10.2908/tet00035
title: Extra-EU28 trade, by main partners, total product
DOI: 10.2908/tet00036
title: Share of trade with the EU28
title: Part du commerce avec l'UE28
title: Anteil am Handel mit der EU28

description: Die Tabelle zeigt den Anteil am Warenhandel mit der EU28 an (% der Importe aus der EU28 an den Gesamtimporten und % der Exporte nach der EU28 an den Gesamtexporten) der Mitgliedstaaten und den Hauptakteuren auf dem Weltmarkt.
description: The table shows the share of trade exchanges with the EU28 (% of imports from the EU28 in the total imports and % of the exports to the EU28 in the total exports) of the Member States and of the main players in the world market.
description: Le tableau montre la part des échanges commerciaux avec l'UE28 (% des importations venant de l'UE28 dans les importations totales et % des exportations vers l'UE28 dans les exportations totales) des Etats membres et des principaux acteurs sur le marché mondial.
DOI: 10.2908/tet00047
title: Intra-EU28 trade, by Member State, total product
title: Commerce intra UE28, par Etats membre, produit total
title: Intra-Handel der EU28, nach Mitgliedstaaten, Gesamtprodukt

description: Die Tabelle zeigt den Anteil (wertmässig und in %) der Mitgliedstaaten am intra-Handel der EU28 der Union.
description: The table shows the Member States' contribution (in value and %) to the intra-EU28 trade of the Union.
description: Le tableau montre la contribution (en valeur et %) des Etats membres au commerce intra-UE28 de l'Union.
DOI: 10.2908/tet00055
title: Extra-EU28 trade, by Member State, total product
DOI: 10.2908/tet00056
title: Extra-EU28 trade of mineral fuels, lubricants and related materials (SITC 3), by Member State
DOI: 10.2908/tet00058
title: Extra-EU28 trade of chemicals and related products (SITC 5), by Member State
DOI: 10.2908/tet00059
title: Extra-EU28 trade of machinery and transport equipment (SITC 7), by Member State
DOI: 10.2908/tet00060
title: Extra-EU28 trade of other manufactured goods (SITC 6+8), by Member State
DOI: 10.2908/tet00062
title: Extra-EU28 trade, by product group
DOI: 10.2908/tet00063
title: Extra-EU28 trade of food, drinks and tobacco (SITC 0+1), by Member State
DOI: 10.2908/tet00064
title: Extra-EU28 trade of raw materials (SITC 2+4), by Member State
DOI: 10.2908/ten00140
title: Greenhouse gas emissions in effort sharing decision (ESD) sectors
title: Treibhausgasemissionen in den von der EU-Entscheidung über die Verteilung der Anstrengungen (ESD) abgedeckten Sektoren
title: Emissions de gaz à effet de serre dans les secteurs couvert par la Décision relative à l'effort partagé (ESD)

description: Der Berechnung des Indikators beruht auf denjenigen Emissionen, welche von der Entscheidung Nr. 406/2009/EC vom 23. April 2009 über die Anstrengungen der Mitgliedstaaten zur Reduktion ihrer Treibhausgasemissionen (ESD Entscheidung) abgedeckt werden. Die ESD Entscheidung definiert jährliche, verbindliche, nationale Ziele für Emissionen, welche nicht vom EU Emissionsrechtehandel abgedeckt sind. Die ESD Emissionen werden berechnet, indem Emissionen aus dem Emissionsrechtehandel, CO2 Emissionen aus nationaler Luftfahrt, sowie Emissionen von NF3 vom den nationalen Gesamtemissionen abgezogen werden. Die Gesamtemissionen sind die nationalen Gesamtwerte, welche der UNFCCC gemeldet wurden (abzüglich LULUCF, internationaler Luftfahrt und internationalem Transport zur See). Für den Zeitraum 2005-2012 wurden zusätzliche Emissionsschätzungen abgezogen, um den heutigen Umfang des EU Emissionsrechtehandels zu berücksichtigen. Dieser Berechnung liegt eine Methodik der Europäischen Umweltagentur zur Berechnung konsistenter Zeitreihen zugrunde.
description: The indicator calculation is based on the emissions covered under the Effort Sharing Decision (406/2009/EC). The Effort Sharing Decision sets national annual binding targets for emissions not covered under the EU emission trading scheme (ETS). The ESD emissions are calculated by deducting ETS verified emissions, CO2 emissions from domestic aviation and NF3 emissions from national total emissions. Total emissions are national totals reported under the UNFCCC (excluding LULUCF, international aviation and international maritime transport). For the period 2005-2012, additional emission estimates are deducted in order to reflect the current scope of the EU ETS, following the European Environment Agency methodology for calculating consistent time series.
description: Le calcul de l'indicateur est base sur les émissions couvertes par la Décision relative à l'effort partagé 406/2009/EC.. La Décision relative à l'effort partagé prévoit des engagements annuels chiffrés pour les émissions non couvertes par le système d'échange de quotas d'émissions (SEQE). Les émissions régies par la Décision sont calculées en déduisant les émissions vérifiées se rapportant au SEQE, les émissions de CO2 liées aux vols intérieurs et les émissions de NH3 à partir des émissions nationales totales. Les émissions totales sont des totaux nationaux reportés dans le cadre de la convention UNFCCC (à l'exclusion de LULUCF, vols internationaux et transport maritime international). Sur la période 2005-2012, l'estimation des émissions additionnelles sont déduites en vue de refléter le champ actuel du SEQE UE, suivant la méthodologie adoptée par l'Agence Européenne de l'Environnement pour le calcul de séries temporelles cohérentes.
DOI: 10.2908/tgs00028
title: Gross domestic product, Candidate countries and potential candidates
title: Produit intérieur brut aux prix de marché, Pays candidats et candidats potentiels
title: Bruttoinlandsprodukt zu Marktpreisen, Kandidaten- und potentielle Kandidatenländer

description: Das BIP (Bruttoinlandsprodukt) ist ein Indikator für die wirtschaftliche Lage eines Landes. Es entspricht dem Gesamtwert der produzierten Waren und Dienstleistungen abzüglich des Werts der Waren und Dienstleistungen, die bei ihrer Produktion als Vorleistungen verwendet werden.
description: GDP (gross domestic product) is an indicator for a nation's economic situation. It reflects the total value of all goods and services produced less the value of goods and services used for intermediate consumption in their production.
description: Le PIB (produit intérieur brut) est un indicateur de la situation économique d'une nation. Il reflète la valeur totale de tous les biens et services produits, diminuée de la valeur des biens et services utilisés dans la consommation intermédiaire pour leur production.
DOI: 10.5281/zenodo.7388688
title: AOD and FMF dataset retrieved from NNAero in eastern and northern China from 2010 to 2020

description: In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ± (0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF. If you use this dataset for related scientific research,please cite the below-listed corredponding references first(Chen X et al. ,RSE, 2020 ) Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., &amp; Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006.https://doi.org/10.1016/j.rse.2020.112006
description: If you have any questions,please contact me(Email: chenxf@aircas.ac.cn).
description: {"references": ["Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., & Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006."]}
DOI: 10.5281/zenodo.7388838
title: AOD and FMF dataset retrieved from NNAero in eastern and northern China from 2010 to 2020

description: In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ± (0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF. If you use this dataset for related scientific research,please cite the below-listed corredponding references first(Chen X et al. ,RSE, 2020 ) Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., &amp; Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006.https://doi.org/10.1016/j.rse.2020.112006
description: If you have any questions,please contact me(Email: chenxf@aircas.ac.cn).
description: {"references": ["Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., & Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006."]}
DOI: 10.5281/zenodo.7388687
title: AOD and FMF dataset retrieved from NNAero in eastern and northern China from 2010 to 2020

description: In this work, the development of an artificial Neural Network for AEROsol retrieval (NNAero) is presented. NNAero uses data from the NASA MODerate resolution Imaging Spectroradiometer (MODIS) flying on the NASA Terra and Aqua satellites. The MODIS-derived spectral reflectances of solar radiation at the top of the atmosphere (TOA) and at the surface were used together with ground-based Aerosol Robotic Network (AERONET) measurements of Aerosol Optical Depth (AOD) and FMF to train a Convolutional Neural Network (CNN) for the joint retrieval of FMF and AOD. The NNAero results over northern and eastern China were validated against an independent reference AERONET dataset (i.e. not used in training the CNN). The results show that 68% of the NNAero AOD values are within the MODIS expected error (EE) envelope over land of ± (0.05 + 15%), which is similar to the results from the MODIS Deep Blue (DB) algorithm (63% within EE), and both are better than the Dark Target (DT) algorithm (31% within EE). The validation of the NNAero FMF vs AERONET data shows a significant improvement with respect to the DT FMF, with Root Mean Squared Prediction Errors (RMSE) of 0.1567 (NNAero) and 0.34 (DT). The NNAero method shows the potential of improved retrieval of the FMF. If you use this dataset for related scientific research,please cite the below-listed corredponding references first(Chen X et al. ,RSE, 2020 ) Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., &amp; Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006.https://doi.org/10.1016/j.rse.2020.112006
description: If you have any questions,please contact me(Email: chenxf@aircas.ac.cn).
description: {"references": ["Chen, X., de Leeuw, G., Arola, A., Liu, S., Liu, Y., Li, Z., & Zhang, K. (2020). Joint retrieval of the aerosol fine mode fraction and optical depth using MODIS spectral reflectance over northern and eastern China: Artificial neural network method. Remote Sensing of Environment, 249, 112006."]}
DOI: 10.6084/m9.figshare.21665900
title: Boerhavia diffusa (Purnarnava) vs ACE II inhibitor: strategiesCOVID-19

description: Insilico molecular docking
DOI: 10.6084/m9.figshare.21665858.v1
title: code.zip

description: genetic algorithm(GA) is used to optimize the FBG accelerometer to help us fabricating a 3-axis seismometer using a single fiber without bending.
DOI: 10.6084/m9.figshare.21665858
title: genetic algorithm used for optimization of hinge structure based FBG accelerometer.zip

description: genetic algorithm(GA) is used to optimize the FBG accelerometer to help us fabricating a 3-axis seismometer using a single fiber without bending.
DOI: 10.6084/m9.figshare.21665858.v2
title: genetic algorithm used for optimization of hinge structure based FBG accelerometer.zip

description: genetic algorithm(GA) is used to optimize the FBG accelerometer to help us fabricating a 3-axis seismometer using a single fiber without bending.
DOI: 10.6084/m9.figshare.21665900.v1
title: Boerhavia diffusa (Purnarnava) vs ACE II inhibitor: strategiesCOVID-19

description: Insilico molecular docking
DOI: 10.6084/m9.figshare.21665639.v1
title: Rph-assembly_predicted-proteins.fasta

description: The proteome sequence.
DOI: 10.5281/zenodo.7324189
title: Wikidata Dump Banks

description: RDF dump of wikidata produced with wdumper.<br>View on wdumper<b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b>
DOI: 10.5281/zenodo.7324188
title: Wikidata Dump Banks

description: RDF dump of wikidata produced with wdumper.<br>View on wdumper<b>entity count<b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0</b></b>
DOI: 10.5281/zenodo.7387270
title: User validation on Flight Delay Prediction

description: A Flight Delay Prediction tool has been developed using the Artificial intelligence method. EUROCONTROL provided the dataset as the External Advisory Board (EAB) and suggested addressing the issue of explanation for delay propagation. Three different methods of adding explanations are considered. The methods are Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and user-centric XAI using DALEX. The objective of the validation exercise is to assess: How XAI helps users to <strong> understand </strong> the results and the behaviour of the AI system? How XAI helps users to <strong> trust </strong> the results of the AI system? How XAI helps <strong> human performance </strong>? In the exercise, you will assess the explanations given by the three ML methods (LIME, SHAP and DALEX) and rate the explanation by answering a number of questions about each method.
DOI: 10.5281/zenodo.7387269
title: User validation on Flight Delay Prediction

description: A Flight Delay Prediction tool has been developed using the Artificial intelligence method. EUROCONTROL provided the dataset as the External Advisory Board (EAB) and suggested addressing the issue of explanation for delay propagation. Three different methods of adding explanations are considered. The methods are Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and user-centric XAI using DALEX. The objective of the validation exercise is to assess: How XAI helps users to <strong> understand </strong> the results and the behaviour of the AI system? How XAI helps users to <strong> trust </strong> the results of the AI system? How XAI helps <strong> human performance </strong>? In the exercise, you will assess the explanations given by the three ML methods (LIME, SHAP and DALEX) and rate the explanation by answering a number of questions about each method.
DOI: 10.5281/zenodo.7387289
title: Automatic global climate classification, preserving Köppen thermal classes with added aridity information and a hypertropical class

description: A streamlined system using growing degree days for climate classification that delivers both temperature and aridity information in all classes and an added hypertropical class. Primary thermal classes remain consistent with the primary Köppen climate classes and their established connection to biomes. The eight class to tif tool ends with a .tif file of climate classes. The steps may need to be modified if using different inputs than Envirem and WorldClim 1.4. Envirem is missing datum information, but ArcGIS makes the same calculations <br> without the project raster step. Note that WorldClim 1.4 applies a scaling factor of 10 for temperature data, which is propagated in Envirem. Import the symbology layer file climate_8_class. If ArcGIS is being cranky about displaying the (blue) hypertropical class, try reseting with the 'Add All Values' button,<br> followed by hitting 'Default Colors'.
description: Manuscript in review.
description: {"references": ["Title, P.O. and Bemmels, J.B. 2018. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41:291-307.", "Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology: A Journal of the Royal Meteorological Society 25:1965-1978."]}
DOI: 10.5281/zenodo.7387288
title: Automatic global climate classification, preserving Köppen thermal classes with added aridity information and a hypertropical class

description: A streamlined system using growing degree days for climate classification that delivers both temperature and aridity information in all classes and an added hypertropical class. Primary thermal classes remain consistent with the primary Köppen climate classes and their established connection to biomes. The eight class to tif tool ends with a .tif file of climate classes. The steps may need to be modified if using different inputs than Envirem and WorldClim 1.4. Envirem is missing datum information, but ArcGIS makes the same calculations <br> without the project raster step. Note that WorldClim 1.4 applies a scaling factor of 10 for temperature data, which is propagated in Envirem. Import the symbology layer file climate_8_class. If ArcGIS is being cranky about displaying the (blue) hypertropical class, try reseting with the 'Add All Values' button,<br> followed by hitting 'Default Colors'.
description: Manuscript in review.
description: {"references": ["Title, P.O. and Bemmels, J.B. 2018. ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling. Ecography 41:291-307.", "Hijmans, R.J., Cameron, S.E., Parra, J.L., Jones, P.G. and Jarvis, A. 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology: A Journal of the Royal Meteorological Society 25:1965-1978."]}
DOI: 10.5281/zenodo.7387906
title: Nature News Disparities Dataset

description: This dataset can be used to recreate all analyses in the following git repository: https://github.com/greenelab/nature_news_disparities for the manuscript "Analysis of science journalism reveals gender and regional disparities in coverage." The manuscript can be viewed here: https://github.com/greenelab/nature_news_manuscript
DOI: 10.5281/zenodo.5727119
title: High-resolution and full coverage AOD downscaling based on the bagging model over the arid and semi-arid areas, NW China

description: High-resolution and full coverage 250 m monthly AOD product over the arid and semi-arid areas, NW China. the scale factor is 1000.