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1,046 Research products

  • Rural Digital Europe
  • 2018-2022
  • Open Access
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Gängige Formen von Diskriminierung sowie die Reproduktion normativer Stereotype sind auch bei künstlicher Intelligenz an der Tagesordnung. Die Beitragenden erläutern Möglichkeiten der Reduktion dieser fehlerhaften Verfahrensweisen und verhandeln die ambivalente Beziehung zwischen Queerness und KI aus einer interdisziplinären Perspektive. Parallel dazu geben sie einem queer-feministischen Wissensverständnis Raum, das sich stets als partikular, vieldeutig und unvollständig versteht. Damit eröffnen sie Möglichkeiten des Umgangs mit KI, die reduktive Kategorisierungen überschreiten können.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Social Science Open ...arrow_drop_down
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Social Science Open ...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Hung, Jason;

    Here researchers have the disposition to engage in the scholarly discourse on how the pandemic adversely influenced individuals' mental health and what remedies should be exercised in response to the mental health challenges. There is a shortage of scholarly discussion about who benefitted from the occurrence of the pandemic. Mancini et al. argued that the pandemic benefitted the social and mental health functioning of a subset of the population, despite the pandemic causing considerable risks of harm to mental health. In this perspective, the author summarizes relevant findings and arguments to present which subsets of the population benefitted at school, at home, and in the workplace during the pandemic. Although COVID-19 is no longer deemed a pandemic, many by-products of the public health crisis, including the encouragement of remote work and studies, remain. In this perspective, by understanding who benefitted from the pandemic and why, the author can evaluate if any public policies formed in response to the pandemic should be kept in the long run in order to maximize individuals' mental health.

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    Apollo
    Other ORP type . 2022
    Data sources: Apollo
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Apolloarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Apollo
      Other ORP type . 2022
      Data sources: Apollo
  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Koptev, Mikhail;

    These data files containg code sources for dataset creation & model learning (neural-jsdf.zip) and collected synthetic dataset of free & collided postures for robotic arm Franka (sdf_3m_full_mesh.mat). Follow the Readme.MD files to launch the code if needed. Corresponding Git repo: https://github.com/epfl-lasa/Neural-JSDF

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Infoscience - EPFL s...arrow_drop_down
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    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO
    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Infoscience - EPFL s...arrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
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      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: ZENODO
      ZENODO
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Bennetto, Laura;

    This video shows the highlights of our Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 Music Licensing details: ASLC-1E1DFFF8-153AD9FEF0 The music is from Adobe Music, is called GUITAR-EPIC-TRAILER-VOCAL_AdobeStock_511629251 YouTube Link: https://youtu.be/OfRa31Wvix8

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    ZENODO
    Audiovisual . 2022
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    ZENODO
    Audiovisual . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Audiovisual . 2022
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      ZENODO
      Audiovisual . 2022
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Raigonda, Megha;

    Potato (Solanum tuberosum) is the main crop that is vegetatively propagated(cloning). It is the third most important food crop after wheat and rice in terms of human consumption. This crop is affected by various types of diseases like bacterial, fungal, and viral diseases. As, a traditional practice some viral disease are identified by visual symptoms and some by laboratory methods like Enzyme-linked immunosorbent assay (ELISA), real time reverse-transcription- polymerase chain reaction (RT-PCR) etc., the above methods are tedious, computational costs along with time will be high and it is requiring a more labor-intensive and controlled lab structure to carry out the experiment. So, there is an urgent need for developing an automated model which employs machine learning, deep learning methodology which are subset of Artificial Intelligence. This model helps agriculturist/ farmers for early detection of viral disease. and take effective measures for huge yield loss. The dataset shows different types of viral diseases that the potato crop foliar and tubers get effected with namley Mosaic Virus, Potato Leaf Roll Virus(PLRV), on foliar, and on tuber Potato Spindle Tuber Viriod (PSTVD), Potato Virus Y(PVY)-tuber cracking. The dataset of foliar and tuber comprises of Nineteen hundred and seventy two images Mosaic(666), PLRV(527), Healthy leaf(135), PSTVD(85), PVY cracking (559). Each image of size in pixels is 4288*2848 and camera employed is Nikon D90,its configuration details include f-stop-f/5.3, ISO speed -ISO-250, focal length-80mm, contrast -medium, No flash. The dataset is collected from potato fields of University of Agricultural Sciences, Dharwad where 3 acres of land is cultivated with certified seeds from modipuram regional Centre and 1 acre is cultivated with uncertified seeds. The scientist from the research institute shared the image data from Indian Council of Agricultural Research- Central Potato Research Institute (ICAR- CPRI) shimla. The data set is captured in the day light by placing the leaf or tuber on black background. The image data collected has different symptoms like plant affected with mosaic virus has yellow color spread throughout the leaf the leaf can be classified as mild, medium and severe mosaic. The color feature plays a major role in classifying the mosaic viral disease the symptoms include yellow color propagated throughout the leaf. The PLRV symptoms are rolling of leaf towards upward direction the texture of the leaf is little crunchy in nature. The main features here are shape and texture for accurately classifying the disease. In tuber PSTVD where the tubers are elongated that is based on shape features the classifier gives the accurate results. PVY cracking it is one of the strains of potato virus Y where the tubers have cracks on external part which is noninfectious. The cracks generally start at bud and extend lengthwise.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ Mendeley Dataarrow_drop_down
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    Mendeley Data
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Mendeley Data
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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      Mendeley Data
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
      Mendeley Data
      Dataset . 2022
      License: CC BY
      Data sources: Datacite
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    Authors: Emmerson, Parker;

    For praising Jehovah do I publish these mathematical gesturing forms by His graciousness. The Book of Eternity proposes a new, mathematical balancing language in which the logic stems from infinity instead of zero. The designation of the balance comes from the location at which different meanings of infinity linguistically balance with each other and provides a way for people to continue extrapolating forms and functions that logically would follow. This balancing at oneness is analogous to the resurrection of Jesus as the phenomenological velocity can come into being or be canceled out and have no effect. So, when Jesus died, that was the canceling out, and when Jesus (Yeshua Ben Joseph Jehovah) was raised, that was the coming into being. As the Resurrection, He is intimately tied to the creation as the savior. This can be expressed mathematically. As oneness, this balancing is infinite and meets symbolically with the differentiated meanings of oneness. The oneness is both the canceling (of the square roots within the height function), and the intersection of meanings (of infinity through the word of Jehovah). This book elucidates the kinds of dimensionality with which man interacts normally: distance, angle, number, mass.

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    ZENODO
    Audiovisual . 2022
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    Data sources: Datacite
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    ZENODO
    Audiovisual . 2022
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      ZENODO
      Audiovisual . 2022
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      ZENODO
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    Authors: Langsrud, Solveig; Skuland, Silje E.;

    The Risk-behaviour map is a document intended to aid access to and transfer of key data between research groups in the European project Safeconsume. The map covers only steps from retail to consumption for the case studies in Safeconsume where the consumer can reduce risk for foodborne infection (CCHs, Critical Consumer Handling). The map contains information about observed/reported behaviours that can affect risk for foodborne infection divided by country, consumer group, pathogen, food and step in the journey from retail to consumption. Details about data collection is given in: Skuland, S.E., Borda, D., Didier, P., Dumitras¸cu, L., Ferreira, V., Foden, M., Langsrud, S., Maître, I., Martens, L., Møretrø, T., Nguyen-The, C., Nicolau, A. I., Nunes, C., Rosenberg, T. G., Teigen, H. M., Teixeira, P., Truninger, M., 2020. European Food Safety: Mapping Critical Food Practices and Cultural Differences in France, Norway, Portugal, Romania and the UK, in: Skuland, S.E. (Ed.). SIFO report, Oslo. ODA Open Digital Archive: European food safety: Mapping critical food practices and cultural differences in France, Norway, Portugal, Romania and the UK (oslomet.no) Questions about the RM-map can be raised to the SafeConsume project coordinator: Solveig.langsrud@nofima.no Variable list: Name Description CCH/Critical steps Identification of the step and flow diagram the entry belongs to: The step in the flow diagram where the consumer through actions or choices can significantly reduce risk of foodborne infection The CCHs/critical steps belong to one of the following processes: Poultry and vegetables (PVF), Eggs (EGG), Shellfish (SHE), Ready-to-Eat (RTE). Each step is accompanied by the principle of risk reducing effect: Food choice: Buy or eat food with lower risk (e.g avoid buying food if not stored properly in shop, buying pasteurised products, choosing to eat food before use-by-date). Applies to all pathogens. Inhibit growth: Storing ready-to-eat food at cool temperature and consume within expiration date or adding preservatives. Applies to Listeria and Salmonella Wash/Remove: Wash vegetables and fruit. Applies to all pathogens Kill/Heat: Heat treatment to kill pathogens, freezing (Campylobacter) Personal hygiene: Avoid cross-contamination through hand washing or not touching food. Not preparing food when sick Hygiene: Avoid cross-contamination through washing surfaces and using clean utensils Cause or sources Description of causes and sources for the hazard to occur (presence, survival, transfer or growth of pathogen). See Appendix 3 for details Consumer Id Unique identifier of consumer. Pathogen The pathogen(s) that are relevant for the specific CCH/critical step Expert opinion: Effect on pathogen Effect of behaviour on the hazard estimated by a team of microbiologists. Effect on pathogen The effect on pathogen is an estimate of the change in the level of viable pathogens as a direct or indirect consequence of the behaviour, action or process. Consumer group, education, income, rural/urban and country When applicable, demographic data associated with the entry. Classification Name Attributes Classification, llist of codes/units CCH/Critical step Predefined, multiple choices EGG 1 Food choice EGG 3.2 Hygiene EGG 4a Inhibit growth EGG 4b Inhibit growth EGG 5.1 Hygiene EGG 5.2 Personal hygiene EGG 6a Kill EGG 6b Kill EGG 6c Food choice EGG 6c Inhibit growth EGG 7.3 Inhibit growth EGG 8.3 Inhibit growth EGG 9.1 Inhibit growth EGG 11.3 Inhibit growth PVF 1.1 Food choice PVF 1.2 Food choice PVF 2.1 Inhibit growth PVF 3a Inhibit growth PVF 3a Kill PVF 3b Inhibit growth PVF 5.1 Personal hygiene PVF 5.2 Personal hygiene PVF 6.1 Kill PVF 7a Wash/Remove PVF 7b Personal hygiene PVF 7b Wash/Remove PVF 8b Hygiene PVF 8b Personal Hygiene PVF 9.1 Hygiene PVF 10.1 Hygiene PVF 11.1 Inhibit growth PVF 11.2 Inhibit growth RTE 1.1 Food choice RTE 3.1 Inhibit growth RTE 6.1 Inhibit growth RTE 4b Personal hygiene RTE 5.2 Hygiene RTE 5.2 Personal hygiene RTE 6.1 Inhibit growth RTE 7.1 Inhibit growth RTE 7.2 Personal hygiene SHE 1.1 Food choice SHE 7.1 Kill No risk Not designated to CCH Causes/sources Free text Consumer ID Free text Pathogen Predefined, multiple choice Salmonella: S. Enterica Campylobacter: C. jejuni Listeria: Listeria monocytogenes Norovirus Toxoplasma: Toxoplasma gondii Expert opinion: Effect on pathogen Predefined High reduction: This behaviour will have a high reduction on the level of pathogens on food/surfaces/hands Median reduction: This behaviour will reduce the level of pathogens on food/surfaces/hands No effect: This behaviour will most likely not have a significant effect on the level of viable pathogens (< 1 log10 reduction/increase or less than 10 cells/particles transfer) For food choice: Random choice is rated as no effect Median increase: This behaviour will lead to a higher number of pathogens High increase: This behaviour will significantly increase the number of pathogens on food/surfaces/hands Effect on pathogen comment Free text Consumer group Predefined Elderly; >70 years, men and women Pregnant women; immunocompromized Young family: Couples (married or cohabitant) where the women is pregnant or living with their own child(ren) (including stepchildren and adopted children) aged less than 12 months Young, single man: Men age 20-29, Living alone or with flatmates Other: Consumers not belonging to the defined groups Educational level Predefined Basic Secondary Tertiary Not given Living area Predefined Rural Urban Income Predefined Low Median High Country Predefined Portugal, France, Romania, UK, Norway, Hungary, More details about the dataset can be achieved from the authors

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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Dataset . 2022
    License: CC BY
    Data sources: ZENODO; Sygma
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      ZENODO
      Dataset . 2022
      License: CC BY
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      ZENODO
      Dataset . 2022
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    Authors: DSETT, Territoire, évaluation des impacts et adaptation au climat;

    Cartographie des îlots de chaleur et de fraîcheur urbains (ICFU) et des écarts de température relatifs de surface à partir de données satellitaires de 2013-2014. La cartographie des ICFU a été réalisée pour les *[centres de population du recensement de 2021](https://www150.statcan.gc.ca/n1/pub/92-195-x/2021001/geo/pop/pop-fra.htm)* (CTRPOP) ayant au moins 1 000 habitants et une densité d'au moins 400 habitants km2 à laquelle est ajoutée une zone tampon de 2 km, couvre ainsi l'ensemble du Québec urbain, soit 14 508 km2. La méthode de catégorisation des ICFU est le classement en 9 classes des écarts de température relatifs prédits pour chaque centre de population. Les valeurs d'intervalles pour chaque classe et chaque centre de population de diffusion sont présentées dans un index. Les classes 1 à 3 sont généralement choisies pour représenter les îlots de fraîcheur tandis que les classes 8 et 9, les îlots de chaleur. Puisque les températures de surface ont été analysées à l'échelle du Québec entier, mais que les classes sont produites pour une région donnée, les écarts de température entre les différentes classes varient d'une région à l'autre. Ainsi, il existe des différences observées dans les écarts de températures relatifs prédits entre le Nord et le Sud du Québec. Par exemple, un écart de température relatif de 2°C peut être présent dans la classe 1 (plus frais) dans les régions situées dans le Sud du Québec, mais peut être présent dans la classe 9 (très chaud) dans les régions du Nord du Québec. La cartographie des écarts de température est produite à l'échelle de l'écoumène du Québec avec une zone tampon de 5 km (recensement de 2016, 167 764 km2). Cette cartographie a été réalisée avec un modèle prédictif d'apprentissage automatique construit à partir de la bande de température (30 m) de Landsat-8 fournie par le United States Geological Survey (USGS), pour prédire l'écart de température relatif de surface à partir des autres bandes multispectrales fusionnées (résolution de 15 m) en y dérivant plusieurs variables indépendantes ainsi que d'autres variables géospatiales comme l'hydrographie et la topographie. Cette cartographie servira de référence pour une analyse diachronique (2013-2014 c. 2020-2022) disponible à partir de l'hiver 2023. La production de ces données a été coordonnée par l'Institut national de santé publique du Québec (INSPQ) et réalisée par le laboratoire de télédétection forestière du Centre d'Enseignement et de Recherche en Foresterie (CERFO), financé dans le cadre du *[Plan d'action 2013-2020 sur les changements climatiques](https://www.environnement.gouv.qc.ca/changementsclimatiques/plan-action.asp)* du gouvernement du Québec intitulé Le Québec en action vert 2020. Mapping of urban heat and freshness islands (ICFU) and relative surface temperature differences based on satellite data from 2013-2014. ICFU mapping was carried out for* [2021 census population centers] (https://www150.statcan.gc.ca/n1/pub/92-195-x/2021001/geo/pop/pop-fra.htm) * (CTRPOP) with at least 1,000 inhabitants and a density of at least 400 inhabitants km2 to which is added a 2 km buffer zone, thus covers the whole of urban Quebec, i.e. 14,508 km2. The ICFU categorization method is the classification of the predicted relative temperature differences for each population center into 9 classes. The interval values for each class and each dissemination population center are presented in an index. Classes 1 to 3 are generally chosen to represent islands of freshness while classes 8 and 9, islands of heat. Since surface temperatures have been analyzed across Quebec, but the classes are produced for a given region, the differences in temperature between the different classes vary from one region to another. Thus, there are observed differences in the predicted relative temperature differences between northern and southern Quebec. For example, a relative temperature difference of 2°C may be present in class 1 (cooler) in regions located in southern Quebec, but may be present in class 9 (very hot) in northern Quebec regions. Temperature differences are mapped at the Quebec ecumene scale with a 5 km buffer zone (2016 census, 167,764 km2). This mapping was carried out with a predictive machine learning model built from the 30 m temperature band of Landsat-8 provided by the United States Geological Survey (USGS), to predict the difference in relative surface temperature from the other merged multispectral bands (15 m resolution) by deriving several independent variables as well as other geospatial variables such as hydrography and topography. This mapping will serve as a reference for a diachronic analysis (2013-2014 vs. 2020-2022) available from winter 2023. The production of these data was coordinated by the Institut national de Santé Publique du Québec (INSPQ) and carried out by the forest remote sensing laboratory of the Centre for Forestry Education and Research (CERFO), funded under the Government of Quebec's * [2013-2020 Climate Change Action Plan] (https://www.environnement.gouv.qc.ca/changementsclimatiques/plan-action.asp) * entitled Le Québec en action vert 2020.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

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    Authors: Krietemeyer, Andreas; Veldhuis, Marie-Claire ten; van de Giesen, N.C. (Nick);

    RINEX3 (https://files.igs.org/pub/data/format/rinex305.pdf) Hatanaka-compressed (http://sopac.ucsd.edu/hatanaka.shtml) GNSS (Global Navigation Satellite System) data of single-frequency observations obtained by the Ublox Neo M8T receiver (Evaluation Toolkit) and standard Ublox patch antenna. The data is downsampled to 15-second observations spanning from DOY (day of year) 95 in 2017 to DOY 83 in 2020. The unit was placed on the rooftop of the observatory of the NMi building in Delft (approx. coordinate 51.986166, 4.387678). The receiver was connected to a constant power supply. The antenna was placed on the flat rooftop (no significant obstruction in the sky) with a 10cm circular glound plane underneath.

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    4TU.ResearchData
    Dataset . 2022
    License: CC BY
    Data sources: 4TU.ResearchData
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      4TU.ResearchData
      Dataset . 2022
      License: CC BY
      Data sources: 4TU.ResearchData
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    Authors: ETAPAS Project;

    On the 12th of October 2022 the 4th ETAPAS dissemination event "Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations" took place online and on site at the Centre for Research & Technology Hellas (CERTH). If you missed the workshop you can find here the recording of the event, where we presented the first outputs generated by the ETAPAS Project to Greek Public Administration in order display concrete results on how AI can be ethically embedded in their activities. Among the topics we discussed: the ETAPAS Project and key tools developed for the good governance of AI; the chatbot Kari and the challenges using AI-based solutions presents; the development of a misinformation detection platform by CERTH; popAI and TOKEN projects; strategies for a governance framework for artificial intelligence in public administration. Check this recording to see all the interesting presentations and discussions that emerged during the project.

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    ZENODO
    Audiovisual . 2022
    License: CC BY
    Data sources: Datacite
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    ZENODO
    Audiovisual . 2022
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      ZENODO
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/

    Gängige Formen von Diskriminierung sowie die Reproduktion normativer Stereotype sind auch bei künstlicher Intelligenz an der Tagesordnung. Die Beitragenden erläutern Möglichkeiten der Reduktion dieser fehlerhaften Verfahrensweisen und verhandeln die ambivalente Beziehung zwischen Queerness und KI aus einer interdisziplinären Perspektive. Parallel dazu geben sie einem queer-feministischen Wissensverständnis Raum, das sich stets als partikular, vieldeutig und unvollständig versteht. Damit eröffnen sie Möglichkeiten des Umgangs mit KI, die reduktive Kategorisierungen überschreiten können.

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    Authors: Hung, Jason;

    Here researchers have the disposition to engage in the scholarly discourse on how the pandemic adversely influenced individuals' mental health and what remedies should be exercised in response to the mental health challenges. There is a shortage of scholarly discussion about who benefitted from the occurrence of the pandemic. Mancini et al. argued that the pandemic benefitted the social and mental health functioning of a subset of the population, despite the pandemic causing considerable risks of harm to mental health. In this perspective, the author summarizes relevant findings and arguments to present which subsets of the population benefitted at school, at home, and in the workplace during the pandemic. Although COVID-19 is no longer deemed a pandemic, many by-products of the public health crisis, including the encouragement of remote work and studies, remain. In this perspective, by understanding who benefitted from the pandemic and why, the author can evaluate if any public policies formed in response to the pandemic should be kept in the long run in order to maximize individuals' mental health.

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    Apollo
    Other ORP type . 2022
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      Apollo
      Other ORP type . 2022
      Data sources: Apollo
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    Authors: Koptev, Mikhail;

    These data files containg code sources for dataset creation & model learning (neural-jsdf.zip) and collected synthetic dataset of free & collided postures for robotic arm Franka (sdf_3m_full_mesh.mat). Follow the Readme.MD files to launch the code if needed. Corresponding Git repo: https://github.com/epfl-lasa/Neural-JSDF

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    ZENODO
    Dataset . 2022
    License: CC BY
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    ZENODO
    Dataset . 2022
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    Data sources: Datacite
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      ZENODO
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      ZENODO
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    Authors: Bennetto, Laura;

    This video shows the highlights of our Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022. Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/ This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1 Music Licensing details: ASLC-1E1DFFF8-153AD9FEF0 The music is from Adobe Music, is called GUITAR-EPIC-TRAILER-VOCAL_AdobeStock_511629251 YouTube Link: https://youtu.be/OfRa31Wvix8

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    ZENODO
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    ZENODO
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      ZENODO
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      ZENODO
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    Authors: Raigonda, Megha;

    Potato (Solanum tuberosum) is the main crop that is vegetatively propagated(cloning). It is the third most important food crop after wheat and rice in terms of human consumption. This crop is affected by various types of diseases like bacterial, fungal, and viral diseases. As, a traditional practice some viral disease are identified by visual symptoms and some by laboratory methods like Enzyme-linked immunosorbent assay (ELISA), real time reverse-transcription- polymerase chain reaction (RT-PCR) etc., the above methods are tedious, computational costs along with time will be high and it is requiring a more labor-intensive and controlled lab structure to carry out the experiment. So, there is an urgent need for developing an automated model which employs machine learning, deep learning methodology which are subset of Artificial Intelligence. This model helps agriculturist/ farmers for early detection of viral disease. and take effective measures for huge yield loss. The dataset shows different types of viral diseases that the potato crop foliar and tubers get effected with namley Mosaic Virus, Potato Leaf Roll Virus(PLRV), on foliar, and on tuber Potato Spindle Tuber Viriod (PSTVD), Potato Virus Y(PVY)-tuber cracking. The dataset of foliar and tuber comprises of Nineteen hundred and seventy two images Mosaic(666), PLRV(527), Healthy leaf(135), PSTVD(85), PVY cracking (559). Each image of size in pixels is 4288*2848 and camera employed is Nikon D90,its configuration details include f-stop-f/5.3, ISO speed -ISO-250, focal length-80mm, contrast -medium, No flash. The dataset is collected from potato fields of University of Agricultural Sciences, Dharwad where 3 acres of land is cultivated with certified seeds from modipuram regional Centre and 1 acre is cultivated with uncertified seeds. The scientist from the research institute shared the image data from Indian Council of Agricultural Research- Central Potato Research Institute (ICAR- CPRI) shimla. The data set is captured in the day light by placing the leaf or tuber on black background. The image data collected has different symptoms like plant affected with mosaic virus has yellow color spread throughout the leaf the leaf can be classified as mild, medium and severe mosaic. The color feature plays a major role in classifying the mosaic viral disease the symptoms include yellow color propagated throughout the leaf. The PLRV symptoms are rolling of leaf towards upward direction the texture of the leaf is little crunchy in nature. The main features here are shape and texture for accurately classifying the disease. In tuber PSTVD where the tubers are elongated that is based on shape features the classifier gives the accurate results. PVY cracking it is one of the strains of potato virus Y where the tubers have cracks on external part which is noninfectious. The cracks generally start at bud and extend lengthwise.

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    Mendeley Data
    Dataset . 2022
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    Mendeley Data
    Dataset . 2022
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