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2,747 results for “artificial”
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
Data and code associated with "The Observed Availability of Data and Code in Earth Science and Artificial Intelligence"
<p>Data and code associated with "The Observed Availability of Data and Code in Earth Science <br>and Artificial Intelligence" by Erin A. Jones, Brandon McClung, Hadi Fawad, and Amy McGovern.</p> <p>Instructions: To reproduce figures, download all associated Python and CSV files and place<br> in a single directory.<br> Run BAMS_plot.py as you would run Python code on your system.</p> <p>Code:<br>BAMS_plot.py: Python code for categorizing data availability statements based on given data<br> documented below and creating figures 1-3. </p> <p> Code was originally developed for Python 3.11.7 and run in the Spyder <br> (version 5.4.3) IDE.<br> <br> Libraries utilized:<br> numpy (version 1.26.4) <br> pandas (version 2.1.4)<br> matplotlib (version 3.8.0)<br> <br> For additional documentation, please see code file.</p> <p>Data:<br>ASDC_AIES.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence for the Earth Systems (AIES)<br>ASDC_AI_in_Geo.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence in Geosciences (AI in Geo.)<br>ASDC_AIJ.csv: CSV file containing relevant availability statement data for Artificial <br> Intelligence (AIJ)<br>ASDC_MWR.csv: CSV file containing relevant availability statement data for Monthly <br> Weather Review (MWR)<br><br></p> <p><br>Data documentation:<br>All CSV files contain the same format of information for each journal. The CSV files above are <br>needed for the BAMS_plot.py code attached.</p> <p>Records were analyzed based on the criteria below.</p> <p> Records:<br> 1) Title of paper<br> The title of the examined journal article.<br> 2) Article DOI (or URL)<br> A link to the examined journal article. For AIES, AI in Geo., MWR, the DOI is <br> generally given. For AIJ, the URL is given.<br> 3) Journal name<br> The name of the journal where the examined article is published. Either a full<br> journal name (e.g., Monthly Weather Review), or the acronym used in the <br> associated paper (e.g., AIES) is used.<br> 4) Year of publication<br> The year the article was posted online/in print.<br> 5) Is there an ASDC?<br> If the article contains an availability statement in any form, "yes" is <br> recorded. Otherwise, "no" is recorded.<br> 6) Justification for non-open data?<br> If an availability statement contains some justification for why data is not <br> openly available, the justification is summarized and recorded as one of the <br> following options: 1) Dataset too large, 2) Licensing/Proprietary, 3) Can be <br> obtained from other entities, 4) Sensitive information, 5) Available at later <br> date. If the statement indicates any data is not openly available and no <br> justification is provided, or if no statement is provided is provided "None" <br> is recorded. If the statement indicates openly available data or no data <br> produced, "N/A" is recorded.<br> 7) All data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access data associated with the article are given and there is no <br> indication that any data is not openly available; "n" is recorded if no means <br> to access data are given or there is some indication that some or all data is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 8) At least some data available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access data associated with the article are given; "n" is <br> recorded if no means to access data are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 9) All code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if means to access code associated with the article are given and there is no <br> indication that any code is not openly available; "n" is recorded if no means <br> to access code are given or there is some indication that some or all code is <br> not openly available. If there is no availability statement or no data is <br> produced, the record is left blank.<br> 10) At least some code available<br> If there is an availability statement and data is produced, "y" is recorded <br> if any means to access code associated with the article are given; "n" is <br> recorded if no means to access code are given. If there is no availability <br> statement or no data is produced, the record is left blank.<br> 11) All data available upon request<br> If there is an availability statement indicating data is produced and no data <br> is openly available, "y" is recorded if any data is available upon request to <br> the authors of the examined journal article (not a request to any other <br> entity); "n" is recorded if no data is available upon request to the authors <br> of the examined journal article. If there is no availability statement, any <br> data is openly available, or no data is produced, the record is left blank.<br> 12) At least some data available upon request<br> If there is an availability statement indicating data is produced and not all <br> data is openly available, "y" is recorded if all data is available upon <br> request to the authors of the examined journal article (not a request to any <br> other entity); "n" is recorded if not all data is available upon request to <br> the authors of the examined journal article. If there is no availability <br> statement, all data is openly available, or no data is produced, the record<br> is left blank.<br> 13) no data produced<br> If there is an availability statement that indicates that no data was<br> produced for the examined journal article, "y" is recorded. Otherwise, the<br> record is left blank.<br> 14) links work<br> If the availability statement contains one or more links to a data or code <br> repository, "y" is recorded if all links work; "n" is recorded if one or more <br> links do not work. If there is no availability statement or the statement <br> does not contain any links to a data or code repository, the record is left <br> blank. </p>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</p>
Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest
<p>Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest".</p> <p>The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic).</p> <p>Abstract of the paper:</p> <p>A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels.</p> <p>The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare.</p> <p>We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.</p>
LARD: Large-scale Artificial Disfluency Generation
<p>This dataset contains 95,992 examples of utterances with 71,994 artificial inserted disfluencies using the LARD method. We use the <a href="https://arxiv.org/pdf/1801.04871.pdf">Schema-Guided Dialogue (SGD) </a>dataset as a base to construct the synthetic disfluencies. The LARD dataset contains three different types of disfluencies: repetitions, replacements, and restarts. </p>
A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)
<p><strong>A controlled vocabulary for research and innovation in the field of Artificial Intelligence (AI)</strong></p> <p>This controlled vocabulary of keywords related to the field of Artificial Intelligence (AI) was built by SIRIS Academic in collaboration with ART-ER (the R&I and sustainable development in-house agency of the Emilia-Romagna region in Italy) and the Generalitat de Catalunya (the regional government of Catalonia, Spain), in order to identify AI research, development and innovation activities. The work was carried out by consulting domain experts' advice and it was ultimately applied to inform regional strategies on AI and research and innovation policy.</p> <p>The aim of this vocabulary is to enable one to retrieve texts (e.g. R&D projects and scientific publications) featuring the concepts included in the present vocabulary in their titles and abstracts, assuming that these records have a certain contribution of applications, techniques and issues, in the domain of AI.</p> <p>The present effort was carried out because, despite the high number of contributions and technological developments in the field of AI, there is no closed or static vocabulary of concepts that allows to unequivocally define the boundaries of what should be considered “an Artificial Intelligence intellectual product” (or what should not). Indeed, the literature presents different definitions of the domain, with visions that could be contradictory. AI encompasses today a wide variety of subdomains, ranging from general purpose areas such as learning and perception to more specific ones such as autonomous vehicle driving, theorem proving, or industrial process monitoring. AI synthesises and automates intellectual tasks, and is therefore potentially relevant to any area of human intellectual activity. In this sense, it is a genuinely universal and multidisciplinary field. AI draws upon disciplines as diverse as cybernetics, mathematics, philosophy, sociology and economics.</p> <p>As a ground for the construction of the AI controlled vocabulary, an initial set of concepts was taken from different subdomains of the <em>ACM Computing Classification System 2012, </em> to define the boundaries of the AI domain. Notably, although some relevant AI subdomains have an independent category in the ACM taxonomy outside of AI, they have been included in the list of subdomains. In order to align the ACM taxonomical definition with the Catalan Strategy of AI, <em>CATALONIA.AI</em>, in <em>version 1 </em>of this resource the emerging area of AI Ethics was included in the vocabulary, while some other categories which are not relevant for the objectives were removed from the subdomains list. In the current <em>version 2</em>, the classification and the labels of the subdomains have been revised because of the evolution of the field. Some fields have been grouped in order to reduce the overlap between subdomains and to provide a taxonomy that makes more sense for the analysis of R&I ecosystems. </p> <p>The different subdomains in the versions are presented in the following table:</p> <table> <tbody> <tr> <td><strong>Version </strong></td> <td><strong>Subdomains</strong></td> </tr> <tr> <td> <p><em>Version 2</em></p> </td> <td> <p>(1) Machine learning and deep learning; (2) Computer Vision; (3) Natural Language Processing and speech recognition; (4) Intelligent agents, planning, scheduling, problem-solving, control methods, and search; (5) Expert Systems, Knowledge representation and reasoning; (6) AI Ethics.</p> </td> </tr> <tr> <td><em>Version 1</em></td> <td>(1) General, (2) Machine Learning, (3) Computer Vision, (4) Natural Language Processing, (5) Knowledge Representation and Reasoning, (6) Distributed Artificial Intelligence, (7) Expert Systems, Problem-Solving, Control Methods and Search and (8) AI Ethics.</td> </tr> </tbody> </table> <p>Although a keyword rule-based approach suffers from the major shortcomings of not capturing all the lexical and linguistic variants of specific concepts nor the context of the words - namely, keyword-based approaches would miss relevant texts if the specific pattern is not matched during the search - the present vocabulary allowed us to obtain fairly good results, due to the specificity of the concepts describing the AI domain. Furthermore, an understandable and transparent controlled vocabulary allows a better control of the final results and the final definition of the domain borders. Also, a plain list of terms allows a much easier and interactive engagement of interested stakeholders with different degrees of knowledge (such as, for instance, domain experts, policy-makers and potential users) who can make use of vocabulary to retrieve pertinent literature or to enrich the resource itself.</p> <p>The vocabulary has been built taking advantage of advanced language models and resources from knowledge datasets such as arXiv, DBpedia and Wikipedia. The resulting vocabulary comprises 833 keywords, and has been validated by experts from several universities in Emilia-Romagna and Catalonia.</p> <p>The <em>version 0.5</em> of this resource was developed by the SIRIS Academic in 2019 in collaboration with ART-ER, Emilia-Romagna (Quinquillá et <em>al.</em>, 2020), the <em>version 1 </em>was the result of an update done in 2020 in collaboration with the Generalitat de Catalunya, and the current version (<em>version 2</em>) has resulted in 2021 from the collaboration with ART-ER and the integration of an additional set of keywords provided by the <em>Artificial Intelligence and Intelligence Systems (AIIS)</em> Laboratory of the CINI (<em>Consorzio interuniversitario nazionale per l’informatica </em>based in Rome, Italy).</p> <p>The methodology for the construction of the controlled vocabulary is presented in the following steps:</p> <ol> <li> <p>An initial set of scientific publications was collected by retrieving the following records as a weakly-supervised (in the sense that records are linked to AI by their taxonomy and not by a manual label) dataset in the domain of Artificial Intelligence :</p> <ol> <li> <p>Publications from Scopus with the keyword “Artificial Intelligence”</p> </li> <li> <p>Publications from arXiv in the category “Artificial Intelligence”</p> </li> <li> <p>Publications in relevant journals in the scientific domain of “Artificial Intelligence”</p> </li> </ol> </li> <li> <p>An automated algorithm was used to retrieve, from the APIs of DBpedia, a series of terms that have some categorical relationships (i.e. those that are indexed as “sub-categories of”, “equivalent to”, among other relations in DBpedia) with the Artificial Intelligence concept and with the AI categories in the ACM taxonomy. The DBpedia tree has been exploited down to the level 3, and the relevant categories have been manually selected (for instance: <em>Classification algorithms</em>,<em> Machine learning</em> or <em>Evolutionary computation</em>) and others were ignored (for instance: <em>Artificial intelligence in fiction</em>, <em>Robots</em> or <em>History of artificial intelligence</em>) because they were not relevant, or not specifically in the domain.</p> </li> <li> <p>The keywords in publications in the dataset were extracted from the keyword sections and from the abstracts. The keywords with a higher <em>TF-IDF</em>, using an <em>IDF</em> matrix in the open domain, have been selected. The co-occurrence of keywords with categories in specific AI subdomain and a clusterization of the main keywords has been used for a categorization of the keywords at the thematic level.</p> </li> <li> <p>This list of keywords tagged by thematic category has been manually revised, removing the non-pertinent keywords and changing the wrong categorizations by fields.</p> </li> <li> <p>The weak-supervised dataset in the domain of Artificial Intelligence is used to train a Word2Vec (Mikolov <em>et al.</em>, 2013) word embedding model (a machine learning model based on neural networks).</p> </li> <li> <p>The terms’ list is then enriched by means of automatic methods, which are run in parallel: </p> <ol> <li> <p>The trained Word2Vec model is used to select, among the indexed keywords of the reference corpus, all terms “semantically close” to the initial set of words. This step is carried out to select terms that might not appear in the texts themselves, but that were deemed pertinent to label the textual records.</p> </li> <li> <p>Further, terms that are mentioned in the texts of the reference corpus and that are valued by the trained Word2Vec model as “semantically close” to the initial set of words are also retained. This step is performed to include in the controlled vocabulary a series of terms that are related to the focus of the SDGs and which are used by practitioners.</p> </li> </ol> </li> <li> <p>The final list produced by steps 2-6 is manually revised.</p> </li> </ol> <p> </p> <p>The definition of the vocabulary does not, per se, allow to identify STI contributions to AI: this activity in fact boils down to actually matching the terms in the controlled vocabulary to the content of the gathered STI textual records. To successfully carry out this task, a series of pattern matching rules must be defined to capture possible variants of the same concept, such as permutations of words within the concept and/or the presence of null words to be skipped. For this reason, we have carefully crafted matching rules that take into account permutations of words and that allow words within concept to be within a certain distance. Some relatively ambiguous keywords (which may match unwanted pieces of text), have a set of associated “extra” terms. These “extra” terms are defined as further terms that must co-appear, in the same sentence, together with their associated ambiguous keywords.</p> <p>Finally, each keyword in the vocabulary was assigned one or more AI subdomains, so that the vocabulary can also be used to tag collections of texts within narrower AI sub-domains. In order to complement the alignment between keywords and subdomains, a set of subdomain-specific keywords have been defined to better capture the scope of the subdomains. These allow better characterization of subdomains that are more difficult to define only by means of unambiguous specific concepts, or that overlap with the wide “machine learning” subdomain (example: machine learning applied to object recognition or text translation). The alignment between keywords and subdomains, and these keyword lists of each subdomain, have been applied to capture AI subdomains in research outputs. Through this classification process, we have identified projects and publications related to AI, with a focus on mapping the research competencies in the AI domain in Emilia-Romagna. The resulting research records have been reviewed by experts in the domain, given the occurrence of some false positives, which have been used to improve the approach.</p> <p>The final controlled vocabulary has been evaluated with an external test set, proposed by (Dunham <em>et al.,</em> 2020). The test set consists of the abstract of 10,606 papers published in the arXiv repository, of which 1,076 within the Artificial Intelligence subcategories and 9,530 in arXiv categories other than Artificial Intelligence. Evaluating the controlled vocabulary on this data set, we observe accuracy of .94. However, because the pertinence of these publications to the field of AI is based solely on their taxonomic classification (i.e., on whether they are classified in the arXiv within Artificial Intelligence and not on a manual labelling), this evaluation can only yield an orientative performance assessment.</p> <p>The version 2 includes new keywords extracted from the (1) re-training of the enrichment pipeline (steps 5-6 in the methodology) considering as initial set of terms the version 1 of the vocabulary on a reference corpus of new publications, and (2) from the flat keywords list provided by the <em>Artificial Intelligence and Intelligence Systems</em> <em>(AIIS)</em> Lab of CINI (Consorzio interuniversitario nazionale per l’informatica). The keywords in (2) have been cleaned by calculating precision and f-measure on the dataset (Dunham et al., 2020), selecting those keywords with the highest scores, and being manually validated a posteriori.</p> <p>The AI controlled vocabulary has been applied in two practical cases, which have the purpose of identifying skills, stakeholders and capabilities, of a specific research ecosystem at the regional level. See the following references:</p> <ul> <li> <p>Quinquillá, Arnau, Duran-Silva, Nicolau, Massucci, Francesco Alessandro, Fuster, Enric, Rondelli, Bernardo, Bologni, Leda, … Moretti, Giorgio. (2020). Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna. Zenodo. <a href="http://doi.org/10.5281/zenodo.3606342">http://doi.org/10.5281/zenodo.3606342</a>. Poster presented at: World Open Innovation Conference 2019 (WOIC); 11th december 2019, Rome, Italy.</p> </li> <li> <p>Bigas, E., Duran, N., Fuster, E., Parra, C., Fernández, T. (2021): “Anàlisi de l’especialització en intel·ligència artificial”. Col·lecció Monitoratge de la RIS3CAT, Generalitat de Catalunya <a href="http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf">http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf</a></p> </li> </ul> <p> </p> <p><strong>Acknowledgements</strong></p> <ul> <li> <p>Tatiana Fernández (Direcció General de Promoció Econòmica, Competència i Regulació, de la Generalitat de Catalunya), </p> </li> <li> <p>Daniel Marco, Daniel Santanach and Eduard Balbuena (Departament de Polítiques Digitals i Administració Pública, de la Generalitat de Catalunya) </p> </li> <li> <p>Albert Sabater (Observatori d’Ètica en Intel·ligència Artificial i Universitat de Girona)</p> </li> <li> <p>Leda Bologni, Lucia Mazzoni and Giorgio Moretti (Art-ER)</p> </li> <li> <p>Prof. RIta Cucchiara and Dr. Lorenzo Baraldi (Università degli Studi di Modena e Reggio Emilia)</p> </li> <li> <p>Artificial Intelligence and Intelligence Systems (AIIS) Lab of CINI (Consorzio interuniversitario nazionale per l’informatica)</p> </li> </ul> <p> </p> <p><strong>Bibliography</strong></p> <p>Bigas, E., Duran, N., Fuster, E., Parra, C., Fernández, T. (2021): “Anàlisi de l’especialització en intel·ligència artificial”. Col·lecció Monitoratge de la RIS3CAT, Generalitat de Catalunya <a href="http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf">http://catalunya2020.gencat.cat/web/.content/00_catalunya2020/Documents/estrategies/fitxers/analisi-especialitzacio-intelligencia-artificial.pdf</a></p> <p>Dunham, J.W., Melot, J., & Murdick, D. (2020). Identifying the Development and Application of Artificial Intelligence in Scientific Text. ArXiv, abs/2002.07143. Available at: <a href="https://arxiv.org/abs/2002.07143">https://arxiv.org/abs/2002.07143</a></p> <p>Mikolov, Tomas & Corrado, G.s & Chen, Kai & Dean, Jeffrey. (2013). Efficient Estimation of Word Representations in Vector Space. 1-12.</p> <p>Quinquillá, Arnau, Duran-Silva, Nicolau, Massucci, Francesco Alessandro, Fuster, Enric, Rondelli, Bernardo, Bologni, Leda, … Moretti, Giorgio. (2020). Text mining to identify skills, stakeholders and capabilities: the case of Artificial Intelligence in Emilia-Romagna. Zenodo. <a href="http://doi.org/10.5281/zenodo.3606342">http://doi.org/10.5281/zenodo.3606342</a>. Poster presented at: World Open Innovation Conference 2019 (WOIC); 11th december 2019, Rome, Italy.</p>
Classification of Artificial Intelligence and eXplainable Artificial Intelligence publications in Air Traffic Management
<p>v1.0 version used and partially published in "A Survey on Artificial Intelligence (AI) and eXplainable AI in Air Traffic Management: Current Trends and Development with Future Research Trajectory". In this version, it references mainly Transportation Reasearch Part C, ICRAT, Journal of ATM, and ATM Seminar, IEEE transaction on ITS, but not only</p>
Landslide Susceptibility and 3-day Antecedent Rainfall generated by Artificial Neural Networks
<p>[ENGLISH]</p> <p>In this dataset, you can find:</p> <p>- Landslide susceptibility indexes for the Serra Geral geomorphic unit, from 0 (low susceptibility) to 1 (high susceptibility). Files starting in map_susc</p> <p>- 3-day Antecedent Rainfall Thresholds for rainfall-induced landslides in the Serra Geral geomorphic unit. Files starting in map_3day</p> <p>The figure tiles_location.png shows the locations of each tile within the states of Rio Grande do Sul and Santa Catarina, Brazil. Background map: OpenStreetMap contributors (2022)</p> <p>This dataset was produced within the research conducted for the PhD Thesis of Luísa Vieira Lucchese. The link to the Thesis will be added here when it is available. Reference:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. PhD Thesis (Water Resources and Environmental Sanitation) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, 2022.</p> <p> </p> <p>[PORTUGUÊS DO BRASIL]</p> <p>Neste conjunto de dados, você encontra:</p> <p>- Índices de suscetibilidade a deslizamentos de terra para a unidade geomorfológica da Serra Geral, de 0 (baixa suscetibilidade) até 1 (alta suscetibilidade). Os arquivos têm o prefixo map_susc</p> <p>- Precipitação antecedente de 3 dias para a ocorrência de deslizamentos de terra na unidade geomorfológica da Serra Geral. Os arquivos têm o prefixo map_3day</p> <p>A figura tiles_location.png mostra a localização de cada bloco dentro dos estados do Rio Grande do Sul e de Santa Catarina. Mapa de fundo: OpenStreetMap contributors (2022)</p> <p>Este conjunto de dados é produto da Tese de Doutorado de Luísa Vieira Lucchese. O link para a Tese será adicionado aqui, quando estiver disponível. Referência:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. Tese (Doutorado em Recursos Hídricos e Saneamento Ambiental) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande Sul, Porto Alegre, 2022.</p>
MB2017: Artificial spiking neural data with ground truth
<p>This dataset comprises simulated extracellular spiking neural signals, for which the activity of the active neurons is known. These data can thus be used to test spike sorting algorithms.</p> <p>This dataset has been generated for the study reported in:</p> <p>Bernert M, Yvert B (2018) An attention-based spiking neural network for unsupervised spike-sorting. International Journal of Neural Systems, https://doi.org/10.1142/S0129065718500594</p> <p>Please cite this paper as a reference.</p>
On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods - Dataset & Code
<p>This object contains the dataset and python code used for the paper:</p> <p>S. Brenner and R. Sablatnig. On the Use of Artificially Degraded Manuscripts for Quality Assessment of Readability Enhancement Methods. Accepted for OAGM Workshop 2019<strong>, </strong>Steyr, Austria.</p> <p>The dataset is a modified subset of the UCL Multispectral Processed Images of Parchment Damage Dataset (<a href="http://dx.doi.org/10.14324/000.ds.1469099">10.14324/000.ds.1469099</a>). The accompanying code documents how the modified version was created and how the evaluations described in the paper were performed.</p>
Continuous magnetic phase transition in artificial square ice
<p>Open access data set for manuscript "Continuous magnetic phase transition in artificial square ice" published in Physical Review B, <strong>99</strong>, 214430 (2019)</p>
The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf
<p>This dataset contains the measured hourly mean microclimate and surface energy flux data from a field experiment. The experiment consisted of three treatments: unirrigated artificial turf, unirrigated natural turf, and irrigated natural turf (4 mm/day, 13:00-13:23 local time). The experiment was conducted from 2024-01-28 to 2024-03-18 in Burnley, Melbourne, Australia.<br><br>For each treatment, the measured hourly mean data included albedo, soil moisture content, air temperature, vapour pressure of water, wind speed, black globe temperature, mean radiant temperature, universal theraml climate index, wet-bulb globe temperature, soil temperature, turf surface temperature, incoming and outgoing longwave and shortwave radiant fluxes, sensible heat flux, latent heat flux, and ground heat flux. <br><br>Turf surface temperature, and incoming and outgoing longwave and shortwave radiant fluxes were measured at 1.5 m above ground surface.<br>Air temperature and vapour pressure of water were measured at 0.6 and 1.1 m above ground surface.<br>Wind speed, black globe temperature, mean radiant temperature, universal thermal climate index, and wet-bulb globe temperature were measured at 1.1 m above ground surface.<br>Soil moisture content, soil temperature and ground heat flux were measured at 0.1 m below ground surface.<br>Sensible heat flux and latent heat flux were calculated using the Bowen ratio-energy balance method.<br><br>Additionally, the hourly mean background weather conditions (air temperature and cloud amount) from the nearest public climate station in the study period were included in 'ReferenceClimateStation.csv'. Hourly total rainfall data measured at the study site was also included. <br><br>The aims of this study was to:<br>1. Compare the microclimate and human heat stress among the three treatments.<br>2. Assess and compare the human skin burn risks of the three treaments from their turf surface temperatures.<br>3. Analyse the surface energy fluxes of the three treatments to identify the mechanisms by which artificial turf develops any microclimate, human heat stress and turf surface temperature differences.<br><br>This study was published in:</p> <p><span>Cheung, P. K., & Livesley, S. J. (2025). The microclimate, surface energy flux and human skin burn risks of artificial turf as compared to natural turf. <em>Building and Environment</em>, 112679. https://doi.org/10.1016/j.buildenv.2025.112679<br></span><br>Contact person: Dr Paul Cheung (cheung.p@unimelb.edu.au)</p>
Global Artificial Intelligence in Banking Market 2024–2033
<p><a href="https://www.custommarketinsights.com/report/artificial-intelligence-in-banking-market/" target="_blank" rel="noopener">Artificial Intelligence in Banking Market Size</a>, Trends and Insights By Component (Service, Solution), By Application (Fraud Detection and Prevention, Transaction Monitoring, Identity Verification, Customer Service, Virtual Assistants, Automated Customer Support, Risk Management, Credit Scoring, Market Risk Analysis, Personalized Banking, Customer Recommendations, Targeted Marketing, Compliance and Regulatory Reporting, Anti-Money Laundering (AML), Know Your Customer (KYC), Others), By Technology (Machine Learning, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Natural Language Processing (NLP), Text Analysis, Speech Recognition, Chatbots and Virtual Assistants, Robotic Process Automation (RPA), Process Automation, Workflow Automation, Predictive Analytics, Risk Management, Customer Insights), By Enterprise Size (Large Enterprise, SMEs), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2024–2033</p> <p><strong>VC investments in AI by country</strong></p> <table> <tbody> <tr> <td><strong>Country</strong></td> <td><strong>VC Investment</strong></td> </tr> <tr> <td><strong>US</strong></td> <td><strong>54,836</strong></td> </tr> <tr> <td><strong>China</strong></td> <td><strong>18,270</strong></td> </tr> <tr> <td><strong>EU</strong></td> <td><strong>7,921</strong></td> </tr> </tbody> </table> <p>For more details <strong>DOWNLOAD FREE SAMPLE</strong> Now at <a href="https://www.custommarketinsights.com/request-for-free-sample/?reportid=58189" target="_blank" rel="noopener">https://www.custommarketinsights.com/request-for-free-sample/?reportid=58189</a></p>
Artificial reefs geographical location matters more than shape, age and depth for sessile invertebrate colonization in the Gulf of Lion (NorthWestern Mediterranean Sea)
<p>Artificial reefs (ARs) have been used to support fishing activities. Sessile invertebrates are essential components of trophic networks within ARs, supporting fish productivity. However, colonization by sessile invertebrates is possible only after effective larval dispersal from source populations, usually in natural habitat. While most studies focused on short term colonization by pioneer species, we propose to test the relevance of geographic location, shape, age and depth of immersion on the ARs long term colonization by species found in natural stable communities in the Gulf of Lion. We recorded the presence of five sessile invertebrates species, with contrasting life history traits and regional distribution in the natural rocky habitat, on ARs with different shapes deployed during two immersion time periods (1985 and the 2000s) and in two depth ranges (<20m and >20m). At the local level (~5kms), neither shape, depth nor immersion duration differentiated ARs assemblages. At the regional scale (>30kms), colonization patterns differed between species, resulting in diverse assemblages. This study highlights the primacy of geographical positioning over shape, immersion duration and depth in ARs colonization, suggesting it should be accounted for in maritime spatial planning.</p>
Landscape composition drives the impacts of artificial light at night on insectivorous bats
<p>Abstract of the related publication :</p> <p>Among the most prevalent sources of biodiversity declines, Artificial Light At Night (ALAN) is an emerging threat<br> to global biodiversity. Much knowledge has already been gained to reduce impacts. However, the spatial variation<br> of ALAN effects on biodiversity in interaction with landscape composition remains little studied, though it is<br> of the utmost importance to identify lightscapes most in need of action. Several studies have shown that, at local<br> scale, tree cover can intensify positive or negative effects of ALAN on biodiversity, but none have – at landscape<br> scale – studied a wider range of landscape compositions around lit sites. We hypothesized that the magnitude of<br> ALAN effects will depend on landscape composition and species’ tolerance to light. Taking the case of insectivorous<br> bats because of their varying sensitivity to ALAN, we investigated the species-specific activity response to<br> ALAN. Bat activity was recorded along a gradient of light radiance. We ensured a large variability in landscape<br> composition around 253 sampling sites. Among the 13 bat taxa studied, radiance decreased the activity of two<br> groups of the slow-flying gleaner guild (Myotis and Plecotus spp.) and one species of the aerial-hawking guild<br> (Pipistrellus pipistrellus), and increased the activity of two species of the aerial-hawking guild (Pipistrellus kuhlii<br> and Pipistrellus pygmaeus). Among these five effects, the magnitude of four of them was driven by landscape composition.<br> For five other species, ALAN effects were only detectable in particular landscape compositions, making<br> the main effect of radiance undetectable without account for interactions with landscape. Specifically, effects<br> were strongest in non-urban habitats, for both guilds. Results highlight the importance to prioritize ALAN reduction<br> efforts in non-urban habitats, and how important is to account for landscape composition when studying<br> ALAN effects on bats to avoid missing effects.</p>
OpenChart-SE: A corpus of artificial Swedish electronic health records for imagined emergency care patients written by physicians in a crowd-sourcing project
<p>Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>), where additional batches of quality-controlled EHRs will be released periodically. </p> <p> </p> <p><strong>Dataset content</strong></p> <p><em>OpenChart-SE, version 1 corpus (txt files and and dataset.csv)</em></p> <p>The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.</p> <p> </p> <p><em>Codebook.xlsx</em></p> <p>The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.</p> <p> </p> <p><em>suppl_data_1_openchart-se_form.pdf</em></p> <p>OpenChart-SE mock emergency care EHR form.</p> <p> </p> <p><em>suppl_data_3_openchart-se_dataexploration.ipynb</em></p> <p>This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.</p> <p> </p> <p>More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (<a href="https://github.com/Aitslab/openchart-se">https://github.com/Aitslab/openchart-se</a>).</p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Open Pit Extraction, Valea Sesei and Roșia Poieni (Romania)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Open Pit Extraction (mine located at Valea Sesei and Roșia Poieni (Romania)) (3D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring (Underground Extraction, Pyhäsalmi (Finland)).
<p>Demonstration of the GOLDEN Artificial Intelligence (AI) GUI - Artificial Intelligence Platform for mine site monitoring in the Underground Extraction (mine located at Pyhäsalmi (Finland)) (2D view mode).</p> <p>Accessing the GOLDENAI GUI, please refer to the following link (<strong>login required</strong>): <a href="https://next-gui.goldenai.opt-net.eu/ ">https://next-gui.goldenai.opt-net.eu/ </a></p>
Sources of Environmental risks from artificial nighttime lighting widespread and increasing across Europe
<p>Sources of the article Environmental risks from artificial nighttime lighting widespread and<br> increasing across Europe. Calibrated ISS images using Sánchez de Miguel, A., Zamorano, J., Aubé, M., Bennie, J., Gallego, J., Ocaña, F., ... & Gaston, K. J. (2021). Colour remote sensing of the impact of artificial light at night (II): Calibration of DSLR-based images from the International Space Station. <em>Remote Sensing of Environment</em>, <em>264</em>, 112611.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.