Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
136
datasets available to search
ShareScore release 0.9.0
Dataset results
136 results for “Analytical data”
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
Synthetic time series data generation for edge analytics
<p>In this research, we create synthetic data with features that are like data from IoT devices. We use an existing air quality dataset that includes temperature and gas sensor measurements. This real-time dataset includes component values for the Air Quality Index (AQI) and ppm concentrations for various polluting gas concentrations. We build a JavaScript Object Notation (JSON) model to capture the distribution of variables and structure of this real dataset to generate the synthetic data. Based on the synthetic dataset and original dataset, we create a comparative predictive model. Analysis of synthetic dataset predictive model shows that it can be successfully used for edge analytics purposes, replacing real-world datasets. There is no significant difference between the real-world dataset compared the synthetic dataset. The generated synthetic data requires no modification to suit the edge computing requirements. The framework can generate correct synthetic datasets based on JSON schema attributes. The accuracy, precision, and recall values for the real and synthetic datasets indicate that the logistic regression model is capable of successfully classifying data</p>
Data, Analytical Code, and Model Outputs From: Restoration Treatments Enhance Tree Growth and Alter Climatic Constraints During Extreme Drought
<p>This archive includes data (forest inventories, tree ring measurements, climate variables), statistical code, model outputs, and a preprint copy of Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases
<p>The following data and scripts are part of the manuscript titled 'A scoping review on bovine tuberculosis highlights the need for novel data streams and analytical approaches to curb zoonotic diseases' which is currently going through the peer-review process and has already been published as a preprint. Please read the README.txt file for information on the files uploaded.</p>
Fetal exposure to the Ukraine famine of 1932-1933 and adult Type 2 Diabetes Mellitus (Public data and analytical code)
<p><strong>Abstract</strong></p> <p>The short-term impact of famines on death and disease is well documented but it is difficult to estimate their potential long-term impact. We used the setting of the man-made Ukrainian Holodomor famine of 1932-1933 to examine the relationship between prenatal famine and adult Type 2 diabetes mellitus (T2DM). This ecological study included 128,225 T2DM cases diagnosed between 2000-2008 among 10,186,016 male and female Ukrainians born between 1930 and 1938. Individuals who were born in the first half-year of 1934, and hence exposed in early gestation to the mid-1933 peak famine period, had a larger than two-fold likelihood of T2DM (OR 2.21; 95% CI 2.00-2.45) compared to unexposed controls. There was a dose-response relationship between severity of famine exposure and adult T2DM risk comparing individuals born in regions with severe, very severe, and extreme famine to births in the no-famine region.</p> <p> </p> <p><strong>Description of the data and analytical code</strong></p> <p>In exploratory analyses we first examined whether the odds for T2DM were elevated for any month of birth in the period January 1930 to December 1938 in any of the four regions of varying famine intensity. This was achieved by comparing, within each region, the T2DM odds for births in any month and year of birth relative to the T2DM odds for births in the same month combining all other years of birth. The analysis served to identify potential relations of famine with specific months and years of birth, controlling for month of birth effects. We observed increased T2DM odds ratios for births between January and June 1934 in famine-exposed oblasts, with smaller increases for births in 1935 and 1936 in these months. Our findings suggested that in multivariate modelling statistical control for month of birth effects could be accomplished by adjusting for the January-June period. Our findings are presented in the data file '01 Odds Ratio for T2DM Over Time' and show the odds ratios (ORs) for Type 2 Diabetes Mellitus (T2DM) comparing the region-specific T2DM odds for each birth year and month relative to births in the same months but combining all other years of birth. The R syntax file '01 Odds of T2DM Over Time Figure' provides the code necessary to reproduce the figure.</p> <p> </p> <p>For confirmatory analyses we employed a Difference-in-Differences approach to quantify associations between prenatal exposure to famine and T2DM, taking into account year of birth, half-year of birth (Jan-Jun vs Jul-Dec), region, and their interactions. This analysis was conducted initially for each gender separately and then for both genders combined, adjusting for We carried out sensitivity analyses to assess potential changes in T2DM odds arising from the use of pre-famine births vs post-famine births as controls. Our findings are presented in the data file '02 Ukraine Famine 1932-33 Main Data'. Information on the number of T2DM cases by gender, region of residence, and year and month of birth 1930-1938 in Ukraine was collected by the national Ukraine Diabetes Register (Komisarenko Institute of Endocrinology and Metabolism, Kyiv) between 2000-2008. The number of births in the same subgroups, representing the populations at risk for T2DM, was estimated by demographic population reconstruction methods as reported in the publication. We classified the birth counts by year of birth, the semi-annual birth period (January-June vs. July-December), region of birth, and gender. The SPSS syntax file titled '02 Ukraine Famine 1932-33 Main Analysis' provides the code to replicate our main findings as presented in the publication.</p> <p> </p> <p>In a separate analysis we visualized by a meta-regression approach the relation between famine intensity at the oblast level in 1933 and the odds for adult T2DM. The data required for the replication of our findings are included in the file '03 Odds Ratio for T2DM and Famine Intensity at Oblast Level'. The R syntax file titled '03 Ukraine Famine 1932-33 Meta-regression' provides details on conducting the meta-regression using the R package ‘metafor’.</p> <p> </p> <p><strong>Funding</strong></p> <p>Ukraine State complex program Diabetes Mellitus, project number 0106U000844 (M.K.). Holodomor Research and Education Consortium in Canada (L.H.L., O.W.). NIDI-NIAS Fellowship of the Royal Netherlands Academy of Sciences (L.H.L.). National Institute of Aging R01 AG028593 (L.H.L.). National Institute of Aging R01 AG06687 (L.H.L.).</p> <p> </p> <p><strong>Sharing/Access information</strong></p> <p>Data sharing and use are unrestricted with acknowledgement of the original publication and listing of the funding sources as per the above. Researchers are encouraged to contact the Principal Investigators (PIs) for consultations on data structure and use as needed (L.H. Lumey, <a href="mailto:lumey@columbia.edu">lumey@columbia.edu</a>; Oleh Wolowyna, <a href="mailto:olehw@aol.com">olehw@aol.com</a>).</p>
Data associated to: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors
<p>Data associated to the manuscript entitled: Analytical Physical Model for Organic Metal-Electrolyte-Semiconductor Capacitors by Larissa Huetter, Adrica Kyndiah and Gabriel Gomila</p>
Data and supplementary files of the publication: "What Goes Around Should Not Move Around: Immobilizing Microplastics as a New Approach for Analytical Ring Trials"
<p>Accompanying materials for the publication: <a href="https://doi.org/10.1021/acs.est.4c09427">https://doi.org/10.1021/acs.est.4c09427</a></p>
RDF version of the data from Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).
<p>This is an RDFied version of the dataset published by Anastasios G. et al. Computational enrichment of physicochemical data for the development of a zeta-potential read-across predictive model with Isalos Analytics Platform. NanoImpact (2021).</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.1016/j.impact.2021.100308">https://doi.org/10.1016/j.impact.2021.100308</a></p> <p>The Original publication authors: Anastasios G. Papadiamantis, Antreas Afantitis, Andreas Tsoumanis, Eugenia Valsami-Jones, Iseult Lynch, Georgia Melagraki</p>
RDF version of the data from Anastasios G. Papadiamantis et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform (2020)
<p>This is an RDFied version of the dataset published in Papadiamantis, A.G. et al. Predicting Cytotoxicity of Metal Oxide Nanoparticles Using Isalos Analytics Platform. <em>Nanomaterials</em> <strong>2020</strong>, <em>10</em>, 2017.</p> <p>The original dataset publication DOI: <a href="https://doi.org/10.3390/nano10102017">https://doi.org/10.3390/nano10102017</a></p> <p>The Original publication authors: Papadiamantis, A.G.; Jänes, J.; Voyiatzis, E.; Sikk, L.; Burk, J.; Burk, P.; Tsoumanis, A.; Ha, M.K.; Yoon, T.H.; Valsami-Jones, E.; Lynch, I.; Melagraki, G.; Tämm, K.; Afantitis, A.</p>
Data, Analytical Code, and Model Outputs From: "Green is the New Black: Outcomes of Post-Fire Tree Planting Across the Interior West, USA"
<p>This archive includes data (locations of tree plantings, one-year survival records, remotely sensed canopy cover change), statistical code, and model outputs from Rodman et al. (2024). For more information on specific information, processing methods, and data formats, see "README.md" or "README.html" files associated with this archive</p>
Accompanying Dataset migr_asyappctzm for Efficient Analytical Queries on Semantic Web Data Cubes
<p>This dataset shows how the Eurostat data cube in the orginal publicatin is modelled in QB4OLAP.</p> <p>This data is based on statistical data about asylum applications to the European Union, provided by Eurostat on</p> <p><a href="http://ec.europa.eu/eurostat/web/products-datasets/-/migr_asyappctzm">http://ec.europa.eu/eurostat/web/products-datasets/-/migr_asyappctzm</a></p> <p>Further data has been integrated from: https://github.com/lorenae/qb4olap/tree/master/examples</p>
Illustrative dataset for the article: Vieira, R., McDonald, S., Araujo-Soares, V., Sniehotta, F., Henderson, R. (2017) "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies"
<p>This dataset is supplementary material of the manuscript "Dynamic modelling of n-of-1 data: Powerful and flexible data analytics applied to individualised studies. McDonald et al. (2016) presents a series of novel n-of-1 studies that intended to explore the relationship between physical activity change during the retirement transition. The file contains the data of one participant. The column names correspond to the following variables:</p> <p>time: duration of follow-up (minutes);<br> minute: time of day (hours and minutes);<br> day_num: day since beginning of follow-up (the first two days were considered as adaptation phase and therefore removed); <br> PAscore: accelerometer raw score; <br> startBout: 1 (a bout of PA was initiated in this minute) or 0 (a bout of PA wasn't <br> initiated in this minute); <br> nPAbouts_day: number of PA bouts per day; <br> nPAbouts_day.l1: number of PA bouts in previous day (lag 1); <br> nPAbouts_day.l2: number of PA bouts two day before (lag 2); <br> nBoutsLast2hours: number of PA bouts in previous 2 hours; <br> retirement: 0 (before retirement) or 1 (after retirement)<br> weekday: 0 (workday) or 1 (weekend)<br> sleepLength: number of hours of sleep last night<br> sleepLength.l1: number of hours of sleep the night before<br> sleepLength.l2: number of hours of sleep two nights before<br> pers: personalised measure of partner's influence (scale 0-1)<br> periodDay: morning, evening or afternoon</p> <p>McDonald, S., Vieira, R., O'Brien, N., White, M., & Sniehotta, F. F. (2016). Does physical activity and sedentary behavior change during the retirement transition? Findings from a series of novel n-of-1 natural experiments. <em>International Journal of Behavioral Medicine, 23</em>, S261-S261.</p> <p> </p>
Dataset for publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction protocol implemented in XMapTools"
<p>Datasets for the publication "Multi-phase quantitative compositional mapping by LA-ICP-MS: analytical approach and data reduction in XMapTools"</p>
Covid Data Analytics: Repositório de Dados Provenientes de Múltiplas Fontes sobre a Pandemia de COVID-19 no Brasil
<p>Uma estratégia para melhor compreender as diversas facetas e possíveis impactos da pandemia de COVID-19 na sociedade consiste na extração de informação e conhecimento a partir de dados provenientes de diversas fontes oficiais e não oficiais.</p> <p>A importância desse tema fomentou a publicação de diversos artigos científicos que investigam aspectos relacionados à pandemia de COVID-19 no Brasil por meio de análises de dados. Alguns trabalhos, por exemplo, fornecem caracterizações e descrições da evolução da doença no país~\cite{ranzani2021characterisation}, considerando, inclusive, a subnotificação de casos pelas agências oficiais. Outros modelam e preveem a evolução da COVID-19, utilizando dados referentes aos primeiros meses da pandemia e empregando diferentes métodos ou mesmo utilizando dados de geolocalização e de dinâmica populacional.</p> <p>Nesse contexto, é importante que, sempre que possível, os dados utilizados para as pesquisas sejam disponibilizados à comunidade científica, seja para fins de replicabilidade dos resultados encontrados, seja para a promoção de novas investigações.</p> <p>Os dados disponibilizados no repositório CDA se referem ao período entre 23 de fevereiro de 2020 e 8 de maio de 2021.<br> Esse repositório agrega 1.508 arquivos, classificados em dois tipos principais: (i) bases de dados e tabelas extraídas das fontes descritas anteriormente; e (ii) artigos, relatórios, mapas e gráficos produzidos pelos integrantes do projeto a partir da análise dos dados coletados</p> <p><strong>Dados de Fontes Externas</strong></p> <p>Estes arquivos representam 8\% do total de arquivos que compõem o repositório e estão distribuídos da seguinte maneira:</p> <ul> <li>Séries temporais com indicadores econômicos das Unidades Federativas do Brasil e da União em formato .csv, com aproximadamente 18.400 registros;</li> <li>7 scripts de tratamento de dados em formato .py.</li> <li>5 arquivos com a contagem do número de tweets e retweets coletados semanalmente utilizando 13 palavras-chave (“corona”, “covid”, “coronavirus”, “covid19”, “quarentena”,“hidroxicloroquina”, “cloroquina”, “confinamento”, “distanciamento social”, “aglomeração”, “aglomerações”, “sars” e “covid-19”) formato .csv </li> <li>3 arquivos do Google Trends no formato .csv com 249 registros contendo 124 termos pré-selecionados que têm relação com a pandemia e o percentual relativo de buscas na web nos níveis regional e nacional. %\ana{de novo, o que tem nestes csvs?}.</li> <li>7 arquivos como dados anonimizados do Instagram no formato .csv com 90.787 hashtags contendo os termos \#demito, \#demitida, \#desempregada, \#desempregado, \#desemprego, \#falido, \#reduçãodejornada.</li> <li><strong>Análises e Relatórios</strong></li> <li>Os arquivos com as análises e relatórios representam 92\% do total de arquivos do repositório. Além de documentos de texto, também foram disponibilizados materiais visuais, como mapas e gráficos, em diversos formatos. Os arquivos estão distribuídos da seguinte maneira.</li> </ul> <p> </p> <ul> <li>23 gráficos comparativos de indicadores sociais e econômicos, análises descritivas dos ocupados em atividades essenciais e não essências por regiões em formato .svg;</li> <li>409 gráficos de novos casos e óbitos (02 a 09 de setembro) em formato .png;</li> <li> 522 mapas e gráficos de linhas e barras acerca dos casos e óbitos acumulados de COVID-19 em todo o país entre as semanas epidemiológicas 9 e 32 de 2020 (23/02/2020 a 08/08/2020) em formato .png;</li> <li>15 arquivos de medidas provisórias em formato .pdf;</li> <li>1 gráfico interativo gerado a partir do cálculo da mortalidade (óbitos acumulados por 100 mil habitantes) no Brasil em formato .html;</li> <li>25 animações mostrando a evolução da letalidade (mortes acumuladas / casos acumulados) em \% em todos estados do Brasil a cada semana epidemiológica da 9ª à 31ª em formato .gif;</li> <li>1 relatório sobre análises das informações disponíveis para coleta na ferramenta Google Trends em formato .pdf;</li> <li>4 relatórios sobre análises das informações disponíveis dos grupos de pesquisa em formato .pdf; </li> </ul> <p><strong>Limitações nas Bases de Dados Disponibilizadas</strong><br> Devido a questões de privacidade, algumas bases de dados, obtidas através da extração de informações das redes sociais online não foram integralmente disponibilizadas no repositório.<br> Nestes casos, disponibilizamos análises extraídas a partir destas bases, realizadas com o propósito de responder algumas das perguntas de pesquisa do projeto. As análises realizadas durante o projeto estão disponíveis em \url{https://covid.dcc.ufmg.br/}.</p> <p>A disponibilização dos dados ocorreu por meio do padrão <em>Open Data Standards</em> e, a partir dele, foram criados e organizados os arquivos, de acordo com o respectivo formato e tipo de informação. Eles foram integrados ao drive do grupo tecnológico por intermédio de um formulário, e utilizaram-se de um script para transformar os dados do formulário em um arquivo XML. Como resultado, pôde-se modelar e preencher o banco de dados a partir do arquivo XML e, por fim, integrá-lo a um <a href="https://covid.dcc.ufmg.br/buscador.php">buscador</a> criado em Wordpress, que fica disponível para download no portal do projeto CDA maiores informações: <a href="https://covid.dcc.ufmg.br/linhas/dados">https://covid.dcc.ufmg.br/linhas/dados/ </a></p>
The Piraeus AIS Dataset for Large-scale Maritime Data Analytics
<p><strong>AIS data collected by the University of Piraeus' AIS receiver</strong></p> <p> </p> <p><strong>Abstract</strong></p> <p>The advent of Big Data and streaming technologies has resulted in a swarm of voluminous, heterogeneous information, especially in the domains of Internet of Things (IoT) and transportation. Focusing on the maritime field, we present a dataset that contains vessel position information transmitted by vessels of different types and collected via the Automatic Identification System (AIS). The AIS dataset comes along with spatially and temporally correlated data about the vessels and the area of interest, including weather information. It covers a time span of over 2.5 years, from May 9<sup>th</sup>, 2017 to December 26<sup>th</sup>, 2019 and provides anonymised vessel positions within the wider area of the port of Piraeus (Greece), one of the busiest ports in Europe and worldwide. The dataset consists of over 244 million AIS records, an average of more than 10,000 records per hour, which makes it an ideal input for large-scale mobility data processing and analytics purposes.</p> <p> </p> <p><strong>Dataset related to the following publication</strong></p> <blockquote> <p>Andreas Tritsarolis, Yannis Kontoulis, Yannis Theodoridis, The Piraeus AIS dataset for large-scale maritime data analytics, Data in Brief, Volume 40, 2022, 107782, ISSN 2352-3409, <a href="https://doi.org/10.1016/j.dib.2021.107782">https://doi.org/10.1016/j.dib.2021.107782</a>.</p> </blockquote> <p> </p> <p><strong>Files Description</strong></p> <ul> </ul> <ul> <li><strong>ais_static</strong>: CSV flat files containing vessels' static information and their corresponding types</li> </ul> <ul> <li><strong>geodata</strong>: ESRI Shapefiles containing several geographic-related data (e.g. harbours, islands, etc.)</li> </ul> <ul> <li><strong>noaa_weather</strong>: ESRI Shapefiles containing weather forecast from GRIB files (as provided by NOAA)</li> </ul> <ul> <li><strong>unipi_ais_dynamic</strong>: CSV flat files containing AIS kinematic information </li> </ul> <ul> <li><strong>unipi_ais_dynamic_synopses</strong>: CSV flat files containing metadata (i.e. synopses) regarding vessels' AIS positions</li> </ul> <p> </p> <p><strong>Privacy Statement</strong></p> <p><strong>For privacy-related queries, please contact the authors</strong></p>
Data, analytical, and plotting codes needed to reproduce meta-analyses on within-season divorce in birds
<p>This data package contains data (effect sizes and other variables) and analytical codes needed to reproduce three meta-analyses on within-season divorce and breeding success in socially monogamous birds (Culina & Brouwer: No evidence of immediate fitness benefits of within-season divorce in monogamous birds. 2022. Biology Letters.) It also contains codes to reproduce Figures from the main text and from the Supplement. Readme file and Licence are provided too.</p>
Dataset: Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Pavel Čeleda, Lydia Kraus.<br> <em>Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training.</em><br> In Springer Education and Information Technologies. 2022.<br> <a href="https://doi.org/10.1007/s10639-022-11093-6">https://doi.org/10.1007/s10639-022-11093-6</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2307.08582">https://arxiv.org/abs/2307.08582</a></p> <ul> </ul> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2022applications, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and \v{C}eleda, Pavel and Kraus, Lydia}, title = {{Applications of Educational Data Mining and Learning Analytics on Data From Cybersecurity Training}}, journal = {Education and Information Technologies}, publisher = {Springer}, volume = {27}, year = {2022}, issn = {1360-2357}, url = {https://doi.org/10.1007/s10639-022-11093-6}, doi = {10.1007/s10639-022-11093-6}, }</code></pre> <p><strong>Attached content</strong></p> <p>The files included in the ZIP archive are:</p> <ul> <li>`All-discovered-papers.bib` -- a BibTeX export of the Mendeley database of all considered papers discovered by the automated search.</li> <li>`Candidate-papers-reviewer1.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the first investigator.</li> <li>`Candidate-papers-reviewer2.bib` -- a BibTeX export of the Mendeley database of the candidate papers suggested by the second investigator.</li> <li>`Selected-papers.bib` -- a BibTeX export of the Mendeley database of the 35 papers selected for the literature review.</li> <li>`Selected-papers.xlsx` -- an Excel spreadsheet with the extracted information about the selected papers.</li> <li>`Selected-papers.csv` -- a CSV equivalent of the Excel spreadsheet.</li> </ul>
Supplementary data for Plutniak, S. 2022. "What makes the identity of a scientific method? A history of the 'Structural and analytical typology' in the growth of evolutionary and digital archaeology in southwestern Europe (1950s–2000s)", Journal of Paleolithic Archaeology, vol. 5, 10.
<p>Supplementary data for Plutniak, S. 2022. “What makes the Identity of a Scientific Method? A History of the ‘Structural and analytical typology’ in the Growth of Evolutionary and Digital Archaeology in Southwestern Europe (1950s–2000s)”, <em>Journal of Paleolithic Archaeology</em>. vol 5, 10. DOI: <a href="https://doi.org/10.1007/s41982-022-00119-7">10.1007/s41982-022-00119-7</a>.</p> <p> </p> <p> </p> <p> </p>
Data for Non-Equilibrium Sensing of Volatile Compounds Using Active and Passive Analyte Delivery
<blockquote> <p>Version 2: Added missing files to <code>sniffing_data.zip</code></p> </blockquote> <p>See GitHub repository for data processing functions and examples: <a href="https://github.com/soerenbrandt/sniffing-sensor">https://github.com/soerenbrandt/sniffing-sensor</a></p> <p><strong>Abstract</strong>:<br>Sensor technologies have allowed us to outperform the human senses of sight, hearing, and touch; however, the development of artificial noses is significantly behind their biological counterparts. This is largely due to the complexity of natural olfaction, as it incorporates complex fluid dynamics within the nasal anatomy together with the response patterns of hundreds to thousands of unique molecular-scale receptors for odor interpretation. We designed a sensing approach to identify volatiles that exploits time-dependent information from a single sensor (here, the reflectance spectra from a mesoporous one-dimensional photonic crystal) by augmenting and accentuating differences in the non-equilibrium mass-transport dynamics of vapors stemming from their distinct physicochemical properties, thus obviating the need for a large sensor array. By training a machine learning algorithm on the sensor output, we clearly identify polar and nonpolar volatile organic compounds, determine the mixing ratios of binary mixtures, and accurately predict the boiling point, flash point, vapor pressure, and viscosity of several volatile liquids within those used for training as well as compounds unknown to the model. We further implement a bioinspired active sniffing approach, in which the fluid dynamics and patterns of analyte delivery are controlled, enabling an additional modality of differentiation and reducing the duration of data collection and analysis to seconds. These results outline a strategy to build accurate and rapid artificial noses for volatile liquids that can provide useful information on chemicals such as their composition and properties, and can be applied in a variety of fields, including disease diagnosis, hazardous waste management, and healthy building monitoring.</p>
Data from: Meta-analytical evidence for frequency-dependent selection across the tree of life
<p>Explaining the maintenance of genetic variation in fitness related traits within populations is a fundamental challenge in ecology and evolutionary biology. Frequency-dependent selection (FDS) is one mechanism that can maintain such variation, especially when selection favours rare variants (negative FDS). However, our general knowledge about the occurrence of FDS, its strength and direction remain fragmented, limiting general inferences about this important evolutionary process. We systematically reviewed the published literature on FDS and assembled a database of 747 effect sizes from 101 studies to analyse the occurrence, strength, and direction of FDS, and the factors that could explain heterogeneity in FDS. Using a meta-analysis, we found that overall, FDS is more commonly negative, although not significantly when accounting for phylogeny. An analysis of absolute values of effect sizes, however, revealed the widespread occurrence of modest FDS. However, negative FDS was only significant in laboratory experiments and non-significant in mesocosms and field-based studies. Moreover, negative FDS was stronger in studies measuring fecundity and involving resource competition over studies using other fitness components or focused on other ecological interactions. Our study unveils key general patterns of FDS and points in future promising research directions that can help us understand a long-standing fundamental problem in evolutionary biology and its consequences for demography and ecological dynamics.</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.