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4,694 results for “data analysis”
Dataset for Twitter Sentiment Analysis on Criminal Data Propagation using Naive Bayes Algorithm
<p>This study presents a dataset tailored for conducting sentiment analysis on Twitter regarding the propagation of criminal data. Leveraging the Naive Bayes algorithm, the dataset aims to facilitate research into public perceptions surrounding the dissemination of criminal data on social media platforms. Through a currated collection of tweets, researchers can explore the nuanced sentiments and attitudes expressed by users in response to this phenomenon.</p>
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
Unveiling Mexico City's Airbnb Market Trends: A Data Analysis Exploration.
<p>The present document focuses on analyzing, exploring and visualizing data from the Airbnb market in Mexico City using tools such as Python. The data used for this work was cleaned and prepared to extract relevant insights and relations between popular locations, occupancy rate, and reviews (classified into positive or negative using BERT) of accommodations and market trends. The results obtained provide an in-depth insight into the Airbnb market in Mexico City and may be valuable for property owners to make an informed decision in their dynamic pricing strategy.</p>
Data and code for "Salomon et al. 2024: Effects of dissolved organic matter on the toxicity of micro- and nanoplastic particles to Daphnia - a meta-analysis."
<div> <p>All data and R code for</p> <p><strong>Salomon S, Grubmüller E, Kropf P, Nickl E, Rühl A, Weigel S, Becker F, Antonio Vital AL, Laforsch C, Schott M, Mair MM. (2024). Effects of dissolved organic matter on the toxicity of micro- and nanoplastic particles to <em>Daphnia</em> - a meta-analysis. <em>Microplastics and Nanoplastics</em>. (<a href="https://doi.org/10.1186/s43591-024-00088-4" target="_blank" rel="noopener">https://doi.org/10.1186/s43591-024-00088-4</a>)</strong></p> <p><em>Abstract</em></p> <p>Effects of micro- and nanoplastic particles (MNP) on organisms have been increasingly reported in recent years, with a large number of studies conducted on water fleas of the genus <em>Daphnia</em>. Most of the available studies used pristine particles that have not been exposed to the environment or to organic substances. In natural environments, however, organic substances like dissolved organic matter (DOM) attach to the MNP, forming an ecocorona on the particles’ surface. How the formation of an ecocorona influences MNP toxicity is still uncertain. While some studies suggest that DOM can mitigate the negative effects of MNP on organisms, other studies did not find such associations. In addition, it is unclear whether the DOM attached to the particles’ surface is attenuating the effects of MNP directly or whether co-exposure with DOM solved in the medium attenuates MNP toxicity indirectly, for instance by increasing Daphnia‘s resilience to stressors in general. To draw more solid conclusions about the direction and size of the mediating effect of DOM on MNP-associated immobilization in <em>Daphnia</em> spp., we synthesized evidence from the published literature and compiled 305 data points from 13 independent studies. The results of our meta-analysis show that the toxic effects of MNP are likely reduced in the presence of certain types of DOM in the exposure media. We found similar mediating effects when MNP were incubated in media containing DOM before the exposure experiments, although to a lesser extent. Future studies designed to disentangle the effects of DOM attached to the MNP from the general effects of DOM in the exposure medium will contribute to a deeper mechanistic understanding of MNP toxicity in nature and enhance the reliability of MNP risk assessment.</p> </div>
Figure 1 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
Figure 1. Map of Micronesia showing jurisdictions included in the FIA program and locations of Micronesia Challenge protected areas as of 2018.
Figure 2 in Forest Status Across Micronesia from an Assessment of Micronesia Challenge Terrestrial Measures and Forest Inventory and Analysis Data
Figure 2. Estimated percentage of all trees by diameter class (in inches) by jurisdiction across Micronesia. FSM: Federated States of Micronesia, RMI: Republic of Marshall Islands, CNMI: Commonwealth of Northern Mariana Islands.
Costs of Peatland Restoration in Scotland: Data underpinning MACC analysis
<p>A dataset containing anonymised records outlining the costs of a selection of<span> </span>peatland restoration projects in Scotland that have been granted funding by NatureScot since 2016, merged with a set of environmental/geographic variables. These data in turn are used for underpinning the marginal abatement curve (MACC) analysis as a basis of a spatial cost prediction model for potential restoration of degraded peatland in Scotland. The observations in the database represent individual restored sites and the total costs are on a per hectare basis. The variables capture location, spatial dimensions, meteorological conditions, peat conditions, land cover, use and designation specific for each site. The information has been sourced from various publicly accessible domains, namely the Met Office, James Hutton Institute, Centre from Ecology and Hydrology, Ordnance Survey, NatureScot and Centre for Environmental Data Analysis. </p> <p><span> </span></p>
Data underlying the paper titled "Integrating multimodal Raman and photoluminescence microscopy with enhanced insights through multivariate analysis"
<p>The folder includes Raman and Photoluminescence surface maps of microsamples from Cultural Heritage materials. The maps were obtained using a multimodal optical microscope that integrates Raman and Photoluminescence optical techniques to perform a raster scanning of microsample surface. </p> <p>Data refer to the publication: https://doi.org/10.1088/2515-7647/ad5773</p> <p> </p>
Data from: Live-cell analysis of IMPDH protein levels during yeast colony growth provides insights into the regulation of GTP synthesis
<p>Here we present real-time, live-cell analysis of accumulation of the Imd2 isoform of IMPDH in <em>Saccharomyces cervisiae</em> yeast cells forming a monolayer colony in a microfluidic device over a 50-hour time course. We observe two distinct phases of increased Imd2 accumulation: a guanine-insensitive phase early in outgrowth and a guanine-sensitive phase later, when cells become crowded. We show that the IMPDH inhibitor mycophenolic acid enhances both phases of increase. Deletion of a transcription attenuator upstream of the mRNA start site that decreases Imd2 mRNA synthesis in the presence of high GTP increases the baseline level of Imd2 protein ten-fold and abolishes guanine-sensitive but not guanine-insensitive induction. Our results suggest that at least two mechanisms of yeast Imd2 regulation exist, the known GTP-dependent attenuation of RNA polymerase II elongation and a GTP concentration-independent pathway that may be controlled by cell growth state.</p>
Data for Robust Algorithms for the analysis of the FFC-NMRD dispersion curves
<p>Data for article </p>
Stationary comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides stationary comparison data which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer.</p>
Stationary comparison data and analysis between a new low-cost meteorological device and the MaRTy device
<p>This dataset provides stationary comparison data which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to the established high-end MaRTy device developed by Arizona State University's Sensable Heatscapes and Digital Environments (SHaDE) lab. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost HC2S3 Rotronic HygroClip2 for air temperature and humidity. However, the main purpose of the analysis was a comparison of the ability to predict Mean Radiant Temperature (MRT) as an essential component of thermal comfort. MRT for the low-cost device was calculated using the RayMan Pro software, while MRT for the MaRTy device is an output calculated directly by the device. </p>
Mobile comparison data and analysis between a new low-cost meteorological device and the Technical University of Dresden Chair of Meteorology's backpack meteorological device
<p>This dataset provides mobile comparison data from Tharandt and Dresden, Germany, which was used to demontrate the suitability of a new low-cost and user-friendly meteorological device for the purpose of thermal comfort mapping. The new device was compared to an established high-end backpack-mounted device from the Dresden University of Technology (TUD) Chair of Meteorology, Germany. The main sensors for comparison were: the low-cost SHT 85 Sensirion sensor vs. the high-cost WXT520 for air temperature and relative humdity and the low-cost SR2AD pyranometer vs. the high-cost SKS 1110 pyranometer. The ability of each device to predict the Universal Thermal Climate Index (UTCI), calculated using the software RayMan Pro, was also compared.</p>
Fig. 2 in Changing Of Wintering Site Or Recovery Provision - An Analysis Of Ringing Data Of Hungarian Lapwings, Vanellus Vanellus
Fig. 2. Recoveries of Lapwings ringed in Hungary between 1974–2005 and recovered in the Mediterranean area (dots) or in the Atlantic region (squares)
Fig. 1 in Changing Of Wintering Site Or Recovery Provision - An Analysis Of Ringing Data Of Hungarian Lapwings, Vanellus Vanellus
Fig. 1. Recoveries of Lapwings ringed in Hungary between 1909–1932 and recovered in the Mediterranean area (dots) or in the Atlantic region (squares)
"Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", scripts and data
<p>This repository contains the data, scripts and results for the paper "Demand-side policies for power generation in response to the energy crisis: A model analysis for Italy", https://doi.org/10.1016/j.esr.2024.101329.</p> <p>Results in the paper are divided into three sections, corresponding to the numbers of the folders inside this dataset. They are described as follows:</p> <p>1 - EU policy impact: What is the impact on the Italian electricity of the european proposal of cutting power demand and shifting it during peak hours on gas consumption, system costs and emissions?</p> <p>2 - Gas cost sensitivity: Which would be Italy’s most convenient power system considering different gas prices?</p> <p>3 - DSM in mitigation: What could be the role of demand side measures in power systems with a high penetration of RES?</p>
Supplementary Simulation Data for "A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch"
<p>Additional simulation data for "A contact-based analysis of local energetic frustration dynamics identifies key residues enabling RfaH fold-switch"</p> <p><strong>Content:</strong></p> <p>'clusters_foldswitch': Contains representative structures in PDB format of the refolding landscape of RfaH using all-atom structure-based models. The QA and QB values indicated in each filename correspond to the fraction of native contacts contain in the representative structure in comparison to the total number of contacts in the structure of the autoinhibited ⍺-folded (A) and active β-folded (B) states of the C-terminal domain of RfaH.</p> <p>'input_one_fs': Contains a trajectory of RfaH refolding from the ⍺-folded to the β-folded state, with each frame contained into a separate PDB file, totalling 400 PDB files. These files can be used with the frustration-based windowing method scripts and Colab notebook made available at https://github.com/pb3lab/RfaH-frustration</p> <p>'input_many_fs': Contains several trajectory of RfaH reversible refolding between the ⍺-folded and β-folded states, with each frame contained into a separate PDB file, totalling 11,999 PDB files. These files can be used with the frustration-based windowing method scripts and Colab notebook made available at https://github.com/pb3lab/RfaH-frustration</p> <p>'output_one_fs': Contains output results from the analysis of local energetic frustration dynamics of the 400 frames contained in 'input_one_fs' using the windowing method available in the Colab notebook at https://github.com/pb3lab/RfaH-frustration.</p> <p>'output_many_fs': Contains output results from the analysis of local energetic frustration dynamics of the 11,999 frames contained in 'input_many_fs' using the windowing method available in the Python script at https://github.com/pb3lab/RfaH-frustration.</p>
Analysis code and data for the morphometrics and kinematics of tube feet
<p>Hydrostatic skeletons, such as an elephant trunk or a squid tentacle, permit the transmission of mechanical work through a soft body. Despite the ubiquity of these structures among animals, we generally do not understand how differences in their morphology affect their mechanical properties. Therefore, the present study used mathematical modeling, morphometrics, and kinematics to understand the transmission of force and displacement in the tube feet of the juvenile six-rayed star <em>Leptasterias</em> <em>sp.</em> An inverse-dynamic analysis revealed that the forces generated by the feet during crawling primarily serve to overcome the submerged weight of the body. This load was disproportionately generated by the feet at more proximal positions along each ray, which were used more frequently for crawling. Due to a combination of mechanical advantage and muscle mass, these proximal feet exhibited a greater capacity for force generation than the distal feet. However, the higher displacement advantage of the more elongated distal feet offer a superior ability to extend the feet into the environment. Therefore, the morphology of tube feet demonstrates a gradient in gearing along each ray that matches their role in behavior.</p>
Fig. 2 in Factors Influencing Weed Species Diversity In Southeastern Part Of Latvia: Analysis Of A Two-Year Weed Survey Data
Fig. 2. Influence of field variables and crop groups on weed density, common and rare species richness in 2013 and 2014. Constrained analysis (RDA) with five constraining variables: Year (2013 or 2014), N_group (N1 0-50, N2 50- 100, N3 100-140, N4>140 kg ha–1 pure nitrogen per hectar), pH and crop group (c.s. – spring cereals, c.w. – winter cereals, S o.s.r. – spring oilseed rape, W o.s.r. – winter oilseed rape, Other – other crops, including maize, grassland, root crops and legumes). The proportion of constrained variation was 33%, the overall analysis and each of the factors were significant (p <0.05), significance tested with permutation tests.
Fig. 1 in Factors Influencing Weed Species Diversity In Southeastern Part Of Latvia: Analysis Of A Two-Year Weed Survey Data
Fig. 1. Generalized linear models (Poisson) of the total species richness against species density in 2014 and 2013. Model coefficients were 2.41 (p <0.0001) in 2014 and 2.40 (p <0.0001) in 2013. Residual deviance / residual d.f. ratio was used to estimate model overdispersion (1.62 in 2014 and 1.35 in 2013).
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.