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4,694 results for “data analysis”
Scripts for data analysis and visualization of GOTM-FABM-BFM simulations at the BOUSSOLE site
<p>Model output, data and scripts to reproduce results in Álvarez et al. (2023): Chromophoric dissolved organic matter dynamics revealed through the optimization of a spectrally and vertically resolved model in the NW Mediterranean Sea.</p>
A global analysis of how human infrastructure squeezes sandy coasts - Scripts & Data
<p><strong>Abstract</strong></p> <p>Coastal ecosystems provide vital services, but human disturbance causes massive losses. Remaining ecosystems are squeezed between rising seas and human infrastructure development. While shoreline retreat is intensively studied, coastal congestion through infrastructure remains unquantified. Here we analyse 235,469 transects worldwide to show that infrastructure occurs at a median distance of 392 meter from sandy shorelines. Moreover, we find that 33% of sandy shores harbour less than 100m of infrastructure-free space, and that 23–30% of this space may be lost by 2100 due to rising sea levels. Further analyses show that population density and gross domestic product explain 35–39% of observed squeeze variation, emphasizing the intensifying pressure imposed as countries develop and populations grow. Encouragingly, we find that nature reserves relieve squeezing by 4–7 times. Yet, at presentonly 16%of world’s sandy shores have a protected status. We therefore advocate the incorporation of nature protection into spatial planning policies.</p> <p>==============================================================================================================</p> <p><strong>Methods</strong></p> <p>The analyses rely on the following freely available datasets:</p> <ul> <li>OpenStreetMap - streets: <a href="https://www.openstreetmap.org/#map=7/52.154/5.295">https://www.openstreetmap.org/#map=7/52.154/5.295</a> </li> <li>OpenStreetMap - shoreline: <a href="https://osmdata.openstreetmap.de/data/land-polygons.html">https://osmdata.openstreetmap.de/data/land-polygons.html</a></li> <li>Global Urban Footprint: <a href="https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/">https://www.dlr.de/eoc/en/desktopdefault.aspx/tabid-9628/16557_read-40454/</a></li> <li>World population: <a href="https://data.humdata.org/dataset/worldpop-population-counts-for-world/resource/677d30ab-896e-44e5-9a31-05452bc3124b">https://data.humdata.org/dataset/worldpop-population-counts-for-world/resource/677d30ab-896e-44e5-9a31-05452bc3124b</a></li> <li>GDP per capita: <a href="https://data.worldbank.org/indicator/NY.GDP.PCAP.CD">https://data.worldbank.org/indicator/NY.GDP.PCAP.CD</a></li> <li>Protected areas: <a href="https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA">https://www.protectedplanet.net/en/thematic-areas/wdpa?tab=WDPA</a></li> <li>Projected shoreline change: <a href="https://data.jrc.ec.europa.eu/dataset/18eb5f19-b916-454f-b2f5-88881931587e">https://data.jrc.ec.europa.eu/dataset/18eb5f19-b916-454f-b2f5-88881931587e</a></li> <li>CoastalDEM: <a href="https://www.climatecentral.org/coastaldem-v2.1">https://www.climatecentral.org/coastaldem-v2.1</a></li> </ul> <p>In addition, we requested the sandy shoreline data from:</p> <ul> <li>Luijendijk et al. (2018) "The State of the World's Beaches", Scientific Reports 8: 6641; <a href="https://www.nature.com/articles/s41598-018-24630-6">https://www.nature.com/articles/s41598-018-24630-6</a></li> </ul> <p>The deposited folder contains 4 subfolders based on the separate analyses presented in the paper. In each subfolder you find a Matlab script to run and accompanying datasets to load. A readme file is included, which further explains the scripts and datasets.</p> <p><strong>Source data file</strong></p> <p>In the data file <em>Source_Data.xlsx</em>, each sheet contains the data for one figure or table of the manuscript.</p>
Behavioral Data Cluster Analysis
<p>The data set includes the main behavioral readouts (mean % prepulse inhibition, social preference index, % alternation in the Y-maze, and total distance moved in the open field) used for cluster analyses in the poly(I:C)-based mouse model of maternal immune activation. </p>
Data and codes for the self-consistent analysis of GUVI and SEE observations
<p>This dataset contains the data and Matlab codes that can be used to reproduce all the figures in the following manuscript:</p><p>Resolving some longstanding issues in far ultraviolet remote sensing: A self-consistent analysis of the GUVI and SEE observations on the TIMED mission</p>
DATA for Nano Ranking Analysis: determining NPF event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles
<p>data used for: </p><p>Nano Ranking Analysis: determining NPF event occurrence and intensity based on the concentration spectrum of formed (sub-5 nm) particles</p><p>https://doi.org/10.5194/ar-2023-5</p>
Experimental and theoretical analysis of ultrafast electron diffraction (UED) data for acetylacetone
<p>Here, in two archives with data for ultrafast electron diffraction (UED) study of the acetylacetone.</p><ol><li><a href="https://zenodo.org/api/records/10206479/draft/files/AcAc_UED_theoretical.zip/content">AcAc_UED_theoretical.zip</a> contains the results of theoretical simulations.</li><li><a href="https://zenodo.org/api/records/10206479/draft/files/AcAc_UED_experimental.zip/content">AcAc_UED_experimental.zip</a> contains raw and processed experimental data and scripts for data processing.</li></ol><p>Within each of the ZIP archives, the README files provide information on the actual data content within. </p>
Critical analysis of life cycle inventory datasets for organic crop production systems (Data sets)
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Fig. 2. A–C in How repeatable are scientific studies of Kinorhyncha? An analysis of specimen-based location and deposition data in WoRMS from 1863 to 2020
Fig. 2. A–C. Number of species (A), number of specimens (B), and number of locations (C) per 8-year (1863–1870) and 10-year time interval (1870–2020) between 1863 and 2020; data extracted from WoRMS based on publications from all fields of research on Kinorhyncha. D. Assignment of coloured lines to publication data in A–C. Abbreviations:?, unknown if material of species/ specimens/ material of locations deposited in a collection; known species, previously described species mentioned in publication; museum coll., material of species/ specimens/ material of locations deposited in a museum collection; new species, species described as new in publication; non-type, additional non-type material mentioned in connection with description of a new species; personal coll., material of species/ specimens/ material of locations deposited in a collection without a permanent storage and loan system; type, type material (holo-, syn-, and paratypes).
Fig. 1 in How repeatable are scientific studies of Kinorhyncha? An analysis of specimen-based location and deposition data in WoRMS from 1863 to 2020
Fig. 1. Cumulative number of new species of Kinorhyncha described, number of publications referring to new or known species, and total number of publications per 8-year (1863–1870) and 10-year time interval (1870–2020) between 1863 and 2020; number of publications on known species from all fields of research on Kinorhyncha.
Figure 4 in "How Sensitive are Exports of a Small Open Economy to Prices and Foreign Incomes? Evidence From a Panel Data Analysis for Tunisia"
Figure 4 Principal Cocponent Analysis (PCA) perforced on the environcental factors. V: Vanadium, Cn: Chnomium, Mn: Mancanese, Co: Cobalt, Ni: Nickel, Cu: Coppen, Zn: Zinc, As: Ansenic, Cd: Cadmium, Sn: Tin, Tl: Thallium, Pb: Lead, Li: Lithium, Rb: Rubidium, Sn: Stnontium, Tsol: soil tempenatune, Hsol: soil humiditu, Gna: cnanulometnu, Na+: sodium, MO: oncanic matten.
Figure 1 in "How Sensitive are Exports of a Small Open Economy to Prices and Foreign Incomes? Evidence From a Panel Data Analysis for Tunisia"
Figure 1 Localization of sacplinS laSoon stations. S1: Bizente, S2: El Bchenliua, S3: Old hanbon ob Ghan El Melh, S4: Opposite El Bouchaz, S5: Sidi Ali Mekki, S6: Nonth lacoon ob Tunis, S7: South lacoon ob Tunis, S8: Konba, S9: Tazanka, S10: El Bibane.
Data Analysis for: Coupling Cell Size Regulation and Proliferation Dynamics for C. glutamicum Reveals Cell Division Based on Surface Area
<div>Data and methods of Data Analysis of: Coupling Cell Size Regulation and Proliferation Dynamics of</div> <div>C. glutamicum Reveals Cell Division Based on Surface Area</div> <div> </div> <div>Authors: Cesar Nieto and Zahra Vahdat at University of Delaware (2023)</div> <div>Correspondence: cnieto@udel.edu.</div> <div> </div> <div> </div>
cDNA sequence of E2 gene family in Arabidopsis thaliana and data of statistical analysis
<p>E2 ubiquitin-conjugating enzymes act as a heart role in the ubiquitination process and are responsible for catalysis ubiquitin transfer. Although the function of ubiquitin-protein ligases (E3s) in plant response to diverse abiotic stress by targeting specific substrates has been well studied, the E2s' involvement in environmental responses and their downstream targets are not well understood. Here, we demonstrated that the E2 ubiquitin-conjugating enzyme 18 (UBC18) regulates the stability of FREE1 to modulate iron deficiency stress. UBC18 affects the ubiquitination of FREE1 and promotes its degradation, overexpression of<em> UBC18</em> in plants decreases their sensitivity to iron deficiency by reducing the level of FREE1, and high accumulation of FREE1 in<em> </em>the<em> ubc18</em> mutant resulted in sensitivity to iron deficiency. In addition, we demonstrated the lysine residues K227, K295, K315, and K540 are required for FREE1 ubiquitination and stability regulation, and mutation of these lysines of FREE1 residues resulted in sensitivity to iron starvation in plants. Taken together, our findings reveal a mechanism of UBC18 in response to iron deficiency stress by altering the abundance of FREE1, and further elucidate the role of ubiquitination sites in FREE1 stability regulation and the plant iron deficiency response.</p>
Analysis of two focus groups about data visualization
<p>This repository contains two datasets concerning the analysis of two focus groups about data visualization.</p> <ul> <li><strong>frequency_matrix_v2.xlsx: </strong>this resource contains a frequency analysis matrix of the focus groups. The categories are identified on the horizontal axis, using different colours according to the hierarchy of the categorical system (meta-category, categories, and subcategories) to facilitate the subsequent axial coding of the content.</li> <li><strong>content_matrix_v2.xlsx: </strong>this resource contains the analysis of the content of the two focus groups. Following data reduction, the ideas or units of analysis about each category were grouped and coded. In this way, the content analysis was completed with the coding of the units of analysis about the respective categories using the axial coding procedure.</li> <li><strong>Transcription_experts_ES.pdf: </strong>this document is the original transcription of the focus group discussion with experts in Spanish.</li> <li><strong>Transcription_experts_EN.pdf: </strong>this document is a transcription of the focus group discussion with experts, automatically translated into English by DeepL.</li> <li><strong>Transcription_phd_ES.pdf: </strong>this document is the original transcription of the focus group discussion with Ph.D. students in Spanish.</li> <li><strong>Transcription_phd_EN.pdf: </strong>this document is a transcription of the focus group discussion with Ph.D. students, automatically translated into English by DeepL.</li> </ul>
Data needed to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests"
<p>Data to reproduce analysis from "Frost matters: Incorporating late-spring frost in a dynamic vegetation model regulates regional productivity dynamics in European beech forests".</p> <p>This includes:</p> <ol> <li>Tree ring data (meyer, bdn, principe, dittmar)</li> <li>LPJ-GUESS model output (frost_validation, frost_sensitivity, runs_22012024_revision)</li> <li>Data used for plotting</li> </ol>
Data for Airlines network analysis on an air-rail multimodal system. Journal of Open Aviation Science, 1(2)
<h2>About</h2> <p>This dataset contains all the input data required to generate the analysis and results of the article Delgado, L., Trapote-Barreira, C., Montlaur, A., Bolić, T., & Gurtner, G. (2023). <em>Airlines’ network analysis on an air-rail multimodal system</em>. Journal of Open Aviation Science, 1(2). <a href="https://doi.org/10.59490/joas.2023.7223" rel="nofollow">https://doi.org/10.59490/joas.2023.7223</a></p> <p>The code used is available on GitHub: <a href="https://github.com/UoW-ATM/joas_air_rail_network_analysis">https://github.com/UoW-ATM/joas_air_rail_network_analysis</a></p> <p>The path to the input data can be modified in the scripts provided in the GitHub repository. With the default setting, the input is in a folder called data.</p> <h2>Dataset structure</h2> <ul> <li>data_computed <ul> <li><em>rail_used_emissions.csv</em></li> <li><em>rail_used_emissions_2.csv</em></li> <li><em>routes_emissinos_v2.csv</em></li> </ul> </li> <li>flights_data4 <ul> <li>year=2023 <ul> <li>month=05 <ul> <li><em>1st_week_0523.csv</em></li> </ul> </li> </ul> </li> </ul> </li> <li>renfe <ul> <li>renfe_mid_long <ul> <li><em>agency.txt</em></li> <li><em>calendar.txt</em></li> <li><em>calendar_dates.txt</em></li> <li><em>routes.txt</em></li> <li><em>stops.txt</em></li> <li><em>stop_times.txt</em></li> <li><em>trips.txt</em></li> </ul> </li> </ul> </li> <li><em>aircraftDatabase.csv</em></li> <li><em>airport_static.csv</em></li> <li><em>code_seats.csv</em></li> <li><em>manual_fixed_airports.csv</em></li> <li><em>mat_corrected.csv</em></li> <li><em>type_code_missing.csv</em></li> </ul> <h2>Data description</h2> <h3>data_computed</h3> <p>This folder contains pre-computed values by the authors on rail and air emissions. These are estimated:</p> <ul> <li>For rail using <a href="http://ecopassenger.hafas.de/" rel="nofollow">EcoPassenger</a>.</li> <li>For flights based on the emission model from Montlaur, A., Delgado, L., & Trapote-Barreira, C. (2021). <a href="https://doi.org/10.3390/su131810401" rel="nofollow"><em>Analytical Models for CO<sub>2</sub> Emissions and Travel Time for Short-to-Medium-Haul Flights Considering Available Seats</em></a>. Sustainability 13.18 (2021) and from some specific flights using EUROCONTROL's <a href="https://www.eurocontrol.int/platform/integrated-aircraft-noise-and-emissions-modelling-platform">IMPACT </a>model.</li> </ul> <h3>flights_data4</h3> <p>Information from flights_data4 table from <a href="https://opensky-network.org/data/impala">OpenSky</a>. Please refer to the <a href="https://opensky-network.org/about/terms-of-use">terms of use of OpenSky</a> for the restrictions on the further use of these data.</p> <p>The file <em>1st_week_0523.csv </em>contains the information of the table flights_data4 from OpenSky for the week of the 1st May 2023 (01/05/2023 to 07/05/2003). This was downloaded with the SQL query </p> <p>SELECT * FROM flights_data4 WHERE lastseen >= 1682899200 AND firstseen <= 1683504000;</p> <p>Note that the lastseen and firstseen are in UnixTime and correspond to 2023-05-01 00:00:00 UTC and 2023-05-08 00:00:00 UTC respectively. This ensures capturing all flights landing on 01/05/2023 even if they departed the day before and departing on 07/05/2023 even if landing the day after.</p> <h3>renfe</h3> <p>renfe folder contains the General Transit Feed Specification (GTFS) data from the <a href="https://data.renfe.com/dataset/horarios-de-alta-velocidad-larga-distancia-y-media-distancia">Renfe</a> rail operator with the high-speed, long and medium distances timetables. Note that Renfe provides the data under a <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" rel="noopener">Creative Commons Attribution 4.0</a> license. </p> <h3>Other datasets</h3> <ul> <li><em>aircraftDatabase.csv </em>: Database containing information on aircraft information (type, manufacturer, license, etc.) as a function of their transponder icao24 code. Obtained from <a href="https://opensky-network.org/aircraft-database">OpenSky</a>.</li> <li><em>airport_static.csv</em>: Airport ICAO code, latitude and longitude.</li> <li><em>code_seats.csv</em>: allows each aircraft model to be related to the seats in the cabin. It has been extracted from airline websites and other sources.</li> <li><em>manual_fixed_airports.csv</em>: List of manually modified airports for arrival/departure to fix wrong rotations from OpenSky data. For each airport ICAO code, it provides the one that should be used instead and some information on that airport (e.g., name)</li> <li><em>mat_corrected.csv</em>: identifies the rotation of each aircraft in the week of study. This is especially relevant for fleet analysis as it is necessary to know the start and end airport of each rotation for each day. It has been identified with an ad-hoc algorithm, and some data has been corrected with <a href="https://www.flightradar24.com/">FlightRadar24</a> data support.</li> <li><em>type_code_missing.csv</em>: relates the transponders' icao24 identifiers missing from OpenSky to the aircraft type (complement aircraftDatabase), compiled from <a href="https://www.flightradar24.com/" rel="nofollow">FlightRadar24</a>.</li> </ul>
IHC data and analysis of CASR in Kilfeather et al., 2023
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Extended Data Fig. 2-27 Geographical information of bioinformatic predicted samples based on the analysis of 16S rRNA gene in four PE degrading bacteria
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Data generated for the manuscript: "Increased 'selfness' in the tumor emerges as a possible immune scuplting mechanism: A pan-cancer data analysis of 32 solid tumors in TCGA"
<p>This submission has processed data generated for the manuscript: "Increased 'selfness' in the tumor emerges as a possible immune scuplting mechanism: A pan-cancer data analysis of 32 solid tumors in TCGA".</p> <p>The `data` folder contains processed information for the Human Protein Atlas (HPA) and The Cancer Genome Atlas (TCGA) datasets. The HPA data is found in `data/hpa` and contains gene ranks used to compute the thymus-likeness score (TLS) and files where the TLS has been calculated for the HPA tissues. The TCGA data can be found in `data/proc`, where individual RDS files have been given for each analyzed cohort. These RDS files contain the results of the cutoff scanning procedure mentioned in the manuscript and account for the bulk of the data generated for this study. RDS files can be loaded using the `readRDS` function in an R session. Other information used to create plots is in the `data` folder.</p> <p>The `plots` folder contain the result of plotting the entireity of the cutoff scanning procedure. The manuscript only contains plots for the 28 cohorts where a significant difference in terms of immune difference is observed, and that too, only the final heuristic plots. The `plots/tcga/immuneres` folder has plots at different levels: (i) single cell category/single deconvolution method with permutation tests (ii) single cell category/multiple deconvolution methods and (iii) significant cell categories/representative deconvolution method. Only (iii) is present in the manuscript. Overall, this folder should contain ~2000 plots.</p> <p>The code used to generate this study is deposited at: https://gitlab.com/narencs179/thymus-like-score</p>
Supporting data for "The benefit of in silico predicted spectral libraries in data-independent acquisition data analysis workflows"
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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.