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5,506 results for “variability”
A Spatially Variable Time Series of Sea Level Change Due to Artificial Water Impoundment
<p>This database contains a series of gravitational, rotational, and deformational (GRD) "fingerprints"—the spatial response of sea level—corresponding to redistribution of water mass because of impoundment of water in artificial reservoirs, as reported in Hawley <em>et al</em>. (2020). Fingerprints for the GRanD database (Lehner <em>et al</em>.; 2011) are for individual years, noted in the file name.</p> <p>Three additional files come from the dataset provided by Zarfl <em>et al</em>. (2015), as described in Hawley <em>et al.</em> (2020). "Const" includes the fingerprint for all reservoirs under construction in their database; "Plan" includes the fingerprint for all reservoirs in the planning phase. "Zarfl" includes the fingerprint for all reservoirs in "Const," with 15 years of seepage, as well as all reservoirs for "Plan" with 5 years of seepage, as described in Hawley <em>et al</em>. (2020).</p> <p>Each fingerprint has 525,825 points, which fill out a global grid of 513 x 1025 [lat x lon] points. Each node in latitude and longitude is evenly spaced. The first point represents the northernmost point at 0 [deg] longitude, and increase first to the east, then to the south.</p>
Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"
<p><strong>Input files for Dispa-SET for the JRC report "Power System Flexibility in a variable climate"</strong></p> <p>Here you can find the input files needed to reproduce the results of the <a href="https://doi.org/10.2760/75312">report</a>:</p> <pre><code>De Felice, M., Busch, S., Kanellopoulos, K., Kavvadias, K. and Hidalgo Gonzalez, I., Power system flexibility in a variable climate, EUR 30184 EN, Publications Office of the European Union, Luxembourg, 2020, ISBN 978-92-76-18183-5 (online), doi:10.2760/75312 (online), JRC120338. </code></pre> <p>The results in the report are generated with the Dispa-SET power system model, available and explained at <a href="https://www.dispaset.eu/">www.dispaset.eu</a>.</p> <p>A description of the data sources with the references can be found into the report.</p> <p><strong>How to use this dataset</strong></p> <p>This dataset can be used as input data for the Dispa-SET model. We refer to the <a href="https://doi.org/10.2760/75312">report</a> and the <a href="https://www.dispaset.eu">official model documentation</a> for information about the data and the model.</p> <p><strong>Description of the dataset</strong></p> <p>The file <code>EnVarClim.yml</code> is a template of the YAML configuration file used by Dispa-SET. To run a specific climate year the <code>XXXX</code> present in some input files must be replaced with the year.</p> <p><strong>Availability factors</strong></p> <p>In the folder <code>AvailabilityFactors</code> there are the availability factors (from 0 to 1) for the power plants and the renewable generation. There is a subfolder for each simulated zone and inside a file for each climate year: from <code>emh_and_cc_availability_1990.csv</code> to <code>emh_and_cc_availability_2015.csv</code>.</p> <p><strong>Cross-border transmission</strong></p> <p>In the folder <code>DayAheadNTC</code> there is the file <code>merged_constant_NTC.csv</code> containing the capacity (in MW).</p> <p><strong>NOTE</strong>: due to an error in the pre-processing code there are some additional lines for the Western Balkans countries ending with a <code>1</code> (e.g. <code>GR -> MK1</code>). Those lines are ignored by the model because are not associated to any simulated zone.</p> <p><strong>Cross-border historical flows</strong></p> <p>In the file <code>CC_L_flows.csv</code> under the folder <code>Flows</code> are contained the hourly flows between the simulated zones and their neighbours (RU, TR, UA).</p> <p><strong>Fuel prices</strong></p> <p>In the folder <code>FuelPrices</code> are contained a set of files containing the hourly prices for the fuels (biomass, coal, lignite, gas, oil) and CO2 emissions. It is worth noting that in spite of their hourly resolution the time-series are constant through the year.</p> <p><strong>Hourly load</strong></p> <p>In the folder <code>Load_RealTime</code> there are hourly load time-series for each zone considering a different climate year. For the Western Balkans countries we use the same time-series for each climate year.</p> <p><strong>Outage factors</strong></p> <p>The files <code>CC_L_outages.csv</code> in the folder <code>OutageFactors</code> contain the outage factor (from 1, full outage, to 0) for the various generation units. Whenever a simulation zone is missing the model assumes the absence of outages.</p> <p><strong>Power plants data</strong></p> <p>In the folder <code>PowerPlants</code> there is a file named <code>CC_L_plants.mip.csv</code> for each simulated zone. The CSV files contain the data <a href="http://www.dispaset.eu/en/latest/data.html#power-plant-data">needed by Dispa-SET</a>.</p> <p><strong>Water storage levels</strong></p> <p>The folder <code>ReservoirLevel</code> contains the storage level (values from 0 to 1 relative to the size of the storage) for all the simulated zones. The levels have been computed for each climate year using a different inflow using the <a href="http://www.dispaset.eu/en/latest/mid_term.html">mid-term scheduler</a> recently implemented in Dispa-SET. For the Western Balkans countries we use the same time-series for each climate year.</p> <p><strong>Hydro-power inflows</strong></p> <p>In the folder <code>ScaledInflows</code> are contained the inflows used for the hydro-power generation. The values in the CSV files describes how much energy is available for hydro-power generation compared to the installed capacity.</p> <p><strong>Linked resources</strong></p> <ul> <li>Model output files:<strong> </strong>https://zenodo.org/record/3778133</li> <li>Source code for the figures: https://github.com/energy-modelling-toolkit/figures-JRC-report-power-system-and-climate-variability</li> </ul>
Data from: "Deep Generative Modeling of Periodic Variable Stars Using Physical Parameters"
<p>This dataset was used for the training of a conditioned Variational Autoencoder that generates physically informed light curves of periodic variable stars. The light curves correspond to data obtained from The Optical Gravitational Lensing Experiment (<a href="https://ui.adsabs.harvard.edu/abs/1992AcA....42..253U/abstract">OGLE</a>), while ancillary information was obtained from the Gaia Data Release 2 (<a href="https://ui.adsabs.harvard.edu/link_gateway/2016A&A...595A...1G/doi:10.1051/0004-6361/201629272">GAIA DR2</a>). This repository contains the preprocessed OGLE light curves and the GAIA measurements corresponding to each cross-matched source. We also provided a subsample of cross-matched sources that were carefully validated following several steps described in the companion article (paper reference).</p> <p>This dataset is realized in tandem with the corresponding <a href="https://github.com/jorgemarpa/PELS-VAE">GitHub</a> and <a href="https://arxiv.org/abs/2005.07773">article</a>.</p> <p> </p> <p> </p>
Experimental data for the motor learning study performed: "Promoting Motor Variability During Robotic Assistance Enhances Motor Learning of Dynamic Tasks"
<p>The dataset contains the kinematic data and the questionnaire responses for a robot-assisted motor learning study performed in the Motor Learning and Neurorehabilitation Laboratory at University of Bern. The details of the study are described in [doi: 10.3389/fnins.2020.600059]. The kinematic data for each participant is stored as a data frame inside a “pickle” (serialized python object) file. The questionnaire responses are stored as a “csv” file. The variables inside the files are explained in “DataframeVariableDescription.rtf”. For questions, please contact oezhan.oezen@artorg.unibe.ch or L.MarchalCrespo@tudelft.nl.</p>
Variability Census of Legacy Catalogs: New Pulsating Variables and Eclipsing Binaries
<div> <p>I present the preliminary results of a comprehensive variability census of the four legacy catalogs: BD (+CD+SD), HD, SAO, and PPM. This dedicated survey project aims to identify bright A-F type variable stars using high-precision space photometry from the Transiting Exoplanet Survey Satellite (TESS ) and Gaia. Phases I through V of this survey, encompassing analyses of 193,940 A-F stars selected from the aforementioned catalogs, have been completed. To date, the project has yielded a substantial number of new variable star discoveries, including: over 14,510 new δ Scuti stars, more than 18,382 new γ Doradus stars, and over 2,354 new eclipsing binary systems, with approximately 360 binaries exhibiting pulsations in the primary components. Furthermore, thousands of rotational variables and dozens of Heartbeat stars and RR Lyrae stars have been identified, demonstrating the power of this systematic approach in revealing the rich diversity of stellar variability. Due to potential blending and contamination in TESS photometry, the binarity of a few pulsating stars or those eclipsing binaries exhibiting superimposed intrinsic pulsations of <em>δ</em> Scuti and <em>γ</em> Doradus types should be rechecked. Follow-up studies of individual systems are necessary to resolve contamination issues and accurately identify the true source of variability.</p> <p> </p> </div> <div> <p>The full list of newly identified variable stars is presented in machine-readable format in the attached file `PPM_AFstars_NewVar_R5.csv', and an atlas of selected representative light curves is provided as a separate PDF.</p> </div> <p> </p> <p><strong>Citation:</strong> Zhou, Ai-Ying: 2023, <em>Res. Notes AAS,</em> Vol.<strong>7, </strong> 210 (I)</p> <p> Zhou, Ai-Ying: 2024, <em>Res. Notes AAS,</em> Vol.<strong>8, </strong> 81 (II)</p> <p> Zhou, Ai-Ying: 2024, <em>Res. Notes AAS,</em> Vol.<strong>8, </strong> 190 (III)</p> <p> Zhou, Ai-Ying: 2025, <em>Res. Notes AAS,</em> Vol.<strong>9, </strong> 56 (IV)</p> <p> Zhou, Ai-Ying: 2025, <em>Res. Notes AAS,</em> Vol.<strong>9, </strong> 1?? (V)</p>
Data files for figures in "Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability" by Bird et al.
<p>The data files for figures in <i>Sensitivity of extreme precipitation to climate change inferred using artificial intelligence shows high spatial variability</i> by Bird, Bodeker and Clem. The files required to create each figure in the paper and in the supplementary material are described in a readme.txt file which is also provided below:</p><p><strong>Figure 1</strong></p><p>The background image was obtained from the 'NaturalEarthFeature' function of the python Cartopy library (Figure1_background.png). The data required to generate the plots shown in Figure 1 are provided in the Figure1.nc file:</p><ul><li>The latitudes and longitudes for the 10,000 training sites are provided in Training_location_latitudes and Training_location_longitudes variables. </li><li>The latitudes and longitudes for the 8 sites used to demonstrate the ability of the CNN to generalise spatially are provided in the Validation_location_latitudes and Validation_location_longitudes variables. </li><li>The block maxima at each of the 8 sites are provided in the Location_1year_block_maxima variable.</li><li>The GEV fits at 0°C are provided in the GEV_fit_at_0.0C variable.</li><li>The GEV fits at 1.5°C are provided in the GEV_fit_at_1.5C variable.</li></ul><p><strong>Figure 2</strong></p><ul><li>The 1-in-100 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named Figure2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-100 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named Figure2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure 3</strong></p><ul><li>The cumulative distribution functions (CDFs) shown in the lower four panels are provided as text files listing the ARI in years and the daily total precipitation depth in mm. These files are named CDF_<lat>_<long>.dat where <lat> is the latitude and <long> is the longitude. Files for each region are zipped into .7z files named Figure3_<region>.7z where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The latitudes and longitudes for the upper panels can be inferred from the file names for each region.</li></ul><p><strong>Figure 4</strong></p><p>The data for each panel are provided in a netCDF file named Figure4_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 5</strong></p><p>The data are provided as text files named Figure5_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the sensitivities at ARIs of 10, 20, 50, 100, and 200 years.</p><p><strong>Figure 6</strong></p><p>The data are provided as text files named Figure6_<region> where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the precipitation depths and the sensitivities at ARIs of 10, 20, 50, 100, and 200 years. </p><p><strong>Figure 7</strong></p><p>The data for each panel are provided in a netCDF file named Figure7_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</p><p><strong>Figure 8</strong></p><p>The data are provided as text files named Figure8_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. Each file lists the global surface temperature anomaly (°C) and the average negative log likelihood.</p><p><strong>Figure S1 and Figure S3</strong></p><p>The data are provided as text files named Figure_S1_and_S3_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes its contents.</p><p><strong>Figure S2</strong></p><ul><li>The 1-in-20 year precipitation fields for values of T'Global from 0.0°C to 2.0°C in 0.1°C increments are provided in netCDF files named FigureS2_<region>.nc where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li><li>The ratios of the 1-in-20 year precipitation depths with respect to the long-term (20 years or more) average of the annual maximum 1-day precipitation are provided in text files named FigureS2_Precipitation_mean_block_max_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'.</li></ul><p><strong>Figure S4</strong></p><ul><li>The block maxima for each site are provided in text files named FigureS4_blockmaxima_siteA.txt and FigureS4_blockmaxima_siteB.txt. A header at the top of each column described the column contents. </li><li>The GEV-derived curves for each site are provided in text files named FigureS4_gevcurves_siteA.txt and FigureS4_gevcurves_siteB.txt. A header at the top of each column described the column contents. </li></ul><p><strong>Figure S5</strong></p><p>There are no data associated with this figure. This figure was made using Microsoft Powerpoint.</p><p><strong>Figure S6</strong></p><p>The data are provided as text files named FigureS6_<region>.txt where <region> is either 'north_america', 'australia', 'europe', or 'new_zealand'. A header at the top of each file describes the files contents.</p>
Variabilities of Dopamine (₯) I: Six Paper Collections in 2024
<ol> <li><a title="The Variabilities of Dopamine (₯) - PART V: MeSH: D005239 & NBO:0000209 (Fear Comes in to Play)" href="https://details-or-fragments.blogspot.com/2024/12/DAfear.html" target="_blank" rel="noopener"><strong>The Variabilities of Dopamine (₯) - PART V: MeSH: D005239 & NBO:0000209 (Fear Comes in to Play)</strong></a>: Do you still remember the 67-year-old dopamine girl in the history of dopamine science this year? She gradually became a spokesperson for happiness in her twenties. However, behind this happiness is actually a little fear. In the early stages of research, scientists used basic tools to explore dopamine's role, focusing on its relevance to psychosis and antipsychotic drugs. Initial claims that dopamine was involved in fear conditioning were dismissed due to the inadequacies of the drugs, tools, and techniques used. However, with the development of science, it turns out that the purple "Fear" in "Inside Out" also has some relationship with the dopamine girl. Let's do some brain teasers together this time! In "Dopamine at Forty," we learned that dopamine (DA) is more than just the "happy molecule" we once thought it was. Thanks to advancements in genetics, chemistry, and other technologies in the 21st century, scientists now understand dopamine and its interactions with neurons (DAN) and receptors much better. <a title="多變多巴胺&mdash;&mdash;第五部:恐懼也來湊一腳" href="https://case.ntu.edu.tw/blog/?p=44919" target="_blank" rel="noopener">CASE Science, Center for the Advancement of Science Education, National Taiwan (Chinese Publication)</a>, 2024-12-26 </li> <li><strong><a title="The Variabilities of Dopamine (₯) - PART IV: MeSH:D011954(Bound Receptors:D1~D5" href="https://details-or-fragments.blogspot.com/2024/12/dar.html" target="_blank" rel="noopener">The Variabilities of Dopamine (₯) - PART IV: MeSH:D011954(Bound Receptors:D1~D5</a>) : </strong>Dopamine is a pretty quirky character. Not only does it act as a neurotransmitter, but it also behaves differently depending on the "dopamine receptor" it binds to. Imagine these receptors as different doorways on the surface of a cell, each one changing how dopamine does its job. So, what's so special about these receptors? Well, think of them like the VIP passes that let dopamine into the cell club. You've probably heard about receptors because of the coronavirus (yep, the COVID-19 villain). The virus uses a special receptor called "ACE2" to sneak into our cells. Using this same idea, you can picture dopamine needing its own special receptors to get things done. In short, dopamine receptors are like the bouncers deciding who gets into the cell party, and without them, dopamine would just be left knocking on the door! <a title="多變多巴胺&mdash;&mdash;第四部:綁定的受體D1~D5" href="https://case.ntu.edu.tw/blog/?p=44815" target="_blank" rel="noopener">CASE Science, Center for the Advancement of Science Education, National Taiwan (Chinese Publication)</a>, 2024-11-14 <strong><br></strong></li> <li><strong><a title="The Variabilities of Dopamine (₯) - PART III: CL:0000700 & SIO:000823 (Curious detective DAN's aging)" href="https://details-or-fragments.blogspot.com/2024/09/curiosity.html" target="_blank" rel="noopener">The Variabilities of Dopamine (₯) - PART III: CL:0000700 & SIO:000823 (Curious detective DAN's aging)</a> : </strong>In the complex drama of the brain, there are many characters, but dopaminergic neurons (DAN) take the lead role. These neurons are always on the lookout for new things and solving puzzles, like a brainy Sherlock Holmes. Dopamine, the neurotransmitter, is their trusty sidekick, helping them stay curious and active. But, like in any good story, there's a twist. Over time, these once-energetic neurons start to lose their zest for new experiences. This decline in curiosity mirrors our own aging. It raises an important question: what happens in the brain to cause this loss? What makes our inner Sherlock Holmes lose interest in the unknown? <a title="多變多巴胺&mdash;&mdash;第三部:好奇偵探DAN的變老" href="https://case.ntu.edu.tw/blog/?p=44568" target="_blank" rel="noopener">CASE Science, Center for the Advancement of Science Education, National Taiwan (Chinese Publication)</a>, 2024-09-06 </li> <li><a title="The Variabilities of Dopamine (₯) - PART II: CL: 0000700" href="https://details-or-fragments.blogspot.com/2024/08/CL0000700.html"><strong>The Variabilities of Dopamine (₯) - PART II: CL: 0000700: </strong></a>What are dopaminergic neurons (DANs), the cells in your brain that help you do your job every day? Let’s give it a try . Let’s use the “Knowledge Manual” - the ontology. Starting from DAN’s ID, CL: 0000700, a few pictures will present the important relationship between DAN and dopamine. Once you have the concept of DAN-related knowledge graph, will it collide with the DAN and dopamine working in your mind to inspire a new cognitive world spark that belongs to you? | <a title="多變多巴胺&mdash;&mdash;第二部:工作細胞DAN " href="https://case.ntu.edu.tw/blog/?p=44411" target="_blank" rel="noopener">CASE Science, Center for the Advancement of Science Education, National Taiwan (Chinese Publication)</a>, 2024-08-04</li> <li><a title="The Variabilities of Dopamine (₯) - PART I:ChEBI:18243" href="https://details-or-fragments.blogspot.com/2024/06/ChEBI18243.html"><strong>The Variabilities of Dopamine (₯) - PART I:ChEBI:18243:</strong> </a>What is the specific image of the charming and changeable dopamine among scientists? What is the family tree of dopamine established by chemists and information scientists? What is so called ontology? Let us try to brief in common words, knowledge ontology (ontology for short) is a basic computing model compiled by scientific experts in a specific field and fed to computers. Today, we try to use the knowledge architecture of these computers to feed human readers in the context of popular science for writing and reading. This is an innovative creative experiment that uses dopamine to open a new chapter in popular science. It’s so exciting, so nervous for me. What are the benefits of learning about dopamine through Ontology? (1) Telling stories by the graphical structure of the basic knowledge summary, let people understand the information quickly and clearly at a glance. (2) Linking to specific knowledge bases, the information can be “tasted” briefly and deeply by human choices. (3) Are you in urgent need of inspiration tools? Why not try the ontological popular science for different creativity? | <a title="多變多巴胺&mdash;&mdash;第一部:ChEBI:18243" href="https://case.ntu.edu.tw/blog/?p=44277" target="_blank" rel="noopener">CASE Science, Center for the Advancement of Science Education, National Taiwan(Chinese Publication)</a>, 2024-06-27 </li> <li><a title="Variabilities of Dopamine (₯) - Prequel" href="https://details-or-fragments.blogspot.com/2024/04/polymorphic-dopamine-prequel.html" target="_blank" rel="noopener"><strong>Variabilities of Dopamine (₯) - Prequel:</strong> </a>Dopamine should be the most well-known neurotransmitter in the human body. After all, who doesn’t like the “happy molecule”? But you know what? Dopamine is not that simple! There are still divergent opinions about the role she plays in the human body, and it is often said that her actions and reactions affect us in unexpected ways. This article uses the theme of anthropomorphic scientific information to observe and understand the development process of dopamine in the history of science and the different characteristics discovered at different stages. It uses the growth process of a girl as a metaphor to observe and understand it as a leading story to understand the complete knowledge structure of dopamine. | <a href="https://case.ntu.edu.tw/blog/?p=44043">CASE Science, Center for the Advancement of Science Education, National Taiwan(Chinese Publication)</a>, 2024-04-24 </li> </ol>
BGC-Argo Satellite matchup to compute variability in the Chl:C ratio of phytoplankton.
<p>This dataset provides matchups between BGC-Argo and MODIS satellites (both atmospheric and ocean color products). This dataset allows users to compare the variability of the Chlorophyll (Chl) to Phytoplankton Carbon ratio from BGC-Argo floats depending on the light in the mixed layer and link to information obtained from satellites about cloud coverage. </p> <p>Quality control previously performed on this dataset and matchup criteria are described in the associated publication.</p> <p>Here are some of the column headers detailed for clarity:</p> <p>Columns 1-25 represent data from the BGC-Argo floats:</p> <ul> <li>ID: Float WMO ID number</li> <li>dt: Datetime in datenum format.</li> <li>biomes: Biomes according to Fay & McKinley, 2014 (with West Mediterranean biome 18 and East Mediterranean biome 19)</li> <li>zenith: Sun zenith angle at time of surfacing.</li> <li>kd_490_Xing: Downwelling diffuse attenuation coefficient at 490nm from Xing et al.,2021 method. </li> <li>kd_PAR_Xing: Downwelling diffuse attenuation coefficient of PAR from Xing et al.,2021 method. </li> <li>chla: Median chlorophyll from fluorescence in the mixed layer (corrected for Non-Photochemical Quenching following Xing et al., 2012)</li> <li>F_indiv: Calibration factor for chla (chlorophyll from fluorescence) according to the method described in Xing et al., 2011. </li> <li>F_median: Median Correction factor (F) for all the floats in a biome</li> <li>F_median_season: Median Correction factor (F) for all the floats in a biome in a given season</li> <li>Chl_cor: Chla from floats corrected using the F_median factor according to Xing et al., 2011. </li> <li>PAR_0_Argo: PAR(0-) right below the surface also from Xing et al., 2021.</li> <li>Z_iso : Depth of the 0.415 mol/quanta/m-2/d-1 isolume. </li> <li>Zeu: Euphotic depth, 1% of surface light.</li> <li>Eg_Argo: Median light level in the mixed layer during a float's profile, bounded by the surface and the MLD (in mol quanta m^-2 h^-1).</li> <li>MLD: Mixed layer depth, determined using the 0.03 density criteria from de Boyer Montégut, et al.,2004.</li> <li>bbp_XXX: Backscattering at a specific wavelength</li> <li>Cphyto: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Graff et al., 2015.</li> <li>Cphyto_B: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Behrenfeld et al., 2005.</li> <li>Cphyto_M: Median Phytoplankton Carbon in the mixed layer computed from Bbp following Martinez-Vincente et al., 2013.</li> <li>ratio_cor: Chl_cor /Cphyto.</li> </ul> <p>Columns 26-45 have products from matchups with ocean-color MODIS files:</p> <ul> <li>sat_dt: Datetime of satellite overpass in datenum format. </li> <li>chlor_a: Satellite chl obtained from NASA's OBPG hybrid algorithm.</li> <li>sat_IPAR: Instantaneous PAR at time of overpass.</li> <li>sat_PAR: MODIS Daily PAR product above the surface.</li> <li>sat_Daily_PARminus: MODIS Daily PAR product propagated right below the surface (0-)</li> <li>sat_Daily_Eg: Daily median light in the mixed layer computed as sat_DailyPAR_minus * exp(-Kd_PAR*MLD/2) ( in mol quanta m^-2 d^-1).</li> </ul> <p>Columns 46-49 have products from matchups with atmospheric MODIS files: </p> <ul> <li>a_lat, a_lon, a_dt: Same as above but for the atmospheric file</li> <li>Confident Cloudy: Number of pixels (Out of 25) with the Confident Cloudy flag. </li> </ul> <p> </p>
WCRP Baseline Variables - MIP Prioritisation raw data
<p>Supplementary material for the publication: Juckes et al. (2024) Baseline Climate Variables for Earth System Modelling, accepted in GMD. Preprint: https://doi.org/10.5194/egusphere-2024-2363. </p> <p>This data summarises the WCRP Baseline Variables list, and includes the raw data from throughout the prioritisation process.</p>
SuperWASP Variable Star Photometry Archive (VeSPA)
<p>This data set contains the metadata for periodic variable stars that have been classified by Citizen Scientists using the <a href="https://www.zooniverse.org/projects/ajnorton/superwasp-variable-stars">SuperWASP Variable Stars Zooniverse project</a>.</p> <p>The data set is in the same format as custom data exports generated via the <a href="https://www.superwasp.org/vespa/">superwasp.org</a> website. It consists of three files:</p> <ul> <li><strong>export.csv</strong>: The main data export in CSV format, containing one row per folded light curve (i.e. multiple rows per source object).</li> <li><strong>fields.yaml</strong>: A YAML-format list of the columns included in the CSV export with an English description of each one.</li> <li><strong>params.yaml</strong>: A YAML-format copy of the search and filtering parameters which were used to generate the export (in this case this is the full data set with no filtering applied). Also includes a data version number which will be incremented with future data releases or changes to the export format.</li> </ul> <p>Photometry data is also available for download in FITS and JSON format, but this is not included here. URLs for the photometry files are included in <strong>export.csv</strong> for ease of downloading.</p> <p><strong>Acknowledgements</strong></p> <p>The SuperWASP project is currently funded and operated by Warwick University and Keele University, and was originally set up by Queen’s University Belfast, the Universities of Keele, St. Andrews and Leicester, the Open University, the Isaac Newton Group, the Instituto de Astrofisica de Canarias, the South African Astronomical Observatory and by STFC.</p> <p>The Zooniverse project on SuperWASP Variable Stars is led by Andrew Norton (The Open University) and builds on work he has done with his former postgraduate students Les Thomas, Stan Payne, Marcus Lohr, Paul Greer, and Heidi Thiemann, and current postgraduate student Adam McMaster.</p> <p>The Zooniverse project on SuperWASP Variable Stars was developed with the help of the ASTERICS Horizon2020 project. ASTERICS is supported by the European Commission Framework Programme Horizon 2020 Research and Innovation action under grant agreement n.653477</p> <p>VeSPA was designed and developed by Adam McMaster as part of his postgraduate work. This work is funded by STFC, DISCnet, and the Open University Space SRA. Server infrastructure was funded by the Open University Space SRA.</p>
Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'
<p><strong>Abstract from '<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>':</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>
Synthetic cryo electron subtomograms containing biomolecular complexes with continuous conformational variability, used for validating TomoFlow method
<p>Two datasets used for validating TomoFlow method, an optical-flow based approach for analyzing continuous conformational variability of biomolecular complexes in cryo electron subtomograms. The TomoFlow method and the methods used to synthesize the two test datasets have been fully described in the following article: "M. Harastani, M. Eltsov, A. Leforestier, S. Jonic, TomoFlow: Analysis of continuous conformational variability of macromolecules in cryogenic subtomograms based on 3D dense optical flow, Journal of Molecular Biology (2021), doi: https://doi.org/10.1016/j.jmb.2021.167381". Additionally, this article describes a test of TomoFlow using one experimental cryo electron tomography dataset (available in EMPIAR and EMDB databases under the accession codes EMPIAR-10679 and EMD-12699). </p>
Attributing decadal climate variability in coastal sea-level trends
<p>The data produced from analysis to be published in Ocean Science Discussions, paper entitled "Attributing decadal climate variability in coastal sea-level trends". NetCDF contains the following sets of fields:</p> <p>1. Indexing: An <em>index</em> and location (<em>lat, lon</em>) of the coastal grid cells, a locator index attributing each cell to Atlantic, Pacific and Indian Ocean basin, a <em>time</em> (decimal year) index.</p> <p>2. NEMO model trends (<em>nemo_<component>_trend</em>): Rolling decadal trends at each coastal grid cell from the NEMO model run for steric, manometric (dynamic) and GRD. The sum of these components gives the equivalent to absolute sea level trend. </p> <p>3. Climate and oceanographic mode indices: The rolling decadal trends in climate indices and the AMOC index calculated from the AMOC model (<em>ci_trend</em>) and their names (<em>ci_index</em>).</p> <p>4. Empirical Orthogonal Function spatial pattern (<em>eof_<basin>_<component>_D</em>) and Principal Component time series (<em>eof_<basin>_<component>_PC</em>)<em> </em>of the NEMO model trends.</p> <p>5. Coefficient of linear regression between PC and climate indices (<em>recon_<basin>_<component>_beta</em>) and the rolling trend time series at each grid cell from the reconstruction, sum{ci_trend*beta} (<em>recon_<basin>_<component>_trend</em>).</p> <p>In 4 and 5, the indices are given by basin. The total coastline is a concatenation of the Atlantic, Pacific and Indian basin data in that order. The absolute SSH is given by the sum of components. i.e. the SSH for all coastal cells in order <em>index</em>:</p> <p>recon_sum_trend([index(Atlantic_index); index(Pacific_index); index(Indian_index)] = ...</p> <p> [recon_Atlantic_manometric_trend+recon_Atlantic_steric_trend+recon_Atlantic_grd_trend; ...</p> <p> recon_Pacific_manometric_trend+recon_Pacific_steric_trend+recon_Pacific_grd_trend; ...</p> <p> recon_Indian_manometric_trend+recon_Indian_steric_trend+recon_Indian_grd_trend]</p>
Relative Humidity from Copernicus Essential Climate Variables for July months from 1980 to 2018
<p>This dataset can be used if you have issues with the Essential Climate Variables Galaxy Tool for the Training "Getting your hands-on climate data" in the section "Essential Climate Variables". You can then upload this dataset in your Galaxy history and skip the 1st step (<strong>Copernicus Essential Climate Variables</strong>) and directly start with 2. <strong>map plot gridded (lat/lon) netCDF data.</strong></p>
Seasonal to decadal western boundary current variability from sustained ocean observations
<p> </p> <p>Cross-transect velocity time series for HR-XBT transects IX21, PX30, and PX40 in support of: <a href="http://doi.org/10.1029/2022GL097834">Chandler et al. (2022). Seasonal to decadal western boundary current variability from sustained ocean observations.</a> </p> <p> </p> <p>Each netcdf file includes the following variables:</p> <ul> <li>time</li> <li>longitude</li> <li>latitude</li> <li>depth</li> <li>vel</li> <li>gvel_LNM</li> <li>long_for_vel_err</li> <li>lat_for_vel_err</li> <li>vel_err</li> <li>wbc_transport</li> </ul> <p> </p> <p>See also <a href="https://github.com/mlchandler/wbc_sustained_obs">https://github.com/mlchandler/wbc_sustained_obs</a></p>
Dayside EMMA-RBSP conjunctions during variable geomagnetic conditions
<p>This data set contains a list of simultaneous observations of electron number density and plasma <br> mass density at the magnetic equator, during variable geomagnetic conditions. </p> <p>The equatorial electron number density is obtained by re-scaling Van Allen Probes (RBSP) observations <br> using a dipole field approximation to describe the portion of the field line from the satellite to the<br> equatorial crossing point and assuming a decay rate of <em>r</em><sup>−1</sup>. Original electron density data set is <br> determined using the Neural-network-based Upper-hybrid Resonance Determination (NURD) algorithm <br> (<a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2015JA022132">Zhelavskaya et al., 2016</a>) and is available at <a href="https://doi.org/10.5880/GFZ.2.8.2020.002">https://doi.org/10.5880/GFZ.2.8.2020.002</a>. </p> <p>The equatorial plasma mass density is inferred from field line resonances estimated by magnetic signal <br> detected at the European quasi-Meridional Magnetometer Array (EMMA). The original geomagnetic field data <br> are available from <a href="https://zenodo.org/record/3387216">https://zenodo.org/record/3387216</a>, and detailed information on the method used to infer<br> the plasma mass density is described by <a href="https://www.annalsofgeophysics.eu/index.php/annals/article/view/7751">Del Corpo et al. (2019)</a>.</p> <p>The analysis is limited to daytime hours and to the periods:</p> <ul> <li>2012/09/22-2012/12/01</li> <li>2013/03/13-2013/03/27</li> <li>2013/05/25-2013/06/11</li> <li>2014/02/14-2014/03/09</li> <li>2015/03/13-2015/03/31</li> <li>2015/06/18-2015/06/27</li> <li>2017/05/26-2017/06/02 </li> </ul> <p>A conjunction between the plasma mass density measure and the electron number density measure is <br> identified when they differ in UT by at most 1 h and refer to positions that differ at most by <br> ∆<em>r</em>/<em>r</em> = 0.05 and ∆MLT = 0.5 h, where <em>r</em> is the geocentric distance, and MLT is the magnetic local time.</p> <p>Besides the densities values and their uncertainties, the data set contains date in universal time, <br> magnetic local time and radial distance of both measures, as well as a label to identify the probe <br> (A or B) and a flag indicating if the conjunction refers to the plasmasphere or the plasmatrough region.</p>
Raw and processed hydro-meteorological variables of Jucar river basin for feature selection
<p>The dataset Processed data – input WQEISS.csv was employed for the input variable selection step in Zaniolo et al., 2018. It includes monthly values of 28 hydro-meteorological variables and indexes of Jucar river basin, Spain, for the period 1986-2000, namely:</p> <ul> <li>2 temporal features: day and month of the year;</li> <li>12 inputs to the Jucar State Index: average monthly storage and groundwater levels, average three months river runoff, and cumulated areal precipitation over 12 months;</li> <li>8 additional observed variables in the basin: three months average outflows from, and inflows to, the main reservoirs, and mean monthly areal temperatures;</li> <li>6 traditional drought indicators: Standardized Precipitation Index (SPI) and Standardized Precipitation and Evaporation Index (SPEI). SPI and SPEI indicators are computed on mean monthly data over the entire basin for 3, 6, and 12 months time aggregations.</li> </ul> <p>The last column of the dataset reports the target variable, i.e., the monthly nominal shortage of water conveyed to the irrigation districts simulated via AQUATOOL model. For further details on the dataset please consult Zaniolo et al., 2018, or the dedicated website <a href="http://www.nrm.deib.polimi.it/?page_id=2438">http://www.nrm.deib.polimi.it/?page_id=2438</a></p> <p>The unprocessed data used to compute indices and temporal cumulations in Processed data – input WQEISS.csv are reported in table Raw Data.csv. Public observations of rainfall, streamflows and storage levels come from the SAIH (Hydrological Automatic Information System) of the CHJ (Jucar Hydrological Confederation). Users can directly download data for the last 12 months on the dedicated webpage <a href="http://saih.chj.es/chj/saih/?f">http://saih.chj.es/chj/saih/?f</a> while previous data records are provided for free by CHJ upon request. Observations from piezometers are downloadable from the Piezometric Network Information section section of the CHJ <a href="https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx">https://www.chj.es/es-es/medioambiente/redescontrol/Paginas/Piezometr%C3%ADa.aspx</a>.</p>
Variability and bias in measurements of metals mass fractions in automobile shredder residue
<p>Measured mass fractions of various metals in individually digested test samples of automobile shredder light fraction (single_digestions_ppm.csv) and the calculated means and standard deviations of these (mean_sd_ppm.csv). For all metadata see accompanying readme file Loevik2019_metal_mass_fractions_in_automobile_SLF_Readme.txt.</p>
GitHub Profiles (users/organisations) and Repositories (research/non-research) of Potsdam Researchers and Research Organisations: An annotated dataset of with howfairis and software quality variables.
<p>This dataset accompanies the paper <em>"Software FAIRness, Documentation and Development Practices in Potsdam Researchers' GitHub Repositories"</em> It includes 3 CSV files that contain data related to github profiles of users/organisations, their repositories annotated as research/non-research repositories and followed by FAIRness and other software qualtiy variables. The data were collected using <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP">SWORDS-template-UP</a> (v1.0.0) methods (collect_users, collect_repositories, collect_variables) which is extended version of <a href="https://github.com/UtrechtUniversity/SWORDS-template">SWORS-template</a> adopted according our needs and detailed in the paper.</p> <p><strong>GitHub (research) user/organisation profiles. ( <em>github_profiles.csv )</em></strong></p> <table> <tbody> <tr> <td><strong>Column name</strong></td> <td><strong>Description </strong></td> </tr> <tr> <td>user_id</td> <td>GitHub username </td> </tr> <tr> <td>html_url </td> <td>URL of the GitHub profile </td> </tr> <tr> <td>type </td> <td>Type of profile (user or organization)</td> </tr> <tr> <td>organisation</td> <td>Acronym or name of the organization </td> </tr> </tbody> </table> <p><strong>GitHub repositories <em>(github_repositories.csv)</em></strong></p> <p>This file contains the repositories scraped from the GitHub profiles of research users and organizations.</p> <table> <tbody> <tr> <td><strong>Column name </strong></td> <td><strong>Description </strong></td> </tr> <tr> <td>html_url </td> <td>URL link to the repository </td> </tr> <tr> <td>description</td> <td>GitHub project description </td> </tr> <tr> <td>project</td> <td>Specifies if the project is research or non-research</td> </tr> <tr> <td>language</td> <td>Programming language used in the project </td> </tr> <tr> <td>organisation</td> <td>Acronym or name of the university, institution, or research organization</td> </tr> <tr> <td>research_group</td> <td>Acronym or name of the research group the repository belongs to</td> </tr> </tbody> </table> <p><strong>Research repositories filtered and annotated <em>(github_research_repositories_filtered_annotated.csv)</em></strong></p> <p>This file contains filtered and annotated information about research repositories.</p> <table> <tbody> <tr> <td><strong>Column Name </strong></td> <td><strong>Description </strong></td> <td><strong>Collection Method </strong></td> </tr> <tr> <td>html_url </td> <td>Repository URL </td> <td> </td> </tr> <tr> <td>howfairis_repository</td> <td>Indicates if the repository is public or private (True/False) </td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_license </td> <td>Indicates if the repository has a license (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_registry</td> <td>Indicates if the repository has implemented community registry (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_citation</td> <td>Indicates if the repository has a .cff file (True/False) </td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>howfairis_checklist</td> <td>Indicates if the repository has implemented OpenSSF best practices badge (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/tree/main/collect_variables#usage">howfairis_variable.py</a>) is a wrapper for <a href="https://pypi.org/project/howfairis/">howfairis</a> pypi library that checks the 5 recommendations of <a href="https://fair-software.nl">FAIR</a></td> </tr> <tr> <td>fair_score</td> <td>Score based on howfairis variables (0-5) </td> <td> </td> </tr> <tr> <td>dlr_soft_class</td> <td>Name of the university, company, research institute, or research organization</td> <td>(Manual) Annotated the repository based on <a href="https://core.ac.uk/reader/211557820">DLR software engineering guideline.</a> There are no specific definitions on metrics how to categorise them (github repositories) into application classes. Which were needed to do a comparitive analysis. </td> </tr> <tr> <td>installation_instruction</td> <td>Presence of installation instruction (True/False) </td> <td>(Manual) Checked the presense of Installation Instruction in the readme or in the project wiki pages. </td> </tr> <tr> <td>project_information </td> <td>Presence of basic project information in README (True/False) </td> <td>(Manual) Checked if the readme have basic information about the project. </td> </tr> <tr> <td>usage_guide</td> <td>Presence of folder named test/tests in the root directory (True/False)</td> <td>(Manual) Checked the presense of Usage Guide in the readme or in the project wiki pages. For command line tools checked if they have help command which guides how to use the tool. </td> </tr> <tr> <td>test_folder</td> <td>Presence of folder named test/tests in the root directory (True/False)</td> <td> <p>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/docs/collect_variables/scripts/soft_dev_pract/test_folder.py">test_folder.py</a>) Checks the folder names test/tests in the root directory of the repository.</p> </td> </tr> <tr> <td>requirements_explicit </td> <td>Explicit requirements for Python, R, C++ repositories (True/False)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/requirement_explicit.py">requirement_explicit.py</a>) Checks the files (requirements.txt, DESCRIPTION, CMakeLists.txt) in the root directory. </td> </tr> <tr> <td>continuous_integration</td> <td>Indicates if the repository uses continuous integration (True/False)</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/continious_integration.py">continious_integration.py</a>) Checks the presence of folder .github (github actions) same for other continious integration (travisCI, CircleCI, Jekins, azure pipeline)</td> </tr> <tr> <td>ci_tool </td> <td>Name of the continuous integration tool used</td> <td>(Script- <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/continious_integration.py">continious_integration.py</a>) Checks the presence of folder .github (github actions) same for other continious integration (travisCI, CircleCI, Jekins, azure pipeline)</td> </tr> <tr> <td>add_lint_rule </td> <td>Indicates if additional linting rules are present (True/False)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/add_ci_rules.py">add_ci_rules.py</a>) - it scans the YAML files in the <br>.github/workflows directory to detect the presence of (linters) Python, R, and C++.</td> </tr> <tr> <td>add_test_rule</td> <td>Indicates if additional testing rules are present (True/False) </td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/add_ci_rules.py">add_ci_rules.py</a>) - it scans the YAML files in the <br>.github/workflows directory to detect the presence of (testing libraries) Python, R, and C++.</td> </tr> <tr> <td>comment_at_start</td> <td>Indicates the level of comments at the start of the program (most, more, some, less)</td> <td>(Script - <a href="https://github.com/Software-Engineering-Group-UP/SWORDS-template-UP/blob/main/collect_variables/scripts/soft_dev_pract/comment_at_start.py">comment_at_start.py</a>) Checks the presence of brief comments at the start at source code files in GitHub repositories.</td> </tr> <tr> <td>language </td> <td>Programming language used in the repository </td> <td> </td> </tr> <tr> <td>type </td> <td>Specifies if the profile is a user or organization </td> <td>Github organisation or user profiles.</td> </tr> <tr> <td>organisation </td> <td>Name of the university, company, research institute, or research organization</td> <td>Oraganisation name (from where the user was found)</td> </tr> <tr> <td>research_group</td> <td>Name or acronym of the research group </td> <td> </td> </tr> </tbody> </table> <p> </p> <p>Data for publication - https://github.com/Software-Engineering-Group-UP/potsdam-research-repos</p>
Data and Analysis for Kaplanis, Denny, and Raimondi 2024, "Vertical distribution of rocky intertidal organisms shifts with sea-level variability on the Northeast Pacific Coast".
<p>This repository contains all the data and R scripts used to produce all analyses and figures for Kaplanis, Denny, and Raimondi 2024, as well as all intermediate outputs and final figures. To access this content, download and unzip the intertidalvertdist folder (for intertidal vertical distribution). The R Project is titled "intertidalvertdist". All pertinent information needed to access data, replicate the analyses, and produce figures is contained within the README file, but a brief desciption is below.</p> <p><br>Directory Architecture:</p> <p>Data:<br>Contains all data. Within this folder are two subdirectories - Raw Data, and Processed Data. Raw Data are unmanipulated, straight from the data source. Processed Data are outputs from scripted data wrangling and transformations. </p> <p>Within each of these folders are two more subdirectories: Tide Gauge Data, and MARINe Data. These are the two data sources used in this manuscript - monthly sea-level data from The National Oceanic and Atmospheric Administration Center for Operational Oceanographic Products and Services (NOAA CO-OPS) tide gauge stations, and long-term rocky intertidal biological monitoring data from Multi-Agency Rocky Intertidal Network (MARINe) survey sites.</p> <p>Scripts:<br>All R scripts are contained within the Scripts folder. The scripts have the prefix IVD (for intertidal vertical distribution), then a name that indicates the major function of the code. The scripts either downloads data, manipulates data, conducts analyses, and/or produces a figure.</p> <p>Outputs:<br>Any figures and tables from preliminary analyses, but that are not used in the final manuscript, are saved in Outputs.</p> <p>Figures:<br>All final figures and tables are contained in the Figures folder. All figures are produced by scripts, except Figs. 1 and 2, which are schematics produced manually in a graphics editor. This folder contains two other folders: Supplemenatary Figures, and Partial Regression Plots. Partial Regression plots are the same as the final Figures 8-12, except they are grouped by taxa rather than by explanatory variable.</p> <p>Data Processing Workflow - Overview: <br>Tide Gauge Data (Data/Raw Data/Tide Gauge Data/individual stations) were downloaded using the NOAA Co-Ops API URL Builder (https://tidesandcurrents.noaa.gov/api-helper/url-generator.html), merged, then analyzed. Three MARINe data sets from the Coastal Biodiversity Survey (CBS) were accessed via data requests (https://marine.ucsc.edu/explore-the-data/contact/data-request-form.html). The first MARINe dataset (Data/Raw Data/MARINe Data/CBS_Percent Cover Data, both First Sample and Full Sample) was used to determine the top ten most abundant taxa (hereafter termed “dominant taxa”) across CBS survey sites during the monitoring period of 2001-01-01 to 2021-09-30. The second MARINe dataset (Data/Raw Data/MARINe Data/CBS_Elevation Data) was used to describe the upper limits of vertical distribution of dominant taxa through time. The third MARINe dataset (Data/Raw Data/MARINe Data/CBS_Presence Data) was used to visualize latitudinal distribution of taxa.</p> <p>Location information for Tide Gauge Stations and CBS Survey Sites were assembled into a table (Data/Raw Data/CBS_Tide Gauge_Data.csv)</p> <p>Tide Gauge Data were processed first, then MARINe Data. To replicate this workflow follow the steps described in the README file, in order.</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.