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2,444 results for “coloration”
IODP Expedition 379 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
3D models (true color, TIF): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
IODP Expedition 371 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 360 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 397 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Lake morphometry mediates the relationship between water color and fish biomass in small boreal lakes
<p>The data are for an analysis of the influence of water color and lake depth on fish biomass small (1-10 ha) lakes in boreal Sweden.</p> <p>AllBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.</p> <p> </p> <p>SmallBorealLakes.csv contains a list of surface areas (variable name hectares, given in hectares) for all lakes greater or equal to 1 hectare surface area and less than or equal to 10 hectares in the boreal zone of Sweden. The original lake census comes from the Swedish government (Nisell et al. 2007) and lakes within the boreal zone were extracted based on the boreal zone boundary of Olson et al. (2001). There is also a lake ID number (FID_vivan_) used in the extraction.</p> <p> </p> <p>SNILLE_ms_data.csv contains data on fish biomass for 16 small boreal lakes. The geographic coordinates (Northing and Easting) are based on the Swedish Grid, see: http://www.lantmateriet.se. Lake surface areas based on the Swedish lake census (Nisell et al. 2007). Mean depth (meters) is based on echo sounding with an integrated GIS (Lowrance m52i). Volumes were calculated by calculating a triangulated irregular network and then mean depth subsequently calculated as volume divided by surface area. kd is the vertical light extinction coefficient (m^-1). We calculated <em>k</em><sub>d</sub> from the slope of the linear regression of the logarithm of photosynthetically active radiation (measured with LI-COR LI-193 spherical quantum sensor) versus measurement depth (measured in approximately 0.5 meter intervals over the deepest part of the lake). The shallowest measure was excluded from the calculation. The values in the table are the average of kd calculated from three visits to each lake (once each approximately in June, July, and August 2014). kd is an indicator of colored dissolved organic carbon and water color (brownness) in this region and there is relatively little contribution of phytoplankton or inorganic particulate. CPUE Catch-per-unit-effort (kg wet weight / net) is an indicator of fish biomass. For each lake, we set 8 multi mesh gill nets (Nordic 12 nets, 30 x 1.5 m; Mesh sizes: 5, 6.25, 8, 10, 12.5, 15.5, 19.5, 24, 29, 35, 43, 55 mm) over one night (approximately 12 hours) in August 2014. Four nets were deployed in the littoral zone perpendicular to the shoreline. These nets were approximately equally spaced. Two floating nets were deployed across the deepest point of the pelagic zone, and two benthic nets were set in the hypolimnion near the deepest point of the lake. Net-specific catches were averaged with weighting based on the relative extent of the different habitat types (see Karlsson et al. 2015). Specifically, the profundal nets were assumed to represent the total hypolimnetic volume and the pelagic nets were assumed to represent the volume above the hypolimnion. The volume of the littoral nets was calculated by subtracting the volume of the pelagic and profundal habitats from the total lake volume. These weighted CPUE values are given in the file. Species identified through gill netting are abbreviated as: P for European perch (<em>Perca fluviatilis</em>), R for common roach (<em>Rutilus rutilus</em>), N for northern pike (<em>Esox lucius</em>), B for burbot (<em>Lota lota</em>)</p> <p>Boreal_Area_kd_data.csv contains a list of estimated vertical light extinction coefficients (kd, m^-1) for lakes in boreal Sweden. Specifically, the values are based on water chemistry data from a national water quality survey conducted in Sweden every five years. Lake surface water (0.5 m) was sampled from above the deepest part of the lake during early autumn when the water column is mixed. Water quality analyses were performed using standard limnological techniques (detailed methods available on the internet at: http://www.slu.se/en/departments/aquatic-sciences-assessment/laboratories/geochemicallaboratory/water-chemical-analyses/) by a certified water analysis laboratory at the Swedish University of Agricultural Sciences. The data are freely available on the Internet at http://www.slu.se/vatten-miljo. Absorbance at 420 nm (D) which is a metric of water color (brownness) was used to calculate absorption coefficients per meter (a, m-1) from the initial measurement: a = (D * 2.303) / L. where L is the optical path length in meters, 0.05 in the case of the monitoring data. We then estimated kd (m^-1) based on the calibration curve reported by Seekell et al. (2015): = kd = 0.3121 + 0.1327a. These values were associated with surface areas from the Swedish lake census (Nisell et al. 2007) using a identification number common to both the Swedish water chemistry and lake census datasets. Finally, the file was trimmed to only include lakes with surface areas greater or equal to 1 hectare and less than or equal to 10 hectares.</p> <p>References:</p> <ul> <li>Nisell, J., A. Lindsjö, and J. Temnerud (2007), Rikstäckande virtuellt vattendrags nätverk för flödesbaserad modellering VIVAN, [In Swedish], Rapport 2007:17, Institutionen för miljöanalys, SLU.</li> <li>Olson DM, Dinerstein E, Wikramanayake ED, Burgess ND, Powell GVN, Underwood EC, D’amico JA, Itoua I, Strand HE, Morrison JC, Loucks CJ, Allnutt TF, Ricketts TH, Kura Y, Lamoreux JF, Wettengel WW, Hedao P, Kassem KR (2001) Terrestrial ecoregions o the world: A new map of life on Earth. <em>BioScience</em> 51:933-938.</li> <li> <p>Karlsson J, Bergström AK, Byström P, Gudasz C, Rodriguez P, Hein C (2015) Terrestrial organic matter input suppresses biomass production in lake ecosystems. <em>Ecology</em> 96:2870-2876. doi: 10.1890/15-0515.1</p> </li> <li> <p>Seekell DA, Lapierre JF, Karlsson J (2015) Trade-offs between light and nutrient availability across gradients of dissolved organic carbon concentration in Swedish lakes: Implications for patterns in primary production. <em>Canadian Journal of Fisheries and Aquatic Sciences</em> 72:1663-1671. doi: 10.1139/cjfas-2015-0187</p> </li> </ul>
PlantVillage Disease Classification Challenge - Color Images
<p><br> This work is licensed under a <a href="http://creativecommons.org/licenses/by-sa/3.0/us/">Creative Commons Attribution-ShareAlike 3.0 United States License</a>.<br> <br> # Data origins<br> The dataset is originally hosted at <a href="https://www.crowdai.org/challenges/plantvillage-disease-classification-challenge">PlantVillage Disease Classification Challenge</a>.<br> We use the modified version in <a href="https://github.com/salathegroup/plantvillage_deeplearning_paper_dataset">this github repository</a> to do controlled experiments.<br> We only use the raw color images dataset and delete the unconventional characters in the classes directory name and `.csv` filenames.<br> <br> # Directory explanation<br> The `80-20` direcotry has multiple `.txt` files which contain the training (~80%), validation(~10%) and testing (~10%) datasets instances filenames and the corresponding label indexes. The validation dataset quantity is `5430` in all data separation. In our experiment code (not included in this archive), the validation and testing dataset are merged together.<br> <br> # Data usage<br> ## Replicate our experiments<br> We have used this dataset in writing our paper. The reference information can be seen at https://<a href="https://gitlab.com/huix/leaf-disease-plant-village">gitlab.com/huix/leaf-disease-plant-village</a>.<br> <br> ### Steps<br> 1. `cd` to the direcotry (e.g. `/home/usrname/plantvillage_deeplearning_paper_dataset`) that contains the `color` directory.<br> 2. run `python change_filename_prefix.py --prefix /home/usrname/plantvillage_deeplearning_paper_dataset` to modify the prefix path (which is `/home/h/plantvillage_deeplearning_paper_dataset` in our former generated datasets).<br> 3. Fin. You can use our <a href="https://gitlab.com/huix/leaf-disease-plant-village">opens ource codes repository</a> to do the later experiments.<br> <br> ## Generate your own training/validation/testing datasets<br> This data separation generating code isn't included in the dataset archive, it is in our open source code. Please see our <a href="https://gitlab.com/huix/leaf-disease-plant-village">open source code repository</a> for the detailed information.<br> If you have any questions, you can contact the author through email.<br> The email address is a QR code in the archive.</p>
Source Data and Scripts - MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy
<p>Experimental and simulated STED data and scripts associated with Naas et al. "<em>MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy.</em>" <em>bioRxiv</em> (2024): 2024-02. </p> <p>The MultiMatch Python package and further illustrative examples are available on GitHub repository <a href="https://github.com/gnies/multi_match">https://github.com/gnies/multi_match</a>.</p>
IODP Expedition 398 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 355 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 356 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 353 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
IODP Expedition 359 Color reflectance
Color reflectance data were measured on section halves using an integration sphere and a UV-VIS spectrophotometer mounted on the Section Half Multisensor Logger (SHMSL). Spectral counts are recorded in the range of 380 to 700 nm, covering the visible spectrum, and binned in ~2 nm bins. Spectral data are reduced from spectra and recorded in tristimulus XYZ values, CieLAB L*a*b* values, and other units.
Dual color DMD-SIM by temperature-controlled laser wavelength matching [raw datasets]
<p>Raw data set accompanying the publication "Dual color DMD-SIM by temperature-controlled laser wavelength matching".</p>
Brown Dwarfs are Violet: Python Tools for the Estimation of Human-eye Colors of Stars and Substellar Objects
<p>The accompanying files include a Python Jupyter notebook (and associated data files read in by the Python code) that carry out the calculations described by Cranmer (2023), talk 246.05 presented at the 241st Meeting of the American Astronomical Society (AAS) in Seattle, Washington. The abstract of the talk is provided here:</p> <p>There has always been interest in the perceived colors of the stars. They were key to the development of the H-R diagram, and they are also used widely in educational and public-outreach imagery. Thus, it is useful to develop software tools to compute these colors, as accurately as possible, from spectral energy distributions. This presentation follows up on an RNAAS paper (<a href="https://ui.adsabs.harvard.edu/abs/2021RNAAS...5..201C/abstract">Cranmer 2021</a>) that presented a collection of objective (CIE coordinate) and subjective (RGB triple) colors for main-sequence stars and brown dwarfs. A new empirical method of converting from CIE to RGB values is described, and results for various stellar spectra are presented. Although brown dwarfs over a wide range of effective temperatures (400 to 2000 K) emit most of their flux in the infrared, their visible spectra often exhibit a local maximum around a strong dip in the Na I cross section at 0.4-0.5 microns. Thus, they may appear purple to human eyes. Also, the hottest (O-type) main-sequence stars may appear even "bluer than the blue sky" because of Paschen continuum absorption. This presentation will update earlier stellar and brown-dwarf color estimates using more recently published synthetic spectra, and it will also investigate the effects of atmospheric absorption, over a range of air-mass values, on these perceived colors. Python Jupyter notebooks that carry out these calculations will be uploaded to the Zenodo repository for open-access distribution.</p> <p><strong>NOTE 1: </strong>The algorithms described here, for computing RGB triples, ought to be considered as preliminary results in ongoing research; i.e., they need additional testing and validation by comparing to the results of other more established ways of converting astronomical spectra to perceived colors.</p> <p><strong>NOTE 2:</strong> These files follow on from those provided in another Zenodo upload associated with the 2021 RNAAS paper: <a href="https://doi.org/10.5281/zenodo.5293307">https://doi.org/10.5281/zenodo.5293307</a></p>
Species-colored Themisto v3 index with 640k bacterial genomes
<p>This is a Themisto v3 [1] index containing the 639,981 high-quality genomes from 661k bacterial genomes dataset of Blackwell et al. [2]. The index contains all distinct 31-mers of the dataset (both strands). There are 71 billion distinct 31-mers in the data (35.5 billion reverse complement pairs). Each k-mer is annotated with the set of species identifiers that contain that 31-mer. The species identifiers are called colors. There are 2340 distinct colors in the dataset, so the color identifiers range from 0 to 2339.</p> <p>To pseudoalign reads.fastq against the index using 16 threads, install <a href="https://github.com/algbio/themisto">Themisto v3</a>, and use the following command:</p> <pre><code class="language-bash">themisto pseudoalign -q reads.fastq -i themisto_640k/index -t 16 --temp-dir .</code></pre> <p>This will output one line of space-separated integers per read in the input. The first integer on a line is the zero-based rank of the read in the fastq file, and the rest of the integers are the identifiers of colors that are compatible with the read. The file color_names.csv lists the species name and the taxid for each color.</p> <p>The pseudoalignment counts <strong>should not be directly used as abundance estimates</strong> because they only describe which reads are <em>compatible</em> with which species, and a single read may be compatible with many. We recommend using mSWEEP to estimate abundances based on the pseudoalignment data: https://github.com/PROBIC/mSWEEP.</p> <p>--</p> <p>The index was constructed with Themisto v3.0.0 using the following command line parameters:</p> <pre><code class="language-bash">themisto build -i input_file_list.txt --file-colors --reverse-complements -o 640k_bacteria -m 512000 -t 48 -k 31 --temp-dir temp --verbose -d 20</code></pre> <p>The file source_accessions.txt lists the accession numbers of assemblies included in the database.</p> <p>[1] Alanko, J. N., Vuohtoniemi, J., Maklin, T., & Puglisi, S. J. (2023). Themisto: a scalable colored k-mer index for sensitive pseudoalignment against hundreds of thousands of bacterial genomes. bioRxiv, 2023-02.</p> <p>[2] Blackwell, G. A., Hunt, M., Malone, K. M., Lima, L., Horesh, G., Alako, B. T., ... & Iqbal, Z. (2021). Exploring bacterial diversity via a curated and searchable snapshot of archived DNA sequences. PLoS biology, 19(11), e3001421.</p> <p> </p>
A subjective image quality assessment dataset of color graded inverse tone-mapped HDR images
<p>A subjective image quality assessment dataset that includes quality scores of HDR images generated by nine inverse tone mapping methods. The images in the dataset show a wide variety of artifacts commonly present in dynamic range expanded HDR images. Twelve image pairs comprised of an LDR image and its corresponding HDR version were used to conduct the subjective assessment study. These images contain scenes with a wide range of light conditions, representing challenging situations for dynamic range expansion methods.</p> <p>The image quality dataset includes subjective quality scores for 108 inverse HDR images obtained by the different dynamic range expansion methods, scaled in Just Objectionable Differences (JODs). In addition, it includes the raw data from pairwise comparisons obtained from subjective experimentation. The raw data is composed of 6480 trials collected from 15 human observers.</p> <p><strong>Files included</strong></p> <ul> <li>List of images used in our experiments (images.csv).</li> <li>LDR images used as input (ldr.zip).</li> <li>HDR images used as reference (hdr.zip).</li> <li>Inverse tone-mapped HDR images evaluated in our study (hdr_itmo.zip).</li> <li>Pairwise comparison results and JOD scores (subjective-scores.zip)</li> <li>The objective quality scores of the inverse tone-mapped HDR images, computed by each quality metric assessed (objective-scores.zip).</li> </ul> <p> </p>
Photonic crystals with rainbow colors by centrifugation-assisted assembly of colloidal lignin nanoparticles
<p>Source data (CSV files) associated with the publication titled <strong>Photonic crystals with rainbow colors by </strong><strong>centrifugation-assisted assembly </strong><strong>of colloidal lignin nanoparticles</strong>.</p>
Synthetic Colors for Brown Dwarfs using ATMO Non-Equilibrium Non-Adiabatic Atmospheres
<p>The Tables in the spreadsheets give magnitudes in the Vega system, calculated from synthetic spectra generated by ATMO non-equilibrium non-adiabatic atmospheres (Tremblin et al. 2015, Phillips et al. 2020, Leggett et al. 2021). Each file has three tabs corresponding to three metallicities: [m/H] = 0, -0.5 and -1.0. The file 2023_ATMO_MKO_WISE_Spitzer_phot gives MKO Y, J, H, K, Ks, and L'; WISE W1, W2, W3, and W4; and Spitzer [3.6] and [4.5] (columns 5 - 16 in the Tables). The other three files give colors for JWST NIRCam, NIRISS and MIRI filters, as indicated by the file name.</p> <p>The photometry is given for an observer at the Earth, for a brown dwarf at 10 pc with a radius of 0.1 Rsun. Column 3 in the Tables give the radius determined by evolutionary models for different metallicities by Marley et al. 2021 (and https://zenodo.org/record/5063476), for the specified temperature and gravity (columns 1 and 2). Column 4 gives the correction to the magnitudes for the correct (theoretical) radius. NOTE THAT THE CORRECTION MUST BE ADDED TO THE MAGNITUDES GIVEN IN COLUMNS 5 - 16 TO OBTAIN THE ABSOLUTE MAGNITUDE. For an analysis of the model color trends, using a comparison to observations, see Meisner et al. 2023.</p> <p>The photometry covers the following atmospheric parameters: effective temperatures between 1200 and 250 K (step size 100 K between 1200 and 400 K, with the step size decreasing for lower effective temperatures); log(g) with three values 4.0, 4.5 and 5.0; effective adiabatic index of 1.25; metallicity with three values -1.0, -0.5, and 0. A grid of synthetic spectra was computed at medium resolution (R ~3000) for wavelengths of 0.2 to 30 microns. All the models, for a wider range of parameters, are available at https://opendata.erc-atmo.eu. </p> <p>The models include rainout of condensates which depletes refractory species, but they do not include clouds. Tremblin et al. 2016 shows that diabatic convective processes (Tremblin et al. 2019) can reduce the temperature gradient in the atmosphere and reproduce the spectral reddening previously explained by clouds. Adjustments to the atmospheric temperature gradient have also been shown to be necessary to reproduce the energy distributions of the coldest brown dwarfs (Leggett et al. 2021). The grids used here modify the temperature gradient by adopting an effective adiabatic index. The levels modified are in between 0.15 and 15 bars at log g = 4.5 and are scaled by ×10<sup>(log(g)−4.5)</sup> at other surface gravities. Out-of-equilibrium chemistry is used with Kzz = 10<sup>5 </sup>cm<sup>2</sup>/s at log(g) = 5.0 and is scaled by ×10<sup>(2(5−log(g)))</sup> at other surface gravities. The mixing length is assumed to be 2 scale heights at 1.5 bars and higher pressures at log(g) = 4.5 and is scaled down by the ratio between the local pressure and the pressure at 1.5 bars for lower pressures. The 1.5 bars limit is scaled by ×10<sup>(log(g)−5) </sup>at other surface gravities. The chemistry includes 277 species and out-of-equilibrium chemistry has been performed using the model of Tsai et al. 2017. Opacity sources include H<sub>2</sub>-H<sub>2</sub>, H<sub>2</sub>-He, H<sub>2</sub>O, CO<sub>2</sub>, CO, CH<sub>4</sub>, NH<sub>3</sub>, Na, K, Li, Rb, Cs, TiO, VO, FeH, PH<sub>3</sub>, H<sub>2</sub>S, HCN, C<sub>2</sub>H<sub>2</sub>, SO<sub>2</sub>, Fe, H<sup>-</sup>, and the Rayleigh scattering opacities for H<sub>2</sub>, He, CO, N<sub>2</sub>, CH<sub>4</sub>, NH<sub>3</sub>, H<sub>2</sub>O, CO<sub>2</sub>, H<sub>2</sub>S, SO<sub>2</sub>.</p> <p> </p> <p>REFERENCES</p> <p>Leggett et al 2021 ApJ 918, 11</p> <p>Marley et al. 2021 ApJ 920, 85 (and https://zenodo.org/record/5063476)</p> <p>Meisner et al. 2023, ApJ in press </p> <p>Phillips et al. 2020 A & Ap 637, 38</p> <p>Tremblin et al. 2015 ApJ 804, L17</p> <p>Tremblin et al. 2016 ApJ 817, L19</p> <p>Tremblin et al. 2019 ApJ 876, 144</p>
Texas 2022 water clarity and color (FLAMe and Sentinel-2)
Water clarity and color were determined for six reservoirs using rapid spatial surveys from a sensor equipped boat and concurrent Sentinel-2 satellite imagery across Texas during drought conditions between the months of July and August 2022. From west to east, these systems include Red Bluff Reservoir, O.H. Ivie Lake, Lake Arrowhead, Lake Brownwood, Lake Waco, and Lake Bonham. For the water year leading up to the sampling dates, the precipitation ranged from 182 mm in Red Bluff Reservoir to 1036 mm in Lake Bonham. A total of 254 km of boat path were covered across the six reservoirs with a mean boat speed of 19.17 km/h. The data for this study covers three spatial approaches 1) along the boat path 2) longitudinal transects from dam to river arm and 3) whole system. For the boat path, data variables include turbidity measured continuously with a YSI EXO2 sonde, Secchi disk depth predicted from the turbidity values, normalized difference turbidity index (NDTI), and dominant wavelength. For both the longitudinal transects and whole system data, variables include the two remotely derived measures of clarity and color, NDTI and dominant wavelength. Data is also categorized by zone as either "arm" (reservoir arm) or "body" (main body) determined by a 4m depth threshold to compare between zones.
ScienceDex guides
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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.