Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,509
datasets available to search
ShareScore release 0.9.0
Dataset results
1,509 results for “Calibrations”
Raw Picarro L2140i data - Support to "A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements"
<p>Support data to reproduce Figure 3 - Figure 10 from the article:</p> <p><em>A versatile water vapor generation module for vapor isotope calibration and liquid isotope measurements</em></p> <p>by Hans Christian Steen-Larsen and Daniele Zannoni</p> <p>Accepted in Atmospheric Measurement Techniques on 25/05/2024 (Preprint available at <a href="https://doi.org/10.5194/amt-2023-160" rel="nofollow">https://doi.org/10.5194/amt-2023-160</a>)</p> <p>Figure numbers refer to the final (peer-reviewed and accepted) version of the manuscript.</p> <p>To reproduce the figures, the data can be used in conjunction with the code available at <a href="https://github.com/danielez83/AMT-2023-160" target="_blank" rel="noopener">https://github.com/danielez83/AMT-2023-160 </a>(https://zenodo.org/doi/10.5281/zenodo.12741980)</p> <p><strong>File description</strong></p> <p>Figure numbers are referring to the final (peer-reviewd and accepted) version of the manuscript.</p> <ul> <li>ADEV_BER17k_withmemory_CALIBRATED_R1.csv <ul> <li>Allan Deviation data without removing memory effect, used in FIgure 3</li> </ul> </li> <li>Cal_Pulses_MultiOven_new20230609.csv <ul> <li>Data obtained with the multioven configuration, used in Figure 9</li> </ul> </li> <li>Cal_Pulses_Selector.csv<br> <ul> <li>Data obtained with the VICI selector configuration (only one oven working), used in Figure 9</li> </ul> </li> <li>HKDS2092.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2092) raw data.</li> </ul> </li> <li>HKDS2156.zip<br> <ul> <li>Compressed archive of the Picarro L2140i (HKDS2156) raw data.</li> </ul> </li> <li>HKDS2156_IsoWater_20221116_165037.csv<br> <ul> <li>Results of liquid injections with Picarro vaporizer, used in Figure 4 and Figure 10</li> </ul> </li> <li>SP_BER_inj_time.csv <ul> <li>Date and times of injections, used in Figure 4 and Figure 10</li> </ul> </li> <li>Timings_Picarro.xlsx <ul> <li>Excel spreadsheet with time and dates of experiment. It is used as a lookup table to retrieve the raw data correctly</li> </ul> </li> </ul>
Modelling Kepler red giants in eclipsing binaries: calibrating the mixing-length parameter with asteroseismology
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.475..981L/abstract">Li et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx3079">10.1093/mnras/stx3079</a></p>
Diagnostic Evaluation of Large-domain Hydrologic Models calibrated across the Contiguous United States
<p>Data repository for: Rakovec, O., Mizukami, N., Kumar, R., Newman, A., Thober, S., Wood, A. W., et al. ( 2019). Diagnostic evaluation of large‐domain hydrologic models calibrated across the contiguous United States. <em>Journal of Geophysical Research: Atmospheres</em>, 2019; 124: 13991–14007. <a href="https://doi.org/10.1029/2019JD030767">https://doi.org/10.1029/2019JD030767</a></p> <p>If you use this dataset in scientific publication, the aforementioned publication needs to be acknowledged.</p> <p><strong>rakovec_JGRA_2019.tar.gz </strong>refers to the mHM model simulations</p> <p><strong>Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</strong> refers to a dataset published earlier in Mizukami et al. (2017, doi: 10.1002/2017WR020401)</p> <p>#########################################################################</p> <p>## BASIN-WISE DISCHARGE SIMULATIONS:</p> <p>#########################################################################</p> <p><strong>(1) VIC model: CONUS-wide runs based on the Mizukami et al. 2017 WRR paper</strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04_rgn0.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04_rgn0.txt</li> </ul> <p>Note the time stamp is missing in the VIC files, and should be following for</p> <ul> <li>calibration period: init_date="1999-10-01"</li> <li>validation periods: init_date="1989-10-01"</li> </ul> <p>Finally, headers are missing for the VIC files, should be:</p> <ul> <li>qsim is first column</li> <li>qobs is second column</li> </ul> <p><strong>(2) VIC model: onsite calibrations </strong></p> <p>stored under: Mizukami_etal_2017_WRR_results.calib.basin.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./$BASIN_ID/output/hcdn_calib_case04.txt</li> <li>Validation period: ./$BASIN_ID/output/hcdn_vali_case04.txt</li> </ul> <p><strong>(3) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, 1 parameter set applied across all basins.</p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output_calibMB_eval/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_validMB_eval/daily_discharge.out</li> </ul> <p><strong>(4) mHM model: onsite calibrations </strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>meaning, each basin has different parameter set </p> <ul> <li>Calibration period: ./mHM_basins/$BASIN_ID/calib_001/output/daily_discharge.out</li> <li>Validation period: ./mHM_basins/$BASIN_ID/calib_001/output_valid/daily_discharge.out</li> </ul> <p><strong>(5) mHM model: default parameter set from EU/Germany</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <p>the prior parameter set taken from the develop git branch of mhm</p> <p>Note that the calibration and validation periods are in one file:</p> <ul> <li>./mHM_basins/$BASIN_ID/def_000/output/daily_discharge.out </li> </ul> <p>#########################################################################</p> <p>## CONUS-WISE FLUXES,STATES,PARAMETERS</p> <p>#########################################################################</p> <p><strong>(1) mHM model: CONUS-wide runs based on Rakovec et al. 2019 JGR-A</strong></p> <p>stored under: rakovec_JGRA_2019.tar.gz</p> <ul> <li>Fluxes/states: mHM_entire_domain/calib_001/output/mHM_Fluxes_States.nc (monthly time step: 1950-2010)</li> <li>Model parameters (from the restart file): mHM_entire_domain/calib_001/output/mHM_restart_001.nc</li> </ul> <p> </p>
Data collection that describes the calibration of the temporal and displacement characteristics of an optical tweezers in buffered saline at room temperature.
<p><strong>Introduction</strong>. This data provides the calibration of an optical tweezers within the linear Hookian region. Once the laser was aligned, the power at the objective measured and a bead trapped the instrument was calibrated. <strong>Data Collection</strong>. Calibration was performed by moving a trapped bead by supplying a periodic square wave train input (amplitude: −800 to +800 nm, period: 80 ms) to an acousto-optical device, AOD and monitoring the trajectory of the bead in the 𝑋𝑌 plane with a quadrant photo diode, QPD for ~ one minute. Data was collected at 200 kHz from three (3) QPD channels: displacement in the 𝑋 (∆𝑋<sub>𝑀</sub>) (i) and 𝑌 directions (∆𝑌<sub>𝑀</sub>) (ii) and the sum of the fluorescent intensity (∑<sub>𝐿</sub>) (iii). Typically, each bead was stimulated with a train of square waves of constant amplitude in the 𝑋 direction. This stimulation was performed four (4) times at each of the different amplitudes i.e., −800, +800, −500, and +500 nm. The QPD signal in the dark, ∆𝑋<sub>𝐷</sub>, ∆𝑌<sub>𝐷</sub>, ∑<sub>𝐷</sub> was recorded and subtracted in real time, and the gain, 𝐺 of the QPD noted. The Stokes-Faxen coefficient, 𝛽 was calculated having recorded the height of the bead above the Petri dish, ℎ and the viscosity, 𝜂 of the saline solution. The detected fluorescent bead was excited either with a Xenon lamp or TLED transmitted light source. Some calibrations were performed with a TLED light that was borrowed from the manufacturer for testing, and the signal to noise of these measurements was significantly decreased.<strong> Data Transformation</strong>. For each of the two (2) channels the measured signal was normalized by the sum, and the ~750 waves were averaged for each pulse train stimulus. The reciprocal of the time constant (s<sup>-1</sup>) was determined from the exponential rise or decay of the back-ground subtracted averaged response. Spring constant (pN/nm) was determined from the product of this reciprocal time constant and the Stokes-Faxen coefficient (pNs/nm). The net displacement of the bead (nm) was calculated by determining the resultant vector of the 𝑋 and 𝑌 components of the QPD (V/V). For the four (4) stimuli determined for each bead the normalized displacement (V/V) was plotted as a function of net bead displacement and the slope (V/V/nm) calculated from best linear fit. For each calibration the mean spring constant, reciprocal time constant, and mean slope are provided. <strong>Data Format</strong>. The data was saved in LabView with the proprietary TDMS format (National Instruments, NI, Austin, TX). It was transformed to a text file and imported into MATLAB (The Mathworks, Natick, MA) and stored as struct and analyzed with a Python script. The collection contains raw and transformed results from 69 days for a total 153 beads. The data is provided with annotated descriptions in HDF5, a standard non-proprietary container storage format. Each file is about 1.1 GBs (HDF5).</p> <p> </p> <p>The package contains:</p> <p>1. Standard Container HDF5 with custom organization format of data with annotations.</p> <p>2. Python Script (calibrateopticaltweezers.py file) that describes how data is analyzed and written to HDF5 and .MAT formats. (1 file)<br> </p>
Genus level DNA sequence data for three genes (matK, rbcL, trnH-psbA) for the paper: A comprehensive, genus-level time-calibrated phylogeny of the tree flora of Mediterranean Europe and an assessment of its vulnerability
<p>This data file contains the consensus DNA sequences in fasta format, of 64 tree genera found in Mediterranean Europe, following the checklist of Médail et al. (2019). </p> <p>The data are used in a manuscript submitted for publication to Botany Letters and currently under revision. The manuscript is entitled: "<em>A comprehensive, genus-level time-calibrated phylogeny of the tree flora of Mediterranean Europe and an assessment of its vulnerability</em>". Its authors are: Marwan Cheikh Albassatneh, Marcial Escudero, Loic Ponge<sup>*</sup>, Anne-Christine Monnet, Juan Arroyo, Toni Nikolic, Gianluigi Bacchetta, Francesca Bagnoli, Panayotis Dimopoulos, Agathe Leriche, Frédéric Médail, Anne Roig, Ilaria Spanu, Giovanni Giuseppe Vendramin, Arndt Hampe, Bruno Fady.</p>
Calibrated kinematic Ninapro hand movements data
<p>The kinematic data of the Ninapro (Non Invasive Adaptive Prosthetics) database includes calibrated kinematic data from 77 subjects, performing 40 hand movements and grasps. The whole database aims at allowing worldwide research groups to study hand kinematics. As well the dataset allows also to study the relationship between hand kinematics and muscle activity, since it is linked to sEMG datasets. The final goal of this work is to aid the progress in rehabilitation, physiotherapy, medicine, neuroscience and prosthetics. Therefore, the kinematic database improves the state of the art, being the most accurate, comprehensive and advanced reference for the largest kinematic database existing at the time of writing. <br> <br> <strong>Acquisition protocol</strong>:<br> Hand kinematics was measured using a 22-sensor CyberGlove II dataglove (CyberGlove Systems LLC, <a href="http://www.cyberglovesystems.com/">www.cyberglovesystems.com</a>). The CyberGlove is a motion capture data glove, instrumented with joint-angle measurements. It uses proprietary resistive bend-sensing technology to transform hand and finger motions into real-time digital joint-angle data.<br> <br> The calibrated kinematic Ninapro database includes 40 different movements of 77 intact subjects.<br> The subjects have to repeat several movement represented by movies that are shown on the screen of a laptop.<br> The experiment is divided in two exercises:<br> • Isometric, isotonic hand configurations and basic wrist movements<br> • Grasping and functional movements<br> Joint angles from raw data were calculated according to a very detailed calibration protocol. The protocol was previously applied to 10 different intact subjects and consists in recording 64 different poses or guided movements to obtain the gains and also some corrections because of cross-coupling effects for specific anatomical angles.</p> <p>Gracia-Ibáñez, V., Vergara, M., Buffi, J. H., Murray, W. M. & Sancho-Bru, J. L. Across-subject calibration of an instrumented glove to measure hand movement for clinical purposes. C. Comput. Methods Biomech. Biomed. Eng. 20, 587–597 (2017).<br> <br> <strong>Data Sets</strong>:<br> For each subject and exercise, the database contains one file in Matlab format (<a href="http://www.mathworks.com/">www.mathworks.com</a>) with synchronized variables. The variables included are the following ones:<br> • subject: subject number;<br> • exercise: exercise number;<br> • glove (22 columns): uncalibrated signal from the 22 sensors of the Cyberglove. Details on the location of the sensors are available at the link: <a href="http://ninapro.hevs.ch/node/123">ninapro.hevs.ch/node/123</a>;<br> • angles(22 columns): calibrated signal from the 22 sensors of the Cyberglove by applying an across-subject calibration<br> • re-stimulus (1 column): the a-posteriori refined label of the movement;<br> • re-repetition (1 column): re-stimulus repetition index;</p> <p> • stimulus (1 column): the label of the movement;<br> • repetition (1 column): stimulus repetition index;<br> • order of angles (22 columns) : name of the angles corresponding to variable “angles”.</p> <p>Raw glove data are also included in the folder "Raw data".</p>
Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems
<p>Data set belonging to the IEEE Trans. Instr. Meas. paper with DOI: <a href="https://doi.org/10.1109/TIM.2018.2879171">10.1109/TIM.2018.2879171</a></p> <p>G. Rietveld, E. Mohns, E. Houtzager, H. Badura, and D. Hoogenboom,<br> <em>Comparison of Reference Setups for Calibrating Power Transformer Loss Measurement Systems</em></p> <p>This project has received funding from the European Metrology Programme for Innovation and Research co-financed by the Participating States and in part by the European Union’s Horizon 2020 Research and Innovation Programme.</p>
Text-fig. 2. Date calibration of skull from Moča (Komárno district, southern Slovakia). in A Late Upper Palaeolithic Skull From Moča (The Slovak Republic) In The Context Of Central Europe
Text-fig. 2. Date calibration of skull from Moča (Komárno district, southern Slovakia).
Time-calibrated phylogeny of Neotropical Freshwater Fishes
<p>The Neotropical Freshwater Fish (NFF) fauna exhibits the greatest phenotypic disparity and species richness among all continental aquatic vertebrate faunas, with more than 6,345 species distributed across the mostly tropical regions of Central and South America. The two several decades have seen a proliferation of molecular phylogenies, often at the species level, covering almost all 875 valid NFF genera. This study presents the most comprehensive genome-wide, time-calibrated phylogenetic hypothesis of NFF species to date, based on DNA sequences generated over decades through collaborative efforts of the multinational ichthyological research community. Our purpose is to compile an extensive dataset and organize it systematically, allowing for continuous refinement and expansion, thereby enabling researchers to evaluate macroevolutionary hypotheses in the NFF. Using thousands of DNA sequences from dozens of studies, we compiled a supermatrix of 51 markers across 5,984 taxa, comprising 3,167 species representing 50% of NFF species, and estimating divergence times based on a fossil-calibrated phylogenomic tree of fishes. We used this dataset to infer the most species-rich time-calibrated phylogeny of the NFF taxa to date, summarizing the collective efforts of the ichthyological research community since the midpoint of the last century. We hope this dataset provides a framework for forthcoming evolutionary studies of the NFF fauna, documenting macro-scale patterns in the world's most diverse continental vertebrate fauna.</p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Synergism, Fluorescent Calibration
<p>Fluorescent and darkfield images for the synergism fluorescent calibration droplets of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
High-throughput combinatorial droplet generation by sequential spraying: Fig 4, Antagonism, Fluorescent Calibration
<p>Fluorescent images for fluorescent controls of Figure 4 of "High-throughput combinatorial droplet generation by sequential spraying." Accompanying code is found at https://github.com/RenaFukuda99/High-throughput-combinatorial-droplet-generation-by-sequential-spraying. </p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - CALIBRATED MOVEMENT DATA AND VARIABLES SUPPORTING ANALYSES
<p><strong>Description of the data and file structure<br></strong>This record contains data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: (1) calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13328931 - all 2011 & 2014 audio data.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of the movement data files<br></strong>The movement files have been calibrated from the raw data and are ready to use. The files contain the .mat extension, and need to be opened using Matlab and the tagtools tool kit available at https://github.com/animaltags . Tutorials for working with the toolkit are available at animaltags.org . These files contain several vector and matrix variables. We define those used in our analyses below. For questions about how to work with these files, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p>Aw: calibrated triaxial accelerometer data (converted from tag frame to whale frame)</p> <p>fs: sample rate (50 Hz)</p> <p>head: animal's circular heading (rotation about the dorsal-ventral axis, in radians)</p> <p>Mw: calibrated triaxial magnetometer data (converted from tag frame to whale frame)</p> <p>p: depth (in meters)</p> <p>pitch: animal's pitch (rotation about the left-right axis, in radians)</p> <p>roll: animal's roll (rotation about the anterior-posterior axis, in radians)</p> <p>tempr: temperature recorded on tag (in Celsius)</p> <p>TT: time cues for the start and end of every analyzed dive within a deployment. This matrix contains 6 columns:<br>-col 1: start cue (in sec)<br>-col 2: end cue (in sec)<br>-col 3: maximum depth of dive (m)<br>-col 4: time cue at max depth (in sec)<br>-col 5: mean depth (m)<br>-col 6: mean compression</p> <p> </p> <p><strong>Description of the analyzed variables<br></strong>The data are provided column-wise in a spreadsheet, whereby each column contains one of several variables used to build the corresponding models listed in Table 2 in the above article. Model details are provided in the above article, including the statistical packages needed to run the models. </p> <p><em>The following is a list of variable names (column headers) and their corresponding definitions:<br></em><strong>bzsounds:</strong> binary presence (1)/absence (0) of buzz bouts within a dive. Buzzing is defined as the occurrence of echolocation clicks with an inter-click interval < 11 ms<br><strong>code:</strong> categorical identifier of the numerical week of year in which the tag was deployed (e.g., week 33 of 2009 is different than week 33 of 2011)<br><strong>deployment:</strong> the event whereby a tag was affixed to an individual killer whale and data were collected via tag sensors; each deployment was assigned a unique deployment ID, consisting of the first letter of the Genus and species names (“oo” for Orcinus orca), followed by two digits corresponding to the year (“09” = 2009), followed by the Julian day of the year (e.g. “234”), followed by a letter indicating the deployment order of the day. NRKW deployments were assigned a through l, and SRKW deployments were assigned m through z (e.g. “a” = first deployment of the day for NRKW, “m” = first deployment of the day for SRKW)<br><strong>durwho: </strong>duration of a whole dive, in seconds. Dives were defined as all departures from the surface, to at least 1 m or deeper, followed by a return to within 0.5 m of the surface<br><strong>divenum: c</strong>hronological identifier for dive position within a deployment (e.g., for the 10<sup>th</sup> dive within a deployment, divenum = 10)<br><strong>kindet: </strong>binary presence (1)/absence (0) of a prey capture event within a dive. Prey capture was informed by the occurrence of stereotyped movement signatures in sensor data indicative of prey capture, following an established method validated with visual and acoustic confirmation of predation events. Prey capture is defined as the occurrence of three movement variables indicative of prey capture (peak jerk, roll and heading variance) each exceeding pre-determined thresholds (see Tennessen et al. 2019b in above article for details)<br><strong>maxdep:</strong> maximum depth of a dive, in meters<br><strong>NLmax: </strong>the maximum noise level received during a dive, measured as the root-mean-square sound pressure level (dB re 1 mPa) within one second bins over the 15-45 kHz frequency band<br><strong>population:</strong> population to which the tagged whale belongs (NRKW = Northern Resident killer whale; SRKW = Southern Resident killer whale)<br><strong>sex:</strong> sex of tagged whale (F = female, M = male, NA = unknown)<br><strong>sc:</strong> binary presence (1)/absence (0) of slow-click sounds within a dive. Slow-clicking is defined as the occurrence of echolocation clicks with an inter-click interval >100 ms<br><strong>tagID:</strong> identifier for the individual tag used for each deployment<br><strong>year:</strong> year of deployment</p>
Spectral data for direct detection and quantification of phycocyanin in sediments by hyperspectral imaging: Standard calibration
<p>Accompanying data to publication: <strong>Direct detection and quantification of phycocyanin in sediments by hyperspectral imaging</strong></p> <p>Hyperspectral data with all their processing steps - normalization, ROI, subsets, masking - resulting in RABD indices of Chlorophyll a and Phycocyanin pigments from spiking experiments on sediments.</p>
Raw ptychographic synthetic data for manuscript with title 'Purity-based self-calibration in ptychography'
<ul> <li>Here given are the dataset for purity scan, numerically created based on the synthetic setup in the manuscript titled "Purity-based self-calibration in ptychography". The dataset includes raw diffraction patterns, preprocessed diffraction patterns for reconstruction, as well as the preprocessed script.</li> <li>For the two experimental verificatoin cases, the preprocessed diffraction patterns are provided, where the scanning grid is included.</li> <li>The reconstruction, calculation of purity, and zPIE were conducted at open-source PtyLab framework.</li> <li>Experimental data were measured at the Institute of Applied Physics in Jena using a Fiber Laser driven High-order harmonic source, which can be referenced in </li> </ul> <p>Please contact me (liu.chang@uni-jena.de) for additional support.</p>
An analysis of peak fitting in reference material spectra for calibration of Raman spectroscopy instruments (Dataset)
<p>tbd</p> <p>NeXus format</p>
Design of facilitated dissociation enables control over cytokine signaling duration - SMT raw data - unstimulated - calibration beads - long term tracking - labelled ligand
<p>This dataset contains the raw data for the Single-molecule tracking data of the manuscript: "Design of facilitated dissociation enables control over cytokine signaling duration". Presicely, it contains the calibration beads for all imaging experiments, the long term tracking experiments, the experiments with labelled ligand and the unstimulated probes</p>
Raw data for optimization and calibration of Micellar liquid chromatography as a sustainable tool to quantify three statins in oral solid dosage forms
<p>A method based on micellar liquid chromatography has been developed to determine rosuvastatin, lovas- tatin and simvastatin in oral solid dosage forms. Samples were solved in mobile phase up to the target concentration, filtered and directly injected. The three statins were resolved in 30 min, using an aqueous solution of 0.10 M sodium dodecyl sulfate –7.0% 1-butanol, buffered at pH 3 with 0.01 M phosphate salt as mobile phase, running under isocratic mode at 1 mL/min through a C 18 column. Detection was at 240 nm. The effect of sodium dodecyl sulfate on elution strength was more important than that of the organic solvent. The procedure was successfully validated by the guidelines of the International Coun- cil for Harmonization in terms of: specificity, linearity ( r 2 > 0.990), calibration range (1.5 - 15 mg/L for rosuvastatin, 0.5–10 mg/L for lovastatin and simvastatin), limit of detection (0.4, 0.2 and 0.15 mg/L for ro- suvastatin, lovastatin and simvastatin, respectively), trueness (98.8–101.7%), precision ( < 2.7%), carry-over effect, robustness, and stability. Values were inside the acceptance criteria of the Methods, Method Veri- fication and Validation, Food and Drug Administration-Office of Regulatory Affairs, thus ensuring the re- liability of the results. The main feature was the low proportion of organic solvent used, thus making the procedure sustainable and green. Besides, it was easy-to-conduct and with high sample-throughput, and then useful for routine analysis in pharmaceutical quality control. Finally, it was applied to commercial pharmaceutical preparations.</p>
A comprehensive calibration approach for the Northwest River Forecast Center
<p>This dataset contains data to accompany the paper "A comprehensive calibration approach for the Northwest River Forecast Center", 2025, in prep. </p> <p> </p> <p><strong>CAMELS_all_locations</strong> - Calibration results using the NWRFC approach for all CAMELS locations in the NWRFC domain</p> <p><strong>CAMELS_NWRFC</strong> - Shape file and basin caracteristics for all CAMELS locations in the NWRFC domain</p> <p><strong>cb_2018_us_state_5m</strong> - Shape file with US state boundaries used in figure creation</p> <p><strong>nwrfc_basins_sf</strong> - Shape file with NWRFC basin delineations</p> <p><strong>NWRFC_Boundary</strong> - Shape file with the NWRFC service boundary</p> <p><strong>Optimization_CAMELS_FA</strong> - Full cross validation and calibration results with forcing adjustments applied for 4 NWRFC basins (FSSO3, SAKW1, WCHW1, WGCM8) </p> <p><strong>Optimization_CAMELS_noFA</strong> - Full cross validation and calibration results without forcing adjustments applied for 4 NWRFC basins (FSSO3, SAKW1, WCHW1, WGCM8) </p> <p><strong>world-administrative-boundaries</strong> - Shape file with country boundaries used in figure creation</p>
Geant4 simulation of ARASE HEP instrument calibration
<p>This dataset created for the calibration of ARASE HEP instruments. The Geant4 particle simulation results contained in this data. The format of this data is root. The root analysis tool needs to be required to see this data.<br> </p>
A cerebellar internal model calibrates a feedback controller involved in sensorimotor control
<p>This upload contains datasets from the manuscript by Markov et al., 2021 "A cerebellar internal model calibrates a feedback controller involved in sensorimotor control" (preprint <a href="https://www.biorxiv.org/content/10.1101/2020.02.12.945956v1">here</a>; the code used to acquire and process these data can be found <a href="http://doi.org/10.5281/zenodo.5044042">here</a>). The datasets are organized in the following zip folders:</p> <ul> <li><strong>behavioral_data.zip</strong> contains data from acute reaction and long-term adaptation experiments for wild-type control, treatment control, and Purkinje-cell-ablated zebrafish larvae</li> <li><strong>feedback_control_model.zip</strong> contains parameters of the model after fitting it to the data acquired during the acute reaction experiment.</li> <li><strong>PC_ablation_quantification.zip</strong> contains confocal images of the cerebella of zebrafish larvae before and after pharmaco-genetic ablation of Purkinje cells.</li> <li><strong>reference_brain_stacks.zip</strong> contains z-stacks used for anatomical registration of the functional imaging data</li> <li><strong>whole_brain_imaging_inetgrators.zip</strong> contains whole-brain light-sheet data acquired while zebrafish larvae were performing optomotor response to forward moving grating. This dataset was used to identify sensory integrators in the larval zebrafish brain</li> <li><strong>PC_imaging_long_term_adaptation.zip</strong> contains light-sheet recordings of Purkinje cell activity acquired during long-term adaptation to lagged visual feedback. This dataset was used to identify signatures of internal models in the Purkinje cell activity.</li> <li><strong>whole_brain_imaging_long_term_adaptation.zip</strong> contains whole-brain light-sheet recordings during long-term adaptation to lagged visual feedback. This dataset was used to study correlates of the long-term adaptation in the entire brain (such as change in sensory integration time constants).</li> </ul> <p>For any question about the data, please write at <a href="mailto:ruben.portugues@tum.de">ruben.portugues@tum.de</a> or <a href="mailto:ruben.portugues@tum.de">daniil.markov@charite.de</a>.</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.