Find research datasets worth reusing

opencc-by-4.0May 2020View details →

One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.

Dataset abstract This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location. Dataset contents <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> Dataset license This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/

opencc-by-4.0Nov 2020View details →

Supplementary Data for MOCCASIN: A method for correcting known and unknown confounders in RNA-Seq-based splicing analysis

Contents <ol> <li>moccasin_paper_env.yaml: conda environment file with R and Python packages and modules needed to reproduce  analyses.</li> <li>FigureReproduction.zip: data and code to reproduce main and supplemental figures.</li> <li>MOCCASIN_ExampleDataset.zip: A small subset of the simulated data with example code to run MOCCASIN.</li> <li>encode_corrected.zip: Folder with batch-corrected ENCODE differential splicing quantifications (dPSI).</li> </ol>     (1) moccasin_paper_env.yaml Use the moccasin_paper_env.yaml file to create a conda environment from which all analyses for the paper can be reproduced. <pre><code class="language-bash"># need to first install conda. See here: # https://docs.conda.io/en/latest/miniconda.html # Next, create a conda environment: conda env create --name moccasin_paper_env --file moccasin_paper_env.yaml --force # Activate the environment: conda activate moccasin_paper_env</code></pre> The only Python packages not included in this environment are MAJIQ & VOILA. Please see majiq.biocipers.org for installation instructions.     (2) FigureReproduction.zip Within FigureReproduction are folders with code and data to reproduce the main and supplemental figures of the publication. Each folder contains data, script(s) and a README.txt with instructions on how to reproduce figures.     (3) MOCCASIN_ExampleDataset.zip Within this folder is an example dataset to test MOCCASIN. The README.txt file contains detailed line-by-line instructions for how to run MOCCASIN and do post-MOCCASIN analyses. In this example, we show how to run MOCCASIN on a group of .majiq samples with one known confounding effect. Also demonstrated is how to run an "explore unknown residuals" analysis as described in the detailed methods in the supplemental of the paper.      (4) encode_corrected.zip Includes a file called ENCODE_BeforeAndAfterMOCCASIN.voila.tsv.zip which includes LSV quantifications before and after MOCCASIN. Each row in the file represents a junction from an LSV. Each column header starts with the prefix "BeforeMOCCASIN" or "AfterMOCCASIN" and headers ending in dPSI corresponds to the dPSI of an ENCODE knockdown vs control experiment.

opencc-by-4.0Sep 2020View details →

A Consistent and Corrected Nighttime Light dataset (CCNL 1992-2013) from DMSP-OLS data

DMSP-OLS provides the longest observations of NTL information, from 1992 to 2013, an unparalleled dataset for studying historical artificial lights. Version 4 of the DMSP-OLS Nighttime Lights Time Series is widely used ( Image and data processing by NOAA's National Geophysical Data Center. DMSP data collected by US Air Force Weather Agency ). However, it suffers from three main problems: inter-annual inconsistency, saturation, and blooming effect. We used a  series of methods to mitigate the impact and improve data quality. After processing, we get consistent and corrected nighttime light dataset (CCNL). The version 1 products span the globe from 75N latitude to 65S. The products are produced in 30 arc resolution and are made available in GeoTIFF format. Pixel Unit: 'DN'(Digital Number). Each GeoTIFF filename has 4 filename fields that are separated by an underscore "_". A filename extension follows these fields. The fields are described below using this example filename: CCNL_DMSP_1992_V1 Field 1: CCNL(Consistent and Corrected Nighttime Light dataset) Field 2: Platform "DMSP" Field 3: Year “1992” Field 4: version “V1”

opencc-by-4.0Jun 2022View details →

Data supporting 'Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers'

Note: An updated dataset covering the majority of Greenland's marine-terminating glaciers is available as part of the NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) project through the National Snow and Ice Data Center (NSIDC) at <a href="https://doi.org/10.5067/B28FM2QVVYWY">https://doi.org/10.5067/B28FM2QVVYWY</a>.  Data supporting the paper: <blockquote> Chudley, T. R., Howat, I. M., Yadav, B. N., & Noh, M. J. (2022). Empirical correction of systematic orthorectification error in Sentinel-2 velocity fields for Greenlandic outlet glaciers. The Cryosphere. 16, 2629–2642, https://doi.org/10.5194/tc-16-2629-2022 </blockquote> Dataset consists of four netCDF files containing stacked Sentinel-2 velocity data of four Greenlandic outlet glaciers (Helheim Glacier, Jakobshavn Isbræ, Store Glacier, and Kangerlussuaq) between 2017 and 2021. Velocity data are derived and corrected following the methods outlined in Chudley et al. (2022).   NetCDF files are created by, and tested to be readable by, Python's xarray package. The dimensions of the netCDF file are as follows: <ul> <li>X - x coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li>Y - y coordinates in NSDIC Sea Ice Polar Stereographic North (EPSG:3413).</li> <li>time - temporal midpoint of velocity field.</li> </ul> The variables of the netCDF file are as follows: <ul> <li>dmag - the absolute magnitude of the velocity, in metres per day.</li> <li>dx - the velocity in the x direction, in metres per day.</li> <li>dy - the velocity in the y direction, in metres per day.</li> <li>date1 - the date and time of the first scene acquisition.</li> <li>date2 - the date and time of the second scene acquisition.</li> <li>baseline - the temporal baseline, in days, between scene acquisitions.</li> <li>orbit_pair - the combination of orbital pathways in the string format 'RXXX_RYYY', where XXX is relative orbit number of the first scene and YYY the relative orbit number of the second scene.</li> <li>mag_rmse - the root mean square error of the absolute velocity of the off-ice area. </li> <li>dx_mean - the mean velocity of the off-ice area in the x direction.</li> <li>dx_sd - the standard deviation of the velocity of the off-ice area in the x direction.</li> <li>dy_mean - the mean velocity of the off-ice area in the x direction.</li> <li>dy_sd - the standard deviation of the velocity of the off-ice area in the y direction.</li> </ul>

opencc-by-4.0Jan 2021View details →

Bias-corrected monthly precipitation data over South Siberia for 1979-2019

Bias-Corrected Precipitation data over South Siberia (CPSS 1.2) contains monthly precipitation data for the area within the coordinates 50–65 N, 60–120 E for the period from January 1979 to December 2019. CPSS data were combined from monthly total precipitation data from ERA5 reanalysis European Centre for Medium-Range Weather Forecasts (Copernicus Climate Change…, 2017) and precipitation data records from ground weather stations (Il’in et al., 2013). The ERA5 data were scaled according to the derived scale coefficient. The linear scaling coefficient for each month and weather station were calculated and extrapolated to the study area using the ordinary kriging method. Data spatial resolution is 0.25° in the latitude and 0.25° in the longitude.  CPSS reproduces the spatial variability of precipitation more precisely than can be done from the weather station observation network. The CPSS dataset will be useful for the study of extreme precipitation events and allow for more accurate hydrologic risk assessment at a regional level based on climate model results. Data provided in NetCDF (Network Common Data Form) format. Copernicus Climate Change Service (C3S), 2017. ERA5: Fifth generation of ECMWF atmospheric reanalyses of the global climate. Copernicus Climate Change Service Climate Data Store (CDS), Available at <a href="https://cds.climate.copernicus.eu/cdsapp#!/home">https://cds.climate.copernicus.eu/cdsapp#!/home</a> Il’yin, B.M., Bulygina, O.N., Bogdanova, E.G, Veselov, V.M. and Gavrilova, S.Y., 2013. Dataset of monthly precipitation totals, with the elimination of systematic errors of precipitation gauges. Available at  <a href="http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov">http://meteo.ru/data/506-mesyachnye-summy-osadkov-s-ustraneniem-sistematicheskikh-pogreshnostej-osadkomernykh-priborov</a>

opencc-by-4.0Jan 2024View details →

Global Surface Ozone Concentration Dataset 1990-2017 Generated by Bayesian Maximum Entropy Data Fusion With RAMP Bias Correction

This dataset reports estimates of surface ozone concentration at fine spatial resolution for 1990 to 2017, at 0.5 degree horizontal resolution.  Also reported is the variance.  Estimates correspond to this paper: Becker, J. S., DeLang, M. N., K.-L. Chang, M. L. Serre, O. R. Cooper, H. Wang, M. G. Schultz, S. Schroder, X. Lu, L. Zhang, M. Deushi, B. Josse, C. A. Keller, J.-F. Lamarque, M. Lin, J. Liu, V. Marecal, S. A. Strode, K. Sudo, S. Tilmes, L. Zhang, M. Brauer, and J. J. West (2023) Using Regionalized Air Quality Model Performance and Bayesian Maximum Entropy data fusion to map global surface ozone concentration, Elementa Science of the Anthropocene, 11: 1, doi: 10.1525/elementa.2022.00025. The dataset reports estimates of surface ozone for the OSDMA8 metric (the 6-month ozone-season average of the daily maximum 8-hr concentration), estimated through a data fusion of ozone observations from the Tropospheric Ozone Assessment Report (TOAR) database, and output from multiple global atmospheric models.  Estimates are created in each year by a combination of M3Fusion to create a multi-model composite, Regional Air Quality Model Performance (RAMP) regional and nonlinear bias correction, and Bayesian Maximum Entropy (BME) data fusion in space and time.  The estimates here are the final results using a weighted RAMP bias correction.

opencc-by-4.0Sep 2020View details →

Code for producing a consistent and corrected nighttime light dataset (CCNL 1992-2013) from DMSP-OLS data

The DMSP-OLS NTL product suffers from three main problems, i.e.inter-annual inconsistency, saturation, and blooming effect which will affect the accuracy of urban extraction and the estimation of the social-economic indexes. To address these problems, we adopted three correction methods to rectify inter-annual inconsistency, saturation, and blooming effects. The code is written based on the Javascript API provided by the Google Earth Engine  platform(https://earthengine.google.com/)

opencc-by-4.0May 2024View details →

A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction - Simulation Data

Simulation output data used to generate figures of the paper: "<a href="https://doi.org/10.48550/arXiv.2307.09463">A Memristive Neural Decoder for Cryogenic Fault-Tolerant Quantum Error Correction</a>"

opencc-by-4.0May 2024View details →

Data for "Optimization of decoder priors for accurate quantum error correction"

Datasets of surface code and repetition code memory experiments executed on Google's Sycamore quantum processor. See README at the root of each zip archive for detailed description of each dataset.

opencc-by-4.0Aug 2024View details →

Phantom measurement data for 'Fast bias-corrected conductivity mapping using stimulated echoes', Iyyakkunnel et al. (2024)

This dataset contains the phantom measurement data used in the article by Iyyakkunnel et al., titled "Fast Bias-Corrected Conductivity Mapping Using Stimulated Echoes," published in MAGMA, 2024 (doi: 10.1007/s10334-024-01194-3). In this study, the feasibility of using a stimulated echo sequence for electrical properties tomography (EPT) is demonstrated. The data were acquired with a 3T MRI system (Magnetom Prisma; Siemens Healthcare, Erlangen, Germany) using a dual-tuned 1H/23Na quadrature head coil for transmission and reception (Rapid Biomedical, Rimpar, Germany). The dataset includes magnitude and phase measurements for the proposed Double-Angle Stimulated Echo (DA-STE) sequence, as well as reference measurements, including Double Angle measurements using a Gradient Echo sequence (GRE-DAM) for the B1+ magnitude, and a Single Echo Spin Echo sequence (SE) for the transceive phase. For both the DA-STE and SE sequences, each measurement was repeated with inverted readout gradient polarities, denoted as LR (left-right) and RL (right-left) in the respective measurement folders. For each measurement, magnitude and phase data are provided in separate folders (in dicom (.dcm) format). Please note that for DA-STE, the two echo acquisitions are sequentially stored in the same measurement folder. For further measurement details, please refer to the mentioned original article.

opencc-by-4.0Aug 2024View details →

Data for "Quantum error correction below the surface code threshold"

Datasets of the surface code and repetition code memory experiments executed on Google's quantum processor. See README at the root of each zip archive for detailed description of each dataset.

opencc-by-4.0Dec 2021View details →

Precipitation oxygen isoscape for mainland China from 1870 to 2017 generated based on data fusion and bias correction of iGCMs simulations

The dataset includes the stable oxygen isotope of precipitation for the mainland of China over the 1870-2017 period, at a spatial resolution of 50-60 km and a monthly temporal resolution. In order to make full use of observations to integrate the advantages of various iGCMs, the combination of data fusion and bias correction methods are used. Some physical-based ancillary data are introduced in the fusion methods, including elevation and meteorological data, to enrich the climate and terrain information in the process of data fusion. Specifically,(1) for the 1979-2001 period, nine simulations from six iGCMs (CAM2, GISS E, HadAM3, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused with observations by using the CNN fusion method;(2) for the 2002-2007 period, seven simulations from four iGCMs (GISS E, IsoGSM2, LMDZ4, and MIROC32) and ancillary data are fused by using the CNN fusion method;(3) for the 1969-1978 period, four simulations from three iGCMs (CAM2, GISS E, and HadAM3) and ancillary data are fused by using the CNN fusion method;(4) for the 1958-1968 and 2008-2017 periods, two iGCM simulations (CAM2 and HadAM3 for 1958-1968 and IsoGSM2 and LMDZ4 zoomed for 2008-2017) are corrected by using two BCMs, and ensemble mean (mean of four simulations) is then calculated;(5) for the 1870-1957 period, one iGCM simulation (HadAM3) is corrected by using two BCMs, and the ensemble mean (mean of two simulations) is then calculated.Compared with the existing iGCMs, the isoscape has high quality and stability for a large region in China at the monthly scale. However, it should be noted that the isoscape may be more reliable for the common periods of most iGCMs (1969-2007), but mediocre for other periods.

opencc-by-4.0May 2023View details →

Data supporting Tutorial: EPI Distortion Correction

This data support Tutorial: EPI Distortion Correction by Barbara Dymerska for the Weekend Course during the ISMRM 2023 session: Artifacts & Correction Strategies on Sunday, 04 June 2023: https://github.com/fil-physics/ISMRM2023_educational

opencc-by-4.0Apr 2022View details →

Simulation and data analysis for "Center-of-Mass Corrections in Associated Particle Imaging"

IPython notebook used to run simulations and generate all plots the paper "Center-of-Mass Corrections in Associated Particle Imaging". The notebook also includes some more analysis and plots not shown in the paper.

edi44/100

High Frequency Meteorological, Drift-Corrected Dissolved Oxygen, and Thermistor Temperature Data - Lake Sunapee Buoy, NH, USA, 2007 – 2013

The Lake Sunapee Protective Association (Sunapee, New Hampshire, USA) has been operating an instrumented buoy on Lake Sunapee (maximum depth 33.7 meters) beginning on 27 August 2007. The environmental sensors on the buoy from 2007 - 2013 provided information on weather conditions, lake thermal structure, and oxygen dynamics, and their data can be used to calculate physical and biological variables such as buoyancy frequency, thermocline depth, thermal stability, and lake metabolism. The sensors were programmed to collect environmental data every 10 minutes. The buoy collected meteorological data 1.7 meters above the lake surface, including wind speed and direction (Vaisala WXT52 anemometer), air temperature and humidity (Vaisala HMP50), and photosynthetically active radiation (PAR Li-Cor). The water temperature sensors (TempLine thermistors from Apprise Technology in 2007-2010; NexSens T-node sensors 2010-2013) were situated at 0.5-2 meter intervals from 0-14 meters deep with the bottom sensor approximately 1 meter from the sediments. The dissolved oxygen (Zebra-Tech d-opto) sensor was deployed at approximately 1 meter below the surface and recorded oxygen concentration (mg/L), oxygen saturation (%), and temperature at the sensor (oxygen saturation is not included in this dataset). The buoy was anchored at approximately 15 meter deep water near the Loon Island lighthouse in the northern half of the lake, near the deepest part of the lake (43.390 N, -72.057 W). During the winter of 2007 - 2008, the buoy froze into the ice and continually recorded data with the uppermost thermistor below the bottom of the ice. In winter of 2008 - 2009, the buoy was damaged by ice and data were not collected until re-deployment on 29 July 2009. In subsequent years, the buoy was deployed from April or May to October or November at the Loon Island location and limited data were obtained during the winter months at the Sunapee Harbor (43.386 N, -72.081 W). In 2013, the buoy was taken offl

openCC (other)Jan 2020View details →

edi44/100

Sensor data associated with Lucius et al. 2020 – Using machine learning to correct for nonphotochemical quenching in high-frequency in vivo fluorometer data.

This document describes a dataset used to produce Using machine learning to correct for nonphotochemical quenching in high-frequency, in vivo fluorometer data, as reported in: Lucius, M.A., Johnston, K.E., Eichler, L.W., Farrell, J.L., Moriarty, V.W. and Relyea, R.A. (2020), Using machine learning to correct for nonphotochemical quenching in high‐frequency, in vivo fluorometer data. Limnol Oceanogr Methods, 18: 477-494. https://doi.org/10.1002/lom3.10378 The dataset consists of high-frequency water quality and meterological sensor data collected from two autonomous vertical profiling platforms deployed on Lake George, NY during the ice-free months of 2017-2019. Water quality data include depth-referenced measurements of chlorophyll fluorescence, water temperature and dissolved oxygen. Meteorological data include surface-incident total radiation as well as two derived values: solar azimuth and 1-hr rolling average of total radiation. Finally, using interpolated data from regularly collected subsurface profiles of photosynthetically active radiation, estimates of subsurface total radiation were estimated and included in this dataset. This dataset does not include raw data. The data used were subjected to quality control procedures of the Jefferson Project, as well as additional outlier removal measures and the creation of derived data (as previously described and described in detail in Lucius et al. 2020).

openCC (other)Jan 2021View details →

opencc-by-4.0Sep 2015View details →

Data from Study: Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration

This dataset contains the data used to arrive at the conclusions in the research article Respiratory Motion Correction of PET using MR-Constrained PET-PET Registration, by Balfour et al [BioMedical Engineering OnLine 2015, 14:85]. This study was based upon motion-affected PET images simulated from real dynamic MR image volumes, simulated and reconstructed using the Software for Tomographic Image Reconstruction ("STIR", see http://stir.sourceforge.net/). This dataset includes data from MR scans of 4 healthy volunteers (male, aged 22-33). Three types of data are provided, which should be sufficient for repeating the findings of the study: <ul> <li>Reconstructed PET image volumes, split into 6 respiratory bins ("gates") for each simulation</li> <li>The dynamic 3D MR volumes used to derive the respiratory motion of each volunteer</li> <li>Text files outline which dynamics have NOT been used for PET simulation - these are the ones used to make the motion model in the study</li> </ul> These MR volumes were registered and combined with the head-foot position of the right hemidiaphragm to form a respiratory motion model, which was subsequently used to constrain PET to PET image registration, attempting to correct for the motion in the PET images. For more detailed information regarding the method, please refer to the article. The PET data is split into several sub-categories: <ul> <li>Volunteer ID (4 possibilities, anonymised)</li> <li>Lesion position (9 possibilities - see article for locations)</li> <li>Lesion diameter, in millimetres (10 or 14 mm)</li> <li>Respiratory gate number, ranging from 1 (most inhaled) to 6 (most exhaled)</li> </ul> Note that there are two types of each simulation: with motion, and without motion. These are included in the respective zip files for each volunteer ID.

opencc-by-4.0Nov 2021View details →

Fortnight Climatology Average of Corrected Chlorophyll-a Concentration from MODIS Level-2 Data in the Algerian Basin (2003-2018)

We deposited the corrected MODIS Level-2 Chlorophyll data after a specific processing steps applied for the first time to the standard cloud-corrected Level-2 Chlorophyll fields to detect and remove spurious local patterns that affect data quality, even in time series averages. From a purely spatial point of view, the quality of the Chlorophyll field is improved more significantly by the outlier removal than the previous synthetic representation suggests.

opencc-by-4.0Jan 2022View details →

Inner filter effect correction for fluorescence measurements in microplates (ZINFE and NINFE) - experimental data

Experimental data for the paper entitled Inner Filter Effect Correction for Fluorescence Measurements in Microplates Using Variable Vertical Axis Focus (https://doi.org/10.1021/acs.analchem.2c01031). Separate datasets are provided for data with background correction, without background correction, and absorbance-corrected data. All results were obtained using the online calculator service written in Javascript: https://ninfe.science (version 15.9.2021.). For additional details please visit: https://glymech.pharma.hr//GlyMech.html.