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.
12,753
datasets available to search
ShareScore release 0.9.0
Dataset results
12,753 results for “Corrections”
WDS-RDA-F11 Publishing Data Workflows WG Synthesis FINAL CORRECTED
<p>Final, revised version of http://dx.doi.org/10.5281/zenodo.33413 (published 6 November 2015)</p>
The free-breathing motion-corrected phase sensitive inversion recovery sequence provides improved myocardial fibrosis evaluation while significantly shortening acquisition time upon comparison to conventional gradient echo sequences: a tripartite comparison of phase-sensitive inversion recovery sequences.
<p>This article includes original research performed at a US Academic Center related to comparison of three separate Phase-Sensitive Inversion Recovery (PSIR) pulse sequences (Breath-hold Single-Shot SFFP, Breath-hold TurboFLASH, and Free-breathing Motion-Corrected SSFP) evaluating the ability of each PSIR sequence to demonstrate myocardial hyperenhancement. All three PSIR sequences were performed as a part of a cardiac MRI performed on a patient clinically referred for cardiac MRI with and without contrast. A total of 28 patients were examined with the three PSIR sequences. All three PSIR sequences were performed in the short axis 10 – 25 minutes after intravenous injection of a Gadolinium-based contrast agent. </p> <p>Evaluation of the PSIR sequences ability to detect myocardial late gadolinium enhancement (LGE) was performed by a retrospective review by two blinded, experienced cardiovascular imagers. The review was a qualitative inspection that included grading by a 5-point Likert scale for the sequence’s ability to resist motion artifact, image resolution, ability to visualize hyperenhancement, and overall satisfaction. The number of myocardial segments demonstrating LGE was also quantitated, and the acquisition time of each PSIR sequence was performed.</p> <p>To our knowledge this is the first study that compares the 3 available PSIR LGE sequences with a specific attention to acquisition time (TA). Given our initial study resulted in the conclusion that the motion-corrected SSFP PSIR sequence was superior the the TurboFLASH Gradient Echo PSIR sequence in regards to evaluator grading and acquisition time efficiency.</p>
The Free-Breathing Motion-Corrected Phase Sensitive Inversion Recovery Sequence Provides Improved Myocardial Fibrosis Evaluation while Significantly Shortening Acquisition Time Compared to Conventional Gradient Echo Sequences.
<p>This article includes original research performed at a US Academic Center related to comparison of three separate Phase-Sensitive Inversion Recovery (PSIR) pulse sequences (Breath-hold Single-Shot SFFP, Breath-hold TurboFLASH, and Free-breathing Motion-Corrected SSFP) evaluating the ability of each PSIR sequence to demonstrate myocardial hyperenhancement. All three PSIR sequences were performed as a part of a cardiac MRI performed on a patient clinically referred for cardiac MRI with and without contrast. A total of 28 patients were examined with the three PSIR sequences. All three PSIR sequences were performed in the short axis 10 – 25 minutes after intravenous injection of a Gadolinium-based contrast agent. </p> <p>Evaluation of the PSIR sequences ability to detect myocardial late gadolinium enhancement (LGE) was performed by a retrospective review by two blinded, experienced cardiovascular imagers. The review was a qualitative inspection that included grading by a 5-point Likert scale for the sequence’s ability to resist motion artifact, image resolution, ability to visualize hyperenhancement, and overall satisfaction. The number of myocardial segments demonstrating LGE was also quantitated, and the acquisition time of each PSIR sequence was performed.</p> <p>To our knowledge this is the first study that compares the 3 available PSIR LGE sequences with a specific attention to acquisition time (TA). Given our initial study resulted in the conclusion that the motion-corrected SSFP PSIR sequence was superior the the TurboFLASH Gradient Echo PSIR sequence in regards to evaluator grading and acquisition time efficiency.</p>
Classifying the Correctness of Generated White-Box Tests: An Exploratory Study
<p>White-box test generator tools rely only on the code under test to select test inputs, and capture the implementation's output as assertions. If there is a fault in the implementation, it could get encoded in the generated tests. Tool evaluations usually measure fault-detection capability using the number of such fault-encoding tests. However, these faults are only detected, if the developer can recognize that the encoded behavior is faulty. We designed an exploratory study to investigate how developers perform in classifying generated white-box test as faulty or correct. We carried out the study in a laboratory setting with 54 graduate students. The tests were generated for two open-source projects with the help of the IntelliTest tool. The performance of the participants were analyzed using binary classification metrics and by coding their observed activities. The results showed that participants incorrectly classified a large number of both fault-encoding and correct tests (with median misclassification rate 33% and 25% respectively). Thus the real fault-detection capability of test generators could be much lower than typically reported, and we suggest to take this human factor into account when evaluating generated white-box tests.<br> <br> This material contains the dataset and the recorded videos of the study.</p>
ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data. Additional Datasets.
<p>Data supporting the main figures of the "ParaMask, a new method to identify multicopy genomic regions, corrects major biases in whole-genome sequencing data" manuscript and a copy of the ParaMask software and scripts for analysis, and SV calls from longreads. README files are included.</p>
Data, code and supplementary plots for "A hierarchical spline model for correcting and hindcasting temperature data"
<p>Data, code and supplementary plots for the paper "A hierarchical spline model for correcting and hindcasting temperature data". Please see README.txt for detailed description of the files and relevant instructions.</p>
X-ray beam characterization of an aberration-corrected pair of multilayer Laue lenses with ptychography
<p>This data set is split over three zip archives. Each archive contains a scanning coherent X-ray diffraction (ptychography) data set recorded at an X-ray energy of 16.2 keV. A crossed pair of multilayer Laue lenses (MLL) is used to focus the beam and scan a Siemens star test sample. Each data set includes a configuration file and scan position file. In addition, the final result of the obtained ptychographic reconstruction is included.</p><p><strong>Description of the three data sets:</strong></p><ul><li>scan_00086: X-ray beam characterization of the MLL. On this data set the design of the refractive phase corrector was based upon.</li><li>scan_00338: X-ray beam characterization of the MLL four days after scan_00086 without phase corrector.</li><li>scan_00346: X-ray beam characterization of the MLL with refractive phase corrector.</li></ul><p><strong>Additional information:</strong></p><p>The diffraction patterns can be found in the 'eiger4m_01' folder. They are split up over multiple h5 files and located in the group '/entry/data/data'. The assignment of diffraction patterns to scan positions can be found in the positions.txt file. All relevant input parameters for ptychography are located in the 'input' group in the ptycho.conf files. The reconstruction results are in the European Data Format (EDF).</p><p><strong>The data set has been published in:</strong></p><p>F. Seiboth, A. Kubec, A. Schropp, S. Niese, P. Gawlitza, J. Garrevoet, V. Galbierz, S. Achilles, S. Patjens, M. E. Stuckelberger, C. David, and C. G. Schroer, "Rapid aberration correction for diffractive X-ray optics by additive manufacturing," Optics Express 30(18), 31519 (2022).</p>
Data from: Evaluation of different bias correction methods for dynamical downscaled future projections of the California Current Upwelling System
<p class="Abstract">Biases in global Earth System Models (ESMs) are an important source of errors when used to obtain boundary conditions for regional models. Here we examine historical and future conditions in the California Current System (CCS) using three different methods to force the regional model: (1) interpolation of ESM output to the regional grid with no bias correction; (2) a "seasonally-varying" delta method that obtains a season-dependent mean climate change signal from the ESM for a 30-year future period; and (3) a "time-varying" delta method that includes the interannual variability of the ESM over the 1980–2100 period. To compare these methods, we use a high-resolution (0.1˚) physical-biogeochemical regional model to dynamically downscale an ESM projection under the RCP8.5 emission scenario. Using different downscaling methods, the sign of future changes agrees for most of the physical and ecosystem variables, but the spatial patterns and magnitudes of these changes differ, with the seasonal- and time-varying delta simulations showing more similar changes. Not correcting the ESM forcing leads to amplification of biases in some ecosystem variables as well as misrepresentation of the California Undercurrent and CCS source waters. In the non-bias corrected and time-varying delta simulations, most of the ecosystem variables inherit trends and decadal variability from the ESM, while in the seasonally-varying delta simulation, the future variability reflects the observed historical variability (1980–2010). Our results demonstrate that bias correcting the forcing prior to downscaling improves historical simulations and that the bias correction method may impact the spatial and temporal variability of future projections. </p>
Dataset for Aberration-Corrected STEM to Determine the Surface Coverage and Distribution of Immobilized Molecular Complexes
<p>Raw image data used for the paper "Aberration-Corrected STEM to Determine the Surface Coverage and Distribution of Immobilized Molecular Complexes".</p>
Data for: The meta-analysis of the effects of spatial sampling bias correction on presence only species distribution models
<p>This dataset contains information extracted from 70 studies identified through a systematic review of the peer-reviewed literature (Web of Science and SCOPUS databases both searched on the 13/02/2023) to evaluate the effect of spatial sampling bias correction methods in presence-only species distribution models.</p>
Detrended and corrected GNSS data from Lake Taupō, New Zealand, 2014–2021
<p>If using this data please cite the following publication:</p> <p>Schuler, J., Hreinsdóttir, S., Illsley-Kemp, F., Holden, C., Townend, J., Villamor, P. The response of Taupō Volcano to the M7.8 Kaikōura Earthquake. <em>Journal of Geophysical Research - Solid</em> <em>Earth. </em>2024.</p> <p>Also refer to this publication for full details on the methods used to derive the dataset. </p>
Replication Data for the Paper "Is there a secular decline in disruptive patents? Correcting for measurement bias"
<p><strong>Working Paper Title: The Illusive Slump of Disruptive Patents</strong></p> <p>The repository contains replication data and scripts for the paper: </p> <p>Jeffrey T. Macher, Christian Rutzer, Rolf Weder,<br>Is there a secular decline in disruptive patents? Correcting for measurement bias,<br>Research Policy,<br>Volume 53, Issue 5,<br>2024,<br>104992,<br>ISSN 0048-7333,<br>https://doi.org/10.1016/j.respol.2024.104992</p> <p>The core component of the repository is the file `replication_file.R` which is an R script to replicate all figures and tables of the paper.</p> <p>The script relies on multiple data files. The datasets contain CD-values for granted USPTO utility patents for the years 1976-2016. All data is available in two formats, `.fst' (from the R package fst) and `.csv'.</p> <p> </p> <p><strong>Dataset Descriptions</strong></p> <p><strong>Main Datasets:</strong></p> <p>1. `dat_cd5_no_trunc`: This dataset contains the CD5 index of patents based on a method without truncation. </p> <p>2. `dat_cd5_trunc_1975`: This dataset contains the CD5 index of patents based on a truncation method as in Park et al. (Nature, 2023).</p> <p>3. `dat_cd5_no_trunc_app_adj`: This dataset contains the CD5 index of patents based on a method without truncation and including citations to patent applications granted by 2021. </p> <p> </p> <p><strong>Additional Datasets:</strong></p> <p>4. `dat_cd5_trunc_1985`: This dataset contains the CD5 index of patents based on a truncation of all backward citations to patents published before 1985.</p> <p>5. `dat_cd5_trunc_1995`: This dataset contains the CD5 index of patents based on a truncation of all backward citations to patents published before 1995.</p> <p>6. `dat_cd5_no_trunc_app`: This dataset contains the CD5 index of patents based on a method without truncation and including citations to patent applications granted by 2021, as well as those not yet granted. </p> <p>7. `age_bwc_untrunc`: This dataset contains the age of backward citations using untruncated data.</p> <p>8. age_bwc_trunc: This dataset contains the age of backward citations using truncated data as in Park et al. (Nature, 2023).</p> <p>9. `age_bwc_untrunc_app_adj`: This dataset contains the age of backward citations using untruncated data and considering citations of patent applications granted until 2021.</p> <p>10. `dat_cd10_trunc_1975`: This dataset contains the CD10 index of patents based on a truncation method as in Park et al. (Nature, 2023). </p> <p>11. `dat_cd10_no_trunc_app_adj`: This dataset contains the CD10 index of patents based on a method without truncation and including citations to patent applications granted by 2021. </p> <p>12. `dat_cd2021_trunc_1975`: This dataset contains the CD index as of 2021 of patents based on a truncation method as in Park et al. (Nature, 2023). </p> <p>13. `dat_cd2021_no_trunc_app_adj`: This dataset contains the CD index as of 2021 of patents based on a method without truncation and including citations to patent applications granted by 2021. </p> <p> </p> <p>To successfully run the `replication_file.R' script, make sure all data files are in the directory and the `mainDir1' variable at the beginning of the script is set to the correct path to where the data is stored. In addition, set the `mainDir2' variable to the folder where you want to store the figures created by the script. </p> <p>If you have any questions, please contact christian.rutzer@unibas.ch</p>
Fecal standing crop with real time correction using scat detection dogs to estimate population density
<p>Population density is fundamental information for assessing the conservation status of species and support management and conservation actions for in situ populations, but is unknown for many forest species due to their difficulty in detection. The Fecal Standing Crop (FSC) method using detection dogs is an alternative for cryptic or elusive species. An intrinsic difficulty of FSC is the ability to find fecal samples in the field and to estimate the probability of which feces detection is influenced by degradation due to climatic conditions. Our goal was to propose a concurrent FSC parameter estimation using a scat detection dog under different climatic conditions and apply those parameters in a wild deer population. Ten fecal samples of gray brocket deer (Subulo gouazoubira) were placed weekly in a transect (24 x 1200 m) in both dry and wet seasons (12 weeks each). A scat detection dog was then employed to find experimental fecal samples to determine the FSC parameters that were subsequently used with naturally occurring fecal samples (also dog-detected) to estimate population density. The oldest dog found samples were 21 (Dry) and seven (Wet) days after placement, resulting in dog efficiency of 23% (Dry) and 30% (Wet). Adjusting the model to account for efficiency and scat durability, we estimated similar, seasonal, densities of 4.54 individuals km-2 (SD = 2.21, Dry) and 5.52 indiv. km-2 (SD = 3.71, Wet).</p> <p><em>Synthesis and applications:</em> Our results demonstrate that our concurrent methodology corrected the effects of weather and habitat on FSC parameters thereby allowing for accurate population density estimation. Additionally, this method can provide reasonably precise density estimates with a logistically feasible sample size, as demonstrated by simulation. Following our recommendations, this method allows a reliable estimate of population density because it incorporates any influence of study area, dog ability, and climate in fecal sample detection, providing fundamental information for the conservation of many cryptic and elusive species.</p>
Bias-corrected EURO-CORDEX daily temperature and precipitation dataset for Hungary
<p>These datasets contain bias-corrected regional climate model outputs for daily precipitation, near surface mean-, minimum- and maximum temperature for the historical period 1993-2005 on a regular 0.11°x0.11° lon/lat grid (between latitudes 45.6825°N and 48.6525°N, and longitudes 15.9075°E and 22.9475°E).</p> <p> </p> <p>The datasets contain daily outputs of the following high-resolution (0.11°) regional climate models from the framework of EURO-CORDEX (Jacob et al., 2014):</p> <p>-CCLM</p> <p>-HIRHAM</p> <p>-RACMO</p> <p>-RCA</p> <p>-REMO</p> <p> </p> <p>The reference dataset is HUCLIM, which covers Hungary for the period 1971-2022 (downloaded in 2023) produced by the HungaroMet Hungarian Meteorological Service.</p> <p>File format: NetCDF</p> <p>All bias-corrected data produced by the use of HUCLIM have been created following the work of Mezghani et al. (2017).</p> <p> </p> <p>References:</p> <p>Jacob, D., Petersen, J., Eggert, B., Alias, A., Christensen, O.B., Bouwer, L.M., Braun, A., Colette, A., Déqué, M., Georgievski, G., Georgopoulou, E., Gobiet, A., Menut, L., Nikulin, G., Haensler, A., Hempelmann, N., Jones, C., Keuler, K., Kovats, S., Kröner, N., Kotlarski, S., Kriegsmann, A., Martin, E., van Meijgaard, E., Moseley, C., Pfeifer, S., Preuschmann, S., Radermacher, C., Radtke, K., Rechid, D., Rounsevel, M., Samuelsson, P., Somot, S., Soussana, J.-F., Teichmann, C., Valentini, R., Vautard, R., Weber, B. and Yiou, P. (2014) EURO-CORDEX New high resolution climate change projections for European impact research. Reg. Environ. Change, 14, 563–578. <a href="https://doi.org/10.1007/s10113-013-0499-2" target="_blank" rel="noopener">https://doi.org/10.1007/s10113-013-0499-2</a></p> <p>Mezghani, A., Dobler, A., Haugen, J.E., Benestad, R.E., Parding, K.M., Piniewski, M., Kardel, I. and Kundzewicz, Z.W. (2017) CHASE-PL Climate Projection dataset over Poland – bias adjustment of EURO-CORDEX simulations. Earth Syst. Sci. Data, 9, 905–925. <a href="https://doi.org/10.5194/essd-9-905-2017" target="_blank" rel="noopener">https://doi.org/10.5194/essd-9-905-2017</a></p> <p> </p>
Data folder for the Batch Effect correction part of OBRWR
<p>Data folder for the batch effect correction of phosphoproteomics assay conducted in Dr. Destaing's lab.</p> <p>Batch effect assessment using PCA.</p> <p>Proposing two correction methods : one using PCA, the second (and the one used) using MLM.</p>
Data from "Hardware-efficient quantum error correction using concatenated bosonic qubits"
<p>Includes data for characterizing the bit-flip and logical phase-flip rates of the logical memory. See the README for more details.</p>
Fig. 2 in Arundo micrantha Lam. (Poaceae), the correct name for Arundo mauritanica Desf. and Arundo mediterranea Danin
Fig. 2. – The Arundo micrantha Lam. diagnosis.
Dataset provided with "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction"
<p>Dataset and scripts used in "Artifacts in Optical Projection Tomography Due to Refractive-Index Mismatch: Model and Correction".</p>
Dataset: Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography
<p>The ptychography datasets and processed wavefront data in support of the publication "Two-dimensional wavefront characterization of adaptable corrective optics and Kirkpatrick–Baez mirror system using ptychography". File are in the HDF format and contain a number of datasets detailed below. If you require more information, please contact the corresponding author of the publication or thomas.moxham@eng.ox.ac.uk</p>
Data from: Demographic correction – a tool for inference from individuals to populations
<p>Estimation of responses of organisms to their environment using experimental manipulations, and comparison of such responses across sets of species, is one of the primary tools in ecology research. The most common approach is to compare response of a single life stage of species to an environmental factor and use this information to draw conclusions about population dynamics of these species. Such approach ignores the fact that interspecific fitness differences measured at a single life stage are not directly comparable and cannot be extrapolated to lifetime fitness of individuals and thus species' population dynamics. Comparison of one life stage only while omitting demographic information can strongly bias conclusions, both in experimental studies with a few species, and in large comparative studies.</p> <p>We illustrate the effect of this omission using both an exaggerated fictitious example, and biological data on congeneric species differing in their demography. We are showing, taking simple assumptions, that different demography can completely revert conclusions reached by a comparison based on an experiment focusing on a single life stage.</p> <p>We show that a "demographic correction", namely translating observed effects into differences in outcomes of demographic models, is a solution to this problem. It requires turning the detected effects from the experiment into changes of transition probabilities of projection matrix models. Although such solution is limited by the low number of species with demographic data available, we believe that existing data (and data likely to be collected in the near future) permit at least approximate handling of this problem.</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.