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.
25
datasets available to search
ShareScore release 0.9.0
Dataset results
25 results for “DCA”
DCA and GNMDS output for 4640 subplots and 95 vascular plant species in four alpine grasslands
<p>Ordination output from detrended correspondence analysis (DCA) and global non-metric multidimensional scaling (GNMDS).</p> <p>Analyses were performed in R with the <em>vegan</em> package (Oksanen 2022) for the entire data set of 4630 subplots and 95 species' occurrences ('global', indicated by global or missing site name in file names), and for each of four sites: Skjellingahaugen (skj), Gudmedalen (gud), Låvisdalen (lav), and Ulvehaugen (ulv). Access .Rds files with readRDS in R/RStudio.</p> <p>For GNMDS files, k indicates the chosen number of dimensions. See GitHub repository for scripts to produce and perform further analysis with the files in this archive.</p> <p>Analyses performed by EL with scripts based on originals by RH.</p>
The Multi-Temporal Dual Channel Algorithm (MT-DCA)
<p>I) SUMMARY</p> <p>This soil moisture and vegetation optical depth product is called the Multi-Temporal Dual Channel Algorithm (MT-DCA). It retrieves surface soil moisture and vegetation optical depth (directly related to total water volume in the vegetation canopy) from <a href="https://nsidc.org/data/SPL1CTB_E">SMAP level 1C brightness temperature</a> observations using a robust estimation technique. It is an in-house MIT algorithm and is not an official SMAP product. The data are freely available on 9km and 36km grids from April 2015 to July 2021 in daily time steps.</p> <p>No co-authorship is required for use of this data in publications. However, to properly acknowledge the dataset when publishing any research using the MT-DCA, we ask data users to (1) cite the DOI as an in-text citation and/or in the data acknowledgements in any publication and (2) reference <a href="https://www.sciencedirect.com/science/article/pii/S0034425717302961">Konings et al. (2017</a>) when referring to the MT-DCA in the text. Feel free to send us an email at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a> to let us know how you are using the data. </p> <p>The version 5 update is a re-implementation of the MT-DCA using the updated SMAP L1C brightness temperatures. It extends the data through July 2021.</p> <p>II) CONTACT</p> <p>For questions, please email Andrew Feldman at <a href="mailto:afeld24@mit.edu">afeld24@mit.edu</a>.</p> <p>III) ALGORITHM DESCRIPTION</p> <p>The algorithmic approach uses both horizontally and vertically polarized brightness temperatures to retrieve soil moisture and VOD simultaneously. The key innovation of the MT-DCA is that it recognizes that classical dual-channel algorithms are under-determined: brightness temperature observations are correlated and cannot retrieve two unknowns (soil moisture and VOD) (as illustrated in Konings et al, RSE 2016). This creates amplifying errors in retrievals from snapshot dual-channel algorithms. The MT-DCA uses a viable assumption that VOD changes more slowly than soil moisture between overpasses, and uses information from multiple SMAP overpasses to stabilize the retrieval. It is considered a regularization approach similar to the Sobolev Norm regularization. Specifically, this approach is applied to each temporally adjacent pair of overpasses (for SMAP, two overpasses approximately 2-3-days apart), which includes four brightness temperature measurements. For each overpass pair, the soil moisture at both overpasses is retrieved, along with a constant VOD for both overpasses. This leads to two retrievals of each of soil moisture and VOD at any given overpass time: one where the parameters are retrieved using additional TB information from the overpass before and one from the overpass after. Both retrievals of VOD and soil moisture values at each overpass are averaged. Ultimately, VOD is not held constant, but rather is slowed in time between overpasses. A second key innovation of the MT-DCA is that, because the retrievals are no longer under determined, it is also possible to retrieve a constant single scattering albedo for each pixel. The single scattering albedo is estimated through model selection of the value of the parameter that minimizes the sum of all overpass cost functions. The retrieved albedo is also included in the files here. VOD is reported at nadir.</p> <p>The single scattering albedo is assumed constant over the full record of SMAP data, as is currently accepted practice across approaches with SMAP, SMOS, and AMSR. There is a high amount of computational power required to retrieve an albedo over more than three years of SMAP data. Therefore, an adjustment was made: the single scattering albedo was retrieved over the third year of SMAP data (April 1st, 2017 to March 31st, 2018). This constant value was then applied to the other years without requiring the albedo optimization loop. Tests across many individual pixels revealed that albedo in the third year does not differ greatly from albedo over all years and the other individual years. </p> <p>The algorithm is described in more detail in Konings et al. (2017). The algorithm is based on principles explained in more detail in Konings et al. (2016), which describes the original algorithm development using Aquarius observations. See also the related Konings et al. (2015) publication for quantitative justification for the approach. While the dataset has not been officially validated, the MT-DCA soil moisture retrievals show in-situ comparison statistics similarly to the official baseline SMAP soil moisture product (SMAP soil moisture retrieval in-situ assessment can be found in Chan et al. (2016)). Finally, the MT-DCA vegetation optical depth retrievals are not validated due to only sparsely available ground information related to vegetation water content. Nevertheless, information about error propagation into the MT-DCA soil moisture and VOD retrievals as well as VOD error reductions using the MT-DCA regularization technique can be found in Feldman et al. (2021).</p> <p>Chan, S.K., Bindlish, R., O’Neill, P.E., Njoku, E., Jackson, T., Colliander, A., Chen, F., Burgin, M., Dunbar, S., Piepmeier, J., Yueh, S., Entekhabi, D., Cosh, M.H., Caldwell, T., Walker, J., Wu, X., Berg, A., Rowlandson, T., Pacheco, A., McNairn, H., Thibeault, M., Martinez-Fernandez, J., Gonzalez-Zamora, A., Seyfried, M., Bosch, D., Starks, P., Goodrich, D., Prueger, J., Palecki, M., Small, E.E., Zreda, M., Calvet, J.C., Crow, W.T., Kerr, Y., 2016. Assessment of the SMAP Passive Soil Moisture Product. IEEE Trans. Geosci. Remote Sens. 54, 4994–5007. <a href="https://doi.org/10.1109/TGRS.2016.2561938">https://doi.org/10.1109/TGRS.2016.2561938</a></p> <p>Feldman, A.F., D. Chaparro, and D. Entekhabi (2021). Error propagation in microwave soil moisture and vegetation optical depth retrievals. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. In Press.</p> <p>Konings, A.G., M. Piles, N. Das, and D. Entekhabi (2017). L-band vegetation optical depth and effective scattering albedo estimation from SMAP. Remote Sensing of Environment, 198:460-470. <a href="https://doi.org/10.1016/j.rse.2017.06.037">https://doi.org/10.1016/j.rse.2017.06.037</a></p> <p>Konings, A.G., M. Piles, K. Rötzer, K.A. McColl, S. Chan, and D. Entekhabi (2016). Vegetation optical depth and scattering albedo retrieval using time-series of dual-polarized L-band radiometer observations. Remote Sensing of Environment. 172, 178-189. https://doi.org/10.1016/j.rse.2015.11.009</p> <p>Konings, A.G., K.A. McColl, M. Piles and D. Entekhabi (2015): How many parameters can be maximally estimated from a set of measurements? IEEE Geoscience and Remote Sensing Letters, 12(5), 1081-1085. https://doi.org/10.1109/LGRS.2014.2381641</p> <p>IV) QUALITY CONTROL</p> <p>Several conditions can create uncertainty in the MT-DCA retrievals including surface water bodies (lakes, rivers, coastal areas, etc.), radio frequency interference (RFI), highly sloped surfaces (mountainous regions), dense vegetation, frozen ground, and others. The MT-DCA removes time periods of frozen ground and removes pixels with water body fractions of greater than 0.5. SMAP L1C brightness temperatures are adjusted considering RFI and surface water body information. Nevertheless, the MT-DCA retrievals are purposefully not substantially quality controlled to increase the range of science applications of the data. Therefore, the retrievals are subject to uncertainty in regions where and times when these aforementioned issues occur. We suggest the data user familiarize themselves with quality flags in the SMAP algorithm theoretical basis document in <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>. Conservative quality control can be applied using SMAP quality flag information directly applicable to the dataset here. These quality flags can be downloaded from the SMAP official product files at <a href="https://nsidc.org/data/SPL3SMP_E">https://nsidc.org/data/SPL3SMP_E</a>.</p> <p>V) DATA FORMATTING AND FILE NAMES </p> <p>Data are provided in zipped folders in both netcdf4 (.nc) and matfile (.mat) formats. Each zipped folder contains soil moisture, vegetation optical depth, single scattering albedo, latitude, longitude, and time vector information. Note that as an update in Version 5, the zipped folders for 9km .mat files are separated into soil moisture and vegetation optical depth to reduce zip folder size. The other zipped folders still have all variables within them. These variables are provided at a 9km resolution as well as upscaled to 36km. For both .nc and .mat files, the 9km data are provided in 3-month periods with a naming convention of ‘YYYYMM_YYYYMM’ where YYYY is the 4-digit year, and MM is the 2-digit month. The first YYYYMM string represents the first month and the second YYYYMM string is the final month of the period. The 36km data are provided in 12-month periods with the same naming conventions in the file names.</p> <p>Retrievals are obtained from enhanced-resolution brightness temperatures from SMAP that are gridded at 9km. As such, they are on a 9km EASE2-grid. These retrievals are upscaled to 36km and gridded on a 36km EASE2 grid. Additional information and geolocation tools are available at <a href="https://nsidc.org/data/ease/ease_grid2.html">https://nsidc.org/data/ease/ease_grid2.html</a>. </p> <p>Information specific to folders with .nc and .mat formats is given below:</p> <p>a) NETCDF Files (.nc): The folders with netcdf files contain files with the convention MTDCA_YYYYMM_YYYYMM_Xkm_VX.nc where VX is the version number, Xkm is the grid scale, and YYYYMM strings are the first and last months of the range of data saved in the file. Soil moisture, vegetation optical depth, latitude, longitude, and time index information are provided in these files. A map of single scattering albedo for the full time series is saved in a separate file as MTDCA_OMEGA_Xkm_VX.nc along with latitude and longitude information.</p> <p>b) MATFILES (.mat): The folders with matfiles contain individual files for:</p> <ol> <li>Soil moisture: MTDCA_VX_SM_YYYYXX_YYYYXX_Xkm.mat</li> <li>Vegetation Optical Depth: MTDCA_VX_TAU_YYYYXX_YYYYXX_Xkm.mat</li> <li>Single Scattering Albedo: MTDCA_VX_OMEGA_Xkm.mat</li> <li>Latitude/Longitude: SMAPCenterCoordinatesXKM.mat</li> </ol> <p>A datevector variable in each soil moisture and vegetation optical depth file contains information on the year, month, and day corresponding to the timestep of each variable.</p>
FIGURE 9. DCA-1 in Simulating our ability to accurately detect abrupt changes in assemblage-based paleoenvironmental proxies
FIGURE 9. DCA-1 values observed when assemblages simulated at different event values are mixed with different proportions of the background assemblage. For A, the maximum DCA-1 value observed is shown; for B, the median DCA-1 value observed; and for C, the minimum DCA-1 value observed. In all cases, background assemblages are simulated at a DCA-1 value of -0.5 and are mixed with an event assemblage with a DCA-1 value as given on the x-axis.
Dichloroacetate (DCA) in Patients With Previously Treated Metastatic Breast or Non-Small Cell Lung Cancer (NSCL)
ClinicalTrials.gov study NCT01029925. IPD Sharing: Not stated. Countries: 1. Publications: 9.
Study of DCA (Dichloroacetate) in Combination With Cisplatin and Definitive Radiation in Head and Neck Carcinoma
ClinicalTrials.gov study NCT01386632. IPD Sharing: NO. Countries: 1. Publications: 1.
Interactions of the Ionic Liquid [C2C1Im][DCA] with Au(111) Electrodes: Interplay between Ion Adsorption, Electrode Structure and Stability
Open the record for dataset details and reuse information.
Dichloroacetate (DCA) for the Treatment of Pulmonary Arterial Hypertension
ClinicalTrials.gov study NCT01083524. IPD Sharing: Not stated. Countries: 2. Publications: 1.
The Safety and Efficacy of DCA for the Treatment of Brain Cancer
ClinicalTrials.gov study NCT00540176. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Clinical Trial to Evaluate Dichloroacetate (DCA) as a Treatment for Endometriosis-associated Pain
ClinicalTrials.gov study NCT04046081. IPD Sharing: YES. Countries: 1. Publications: 1.
Study of the Safety and Efficacy of Dichloroacetate (DCA) in Glioblastoma and Other Recurrent Brain Tumors
ClinicalTrials.gov study NCT01111097. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Trial of Dichloroacetate (DCA) in Glioblastoma Multiforme (GBM)
ClinicalTrials.gov study NCT05120284. IPD Sharing: NO. Countries: 1. Publications: 42.
Dichloroacetate (DCA) prevents cisplatin-induced nephrotoxicity without compromising its anti-cancer properties.
GEO Series GSE69652. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Effects of CDK8/19 mediator kinase inhibition by didehydrocortistatin A (dCA) and 15w on gene expression in HEK293 cells
GEO Series GSE200747. Homo sapiens. 18 samples. Type: Expression profiling by high throughput sequencing.
Honokiol Bis-Dichloroacetate (Honokiol DCA) Demonstrates Activity in Vemurafenib-Resistant Melanoma in Vivo.
GEO Series GSE76956. Homo sapiens. 9 samples. Type: Expression profiling by array.
A Phase I, Open-Labeled, Single-Arm, Dose Escalation, Clinical and Pharmacology Study of Dichloroacetate (DCA) in Patients With Recurrent and/or Metastatic Solid Tumours
ClinicalTrials.gov study NCT00566410. IPD Sharing: Not stated. Countries: 1. Publications: 0.
A Pilot Study of Rapid Haplotyping Procedure for Personalized Dosing of Dichloroacetate (DCA) in Healthy Volunteers
ClinicalTrials.gov study NCT02690285. IPD Sharing: NO. Countries: 1. Publications: 0.
Gene expression profiling in zebrafish embryos exposed to 3,4-dichloroaniline (3,4-DCA)
GEO Series GSE6228. Danio rerio. 6 samples. Type: Expression profiling by array.
Exposure of SKGT4 and HET-1A cell lines to deoxycholic acid (DCA)
GEO Series GSE13400. Homo sapiens. 32 samples. Type: Expression profiling by array.
Exposure of squamous esophageal cell line HET-1A to deoxycholic acid (DCA)
GEO Series GSE13378. Homo sapiens. 16 samples. Type: Expression profiling by array.
Differences in gene expression in CP-A cells treated with acid and deoxycholic acid (DCA) and those without acid
GEO Series GSE217263. Homo sapiens. 2 samples. Type: Expression profiling by array.
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.