Find research datasets worth reusing

opencc-by-4.0Mar 2022View details →

iEEG Data for "Functional Group Bridge for Simultaneous Regression and Support Estimation"

The repository contains analysis scripts and data used in Wang Z, Magnotti J, Beauchamp MS, Li M. Functional Group Bridge for Simultaneous Regression and Support Estimation, 2020. The data contains high-gamma brain responses across 8 subjects from "congruency" audio-visual experiment.

opencc-by-4.0Jul 2022View details →

AMOC reconstruction between 1981 and 2016 from hydrographic data using an empirical linear regression model from Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, https://doi.org/10.5194/os-17-285-2021, 2021.

Dataset used to create Figure 8 in Worthington et al., 2021 (https://doi.org/10.5194/os-17-285-2021). Details of the data and methods can be found in the journal article. Worthington, E. L., Moat, B. I., Smeed, D. A., Mecking, J. V., Marsh, R., and McCarthy, G. D.: A 30-year reconstruction of the Atlantic meridional overturning circulation shows no decline, Ocean Sci., 17, 285–299, <a href="https://doi.org/10.5194/os-17-285-2021">https://doi.org/10.5194/os-17-285-2021</a>, 2021.

opencc-by-4.0Jun 2024View details →

Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany

The dataset consists of particulate matter pollution concentration, measured in three localities - Hermsdorf, Charlottenburg and Adlershof, in Berlin, Germany. <a href="../api/records/10076056/draft/files/pm25_summer_rd_30s.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer_rd_30s.geojson</a> shows the observed PM2.5 concentration in a 30 second interval. <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> shows the concentrations shown is the local concentration (observed concentration - background concentration) in a 30 second interval. The background concentration is calculated as the lowest 5 percentile of the measured concentration for each measurement round.  <a href="../api/records/10076056/draft/files/PM2.5_lc_max.geojson/content" target="_blank" rel="noopener noreferrer">PM2.5_lc_max.geojson</a> contains the information from <a href="../api/records/10076056/draft/files/pm25_summer.geojson/content" target="_blank" rel="noopener noreferrer">pm25_summer.geojson</a> in a 25m resolution. Additionally, it contains the land use information for each coordinate. The original publication providing all necessary background information on study sites, methodology and data processing is the following: Venkatraman Jagatha, J., T. Sauter, C. Schneider (2024): Parsimonious Random-Forest-Based Land-Use Regression Model Using Particulate Matter Sensors in Berlin, Germany. MDPI Sensors, 24(13), 4193, DOI: 10.3390/s24134193. The paper is fully open access and can be downloaded at <a href="https://doi.org/10.3390/s24134193">https://doi.org/10.3390/s24134193</a>. Information on working with geojson file can be found under <a href="https://geojson.readthedocs.io/en/latest/">GeoJSON</a> .

opencc-by-4.0Mar 2023View details →

Annual time series of global VIIRS nighttime lights for 2000-2024 at 500-m spatial resolution extrapolated using logistic regression

The <a href="https://eogdata.mines.edu/products/vnl/">Annual Visible Night Light (VNL) V2</a> (VIIRS) images at 500-m spatial resolution for the period 2012 to 2024 (Elvidge et al., 2021) have been used to extrapolate the values backwards for years 2000–2011. This was done by fitting a logistic regression (per pixel) and then predicting the values for the previous years (see nightlights_stack_500m.R). After consistent time-series have been produced, I also derived the difference between year 2024 and year 2000 (nightlights.difference_viirs.v21_m_500m_s_2000_2024_go_epsg4326_v20230318.tif): this shows average rate of change for the 25 years period. Use with caution: extrapolation of values can lead to artifacts. For most of the land surface, however, it appears that the growth of night lights follows exponential growth function and hence nights in the past can be represented accurately by fitting decay / logistic regression function. Original values from the Annual VNL V2 product have been converted from 0–200 to 0–2000 scale and are available as Cloud-Optimized GeoTIFFs. Principal components (PC1, PC2, PC3, PC4) were derived using SAGA GIS (sums-of-squares-and-cross-products matrix) method. The first PC1 usually matches the long-term mean value, PC2 matches the 1st derivation in values. File "nightlights_dmsp.v10_m_1km_s_19920101_20241231_go_epsg4326_v20251006.tif" contains 33 years 1992 to 2024, but at 1 km resolution. To cite the Annual VNL V2, please use: <ul> <li>Elvidge, C. D., Zhizhin, M., Ghosh, T., Hsu, F. C., & Taneja, J. (2021). <a href="https://doi.org/10.3390/rs13050922">Annual time series of global VIIRS nighttime lights derived from monthly averages: 2012 to 2019</a>. Remote Sensing, 13(5), 922. https://doi.org/10.3390/rs13050922</li> </ul> Historic night light images (1 km resolution) are also available from <a href="https://doi.org/10.6084/m9.figshare.9828827.v10">Figshare</a>: <ul> <li>Li, X., Zhou, Y., Zhao, M., & Zhao, X. (2020). <a href="https://doi.org/10.1038/s41597-020-0510-y">A harmonized global nighttime light dataset 1992–2018</a>. Scientific data, 7(1), 168. https://doi.org/10.1038/s41597-020-0510-y</li> </ul>

OpenNeuro48/100

Effects of Phase Regression on High-Resolution Functional MRI of the Primary Visual Cortex

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →

opencc-by-4.0Jun 2021View details →

Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals

Supplementary Material for: Emerling C.A., Springer M.S., Gatesy J., Jones Z., Hamilton D., Xia-Zhu D., Collin M.A., and Delsuc F. (2021). Genomic evidence for the parallel regression of melatonin synthesis and signaling pathways in placental mammals. Open Research Europe 1:75. doi:10.12688/openreseurope.13795.1.   Supplementary File Legends: - Supplementary_Figure_S1.pdf: AANAT PAML ‘master model’ showing branch categories, corresponding to “Model 1: 24 ratio” in Supplementary Table S7. - Supplementary_Figure_S2.pdf: ASMT PAML ‘master model’ showing branch categories, corresponding to “Model 2: 24 ratio” in Supplementary Table S8. - Supplementary_Figure_S3.pdf: MTNR1A PAML ‘master model’ showing branch categories, corresponding to “Model 1: 27 ratio” in Supplementary Table S9. - Supplementary_Figure_S4.pdf: MTNR1B PAML ‘master model’ showing branch categories, corresponding to “Model 1: 46 ratio” in Supplementary Table S10. - Supplementary_Figure_S5.pdf: RAxML AANAT gene tree. Numbers at nodes correspond to bootstrap support values. - Supplementary_Figure_S6.pdf: RAxML ASMT gene tree. Numbers at nodes correspond to bootstrap support values. - Supplementary_Figure_S7.pdf: RAxML MTNR1A+MTNR1B tree. Numbers at nodes correspond to bootstrap support values. - Supplementary_Figure_S8.pdf: Supporting data showing the inactivation of MTNR1A exon 2 in cetaceans. Read Supplementary Table S13 for further details. - Supplementary_Figure_S9.pdf: Supporting data showing the inactivation of ASMT in spalacids and Fukomys damarensis. Read Supplementary Table S13 for further details. - Supplementary_Figure_S10.pdf: Supporting data showing the inactivation of MTNR1A in hyracoids and Cyclopes didactylus. Read Supplementary Table S13 for further details. - Supplementary_Figure_S11.pdf: Supporting data showing the inactivation of MTNR1A in sirenians. Read Supplementary Table S13 for further details. - Supplementary_Figure_S12.pdf: Supporting data showing the inactivation of AANAT in sirenians and a polymorphic premature stop codon in exon 5 of ASMT in Trichechus manatus. Read Supplementary Table S13 for further details. - Supplementary_Figure_S13.pdf: Supporting data showing the inactivation of MTNR1A in Condylura cristata. Read Supplementary Table S13 for further details. - Supplementary_Figure_S14.pdf: Supporting data showing the inactivation of MTNR1A in Phataginus tricuspis. Read Supplementary Table S14 for further details. - Supplementary_Figure_S15.pdf: PAML AANAT results, Model 1: 24 ratio (see Supplementary Table S7). - Supplementary_Figure_S16.pdf: PAML ASMT results, Model 2: 24 ratio (see Supplementary Table S8). - Supplementary_Figure_S17.pdf: PAML MTNR1A results, Model 1: 27 ratio (see Supplementary Table S9). - Supplementary_Figure_S18.pdf: PAML MTNR1B results, Model 1: 46 ratio (see Supplementary Table S10). - Supplementary_Table_S1.xlsx: List of species examined in this study and the sources of the genes. Source key: WGS: Sequences derived from NCBI's Whole Genome Shotgun database, with accession prefix provided; Whole Genome Sequencing of Short Reads: whole genomes were sequenced using short-read technologies. The methodologies varied for the species, and will be or have been published with other projects, so please contact the author(s) for information on the specific methodology and samples used (Xenarthrans, Proteles cristatus, Otocyon megalotis: Frédéric Delsuc, e-mail: Frederic.Delsuc@umontpellier.fr; Crocodylians: John Gatesy, e-mail: jgatesy@amnh.org; Dugong dugon: Mark Springer, e-mail: mark.springer@ucr.edu; SRA: sequences derived from NCBI's Sequence Read Archive; GenBank: sequences derived from NCBI's nucleotide collection; Bowhead Whale Genome Resource: sequences derived from http://www.bowhead-whale.org; Ensembl: sequences derived from Ensembl genome browser (www.ensembl.org)l; Discovar de novo: sequences derived genomes assembled via Discovar de novo  (<a href="https://software.broadinstitute.org/software/discovar/blog/">https://software.broadinstitute.org/software/discovar/blog/</a>). Coverage: indicates coverage of the whole genome (reported in NCBI or other source) or individual genes (derived from short read mapping). Scaffold and contig N50: reported in NCBI or other source. - Supplementary_Table_S2.xlsx: Accession numbers and functionality of AANAT in species examined. If Accession # indicated as “New”, sequence generated for this study and can be found in Supplementary Dataset S1. Parentheses after accession number indicates coordinates for sequence on the contig / scaffold. Exon colors code for the following: green = putatively functional; yellow = missing (e.g., negative BLAST results, negative mapping results); pink = one or more inactivating mutations found. Abbreviations for mutations are as follows: del = deletion; ins = insertion; start = start codon mutation; stop = premature stop codon; ? = ambiguity whether the mutation is shared among all members of the clade. Abbreviations in brackets following an inactivating mutation indicate shared inactivating mutation. Key for each abbreviation follows: Bacu = Balaenoptera acutorostrata; BALA = Balaenidae; BALAEN = Balaenopteridae; Bbon = Balaenoptera bonaerensis; CAB = Cabassous; Ccap = Cebus capucinus; CETA = Cetacea; CHLAM = Chlamyphoridae; CHOL = Choloepus; Cjac = Callithrix jacchus; CING = Cingulata; DASY = Dasypodidae; DELP = Delphinidae; DERM = Dermoptera; Erob = Eschrichtius robustus; INIA = Inia; FOLI = Folivora; GALE = Galeopterus; LIPO = Lipotes; Lobl = Lagenorhynchus obliquidens; MANI = Manidae; MONO = Monodontidae; MYRM = Myrmecophagidae; MYST = Mysticeti; NPP = Not present in Platanista or Physeteroidea, but present in other Odontocetes; NPZ = Not present in Ziphiidae, but present in other Odontocetes; Oorc = Orcinus orca; PEUT = Tolypeutinae; PHOC = Phocoenidae; PHOL = Pholidota; PHOR = Chlamyphorinae; PILO = Pilosa; PHYS = Physeteroidea; PONT = Pontoporia; Schi = Sousa chinensis; SIRE = Sirenia; Tadu = Tursiops aduncus; TOLY = Tolypeutes; VERM = Vermilingua; XEN = Xenarthra. - Supplementary_Table_S3.xlsx: Accession numbers and functionality of ASMT in species examined. See Table S2 caption for details. - Supplementary_Table_S4.xlsx: Accession numbers and functionality of MTNR1A in species examined. See Table S2 caption for details. - Supplementary_Table_S5.xlsx: Accession numbers and functionality of MTNR1B in species examined. See Table S2 caption for details. - Supplementary_Table_S6.xlsx: Codon frequency model selection. These are the results from one ratio dN/dS analyses using different codon frequency models. AIC = Akaike Information Criterion. - Supplementary_Table_S7.xlsx: Results of AANAT PAML dN/dS analyses for mammals. Model: BG = branch(es) grouped with background; fixed 1 = branch(es) fixed at 1. p’-value: p-value after Holm-Bonferroni correction for multiple testing. Model Comparison: if model comparison yields statistically significant differences (p < 0.05), model comparison bolded and given green background; if model comparison is still significant after Holm-Bonferroni correction, asterisk (*) added. For most models, w only shown for branch(es) of interest. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S1. - Supplementary_Table_S8.xlsx: Results of ASMT PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S2. - Supplementary_Table_S9.xlsx: Results of MTNR1A PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S3. - Supplementary_Table_S10.xlsx: Results of MTNR1B PAML dN/dS analyses for mammals. Refer to Table S7 caption for additional details. Numbers in front of taxonomic names in first row correspond to numbers in the master model shown in Supplementary Figure S4. - Supplementary_Table_S11.xlsx: Results of PAML analyses for sauropsids. - Supplementary_Table_S12.xlsx: Results of BLASTing and mapping short reads from Alligator mississippiensis RNA sequencing experiments. - Supplementary_Table_S13.xlsx: Supporting data for validating putative inactivating mutations. Validating data came from four general sources of information: mutations shared by more than one species within a clade, mutations shared by two sources of sequencing data for the same species, mutations validated by coverage of mapped short reads and statistically elevated dN/dS ratio estimates. For additional details, see Supplementary Tables S2–S5 and S7–S10, as well as Figure 2 and Supplementary Figures S8–S18. - Supplementary_Dataset_S1.txt: Genomic alignments in fasta format used to determine the pseudogene/functional status of all four melatonin genes in different taxonomic groups. - Supplementary_Dataset_S2.txt: Alignment of AANAT in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML.  - Supplementary_Dataset_S3.txt: Alignment of ASMT in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML.  - Supplementary_Dataset_S4.txt: Alignment of MTNR1A and MTNR1B in phylip format used in maximum likelihood phylogenetic reconstruction with RAxML.  - Supplementary_Dataset_S5.txt: Codon alignments of AANAT used in selection pressure analyses with PAML.  - Supplementary_Dataset_S6.txt: Codon alignments of ASMT used in selection pressure analyses with PAML. - Supplementary_Dataset_S7.txt: Codon alignments of MTNR1A used in selection pressure analyses with PAML. - Supplementary_Dataset_S8.txt: Codon alignments of MTNR1B used in selection pressure analyses with PAML. - Supplementary_Dataset_S9.txt: Tree topologies in newick format used in selection pressure analyses with PAML.

opencc-by-4.0May 2022View details →

Data sets for Span-level SNR Regression in EONs

<ul> <li>DS1: the symbol rate is fixed and equals 64 Gbaud, and the channel loading factor is selected from [25 − 100];</li> <li>DS2: the symbol rate and channel occupancy status is randomly selected (uniformly distributed) from {32, 64, 96 (GBaud) and {0, 1}, respectively;</li> <li> DS3: both symbol rate and the channel loading factor are fixed and equal to 64 GBaud and 25%, respectively.</li> </ul>

opencc-by-3.0Jul 2021View details →

Files from TCGA-KIRC Study for Body Part Regression Tutorial

The data here are in whole based upon data generated by the TCGA Research Network: <a href="https://cancergenome.nih.gov/">http://cancergenome.nih.gov/</a>. The DICOM files from the <a href="https://wiki.cancerimagingarchive.net/display/Public/TCGA-KIRC#580038695f8cd691bda43dda71b4093c69c7318">TCGA-KIRC  </a>study were converted to nifti files. Moreover, the nifti files with greater size than 35 MB and smaller size than 5 MB were removed (to reduce the size of the dataset and to remove the files with few slices). Furthermore, the metadata from the DICOM files is saved in a separate excel-file.

opencc-by-3.0Jul 2021View details →

Downsampling of CT-Lymph-Node Dataset for Body Part Regression Tutorial

Down sampling of the<a href="https://wiki.cancerimagingarchive.net/display/Public/CT+Lymph+Nodes#19726546f04e74ab3631480694fcb72cac2e5477"> CT Lymph Node</a> dataset from the TCIA. The files were down sampled to a pixel spacing of 7 mm/pixel. Through zero padding and cropping, all images are provided in the size of 64px x 64 px. Moreover, the HU values were clipped between -1000 HU and 1500 HU and rescaled to -1 and 1. To avoid aliasing effects, an additional Gaussian smoothing filter was applied before down sampling. This dataset was created for a Body Part Regression tutorial.

edi48/100

Sensor and nutrient data associated with the article Harrison et al. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression

This document describes a dataset used to produce Random Forests Regression models of stream nitrogen and phosphorus concentrations from high-frequency sensor data, as reported in: Harrison, J.W., Lucius, M.A., Farrell, J.L., Eichler, L.W., and Relyea, R.A. 2020. Prediction of stream nitrogen and phosphorus concentrations from high-frequency sensors using Random Forests Regression. Science of the Total Environment: https://doi.org/10.1016/j.scitotenv.2020.143005. The dataset consists of paired values of stream nitrogen and phosphorus concentrations and various high-frequency sensor parameters (water temperature, specific conductance, pH, fluorescent dissolved organic matter, turbidity, hydrostatic pressure, soil moisture) collected during baseflow and storm events from 2018 to 2019 as part of routine monitoring of eleven tributaries of Lake George, New York. This dataset does not include raw data; two levels of processing were performed: (1) erroneous values (extreme or otherwise outlying values with no apparent environmental cause) were removed from the sensor data as part of the routine QA/QC process of the Jefferson Project, and (2) one-hour rolling medians of the raw sensor data were calculated at a 1-minute timestep to maximize pairing of sensor data with nutrient concentrations. The resultant dataset was used to train and test the models presented in Harrison et al. 2020.

openCC (other)Jan 2021View details →

opencc-by-4.0Dec 2009View details →

Investigating terrestrial isopod abundance in sandplain grassland using a multiple linear regression

Most North American species of terrestrial isopod (Isopoda) have been introduced from Europe. Sandplain grassland is a globally rare habitat that is abundant on Nantucket Island, Massachusetts and the abundance of terrestrial isopods in the habitat has never been studied. The objective of this project was to develop a model to explain isopod abundance based on vegetation characteristics within Sandplain grassland and use this model to test for land management effects (prescribed burning and mowing) on isopod abundance. I counted terrestrial isopods from 175 pitfall traps set for one week and used multiple linear regression with several selection algorithms to select the best model. The vegetation characteristics I used as regressors do not appear to explain terrestrial abundance well and the final model only contains the percent grass coverage as a regressor. The model suggests that terrestrial isopods decrease in abundance with increasing grass coverage and it explains 29 percent of the data. When management effects are incorporated, the model suggests that mowing significantly increases isopod abundance. Funding for this project came from the Nantucket Islands Land Bank, Nantucket Land Council, and the Nantucket Biodiversity Initiative. Associated vegetation data is in the published "Effects of Sandplain Grassland Management on Spider Richness and Abundance on Nantucket Island" dataset.  Sampling methods are in the thesis linked from that dataset. allisopodData.csv - isopod counts by trap dataDictionary.csv - descriptions of variables mckenna-foster_2009.pdf - a report submitted to NBI and used as part of a statistics class at the University of Wisconsin-Green Bay

opencc-by-4.0Dec 2020View details →

Supplementary Data - MORTALITY RATE DUE TO PULMONERY FIBROSIS ASSOCIATED WITH SARS- COV-2 INFECTION: SCOPE OF BEST FIT REGRESSION

The dataset contains number of infected pateints - Death Frquencies - Mortality rate globally due to pulmonary fibrosis associated with SARS-COV-2 infection with effect from 21st Jan to 28 th April ,2020 . Data analysis report by best fit regression software Curve Expert V.1.4 supported with Spreadsheet ( Excel , Office 2007 ) are included for computation of statistical significance .

opencc-by-4.0Mar 2024View details →

Dataset: The effects of class balance on the training energy consumption of logistic regression models

Two synthetic datasets for binary classification, generated with the Random Radial Basis Function generator from WEKA. They are the same shape and size (104.952 instances, 185 attributes), but the "balanced" dataset has 52,13% of its instances belonging to class c0, while the "unbalanced" one only has 4,04% of its instances belonging to class c0. Therefore, this set of datasets is primarily meant to study how class balance influences the behaviour of a machine learning model.

opencc-by-4.0Oct 2021View details →

Covid-19 CT dataset for Body Part Regression Tutorial

The dataset is a subset of CT scans from the <a href="https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443">Covid-19-AR</a> dataset from the Cancer Image Archive. The data were converted from the DICOM file format to the NifTI file format for better and easier handling. The converted dataset was created for a tutorial of the <a href="https://github.com/MIC-DKFZ/BodyPartRegression">bpreg</a> python package. Acknowledgment: The dataset was funded with federal funds from the National Center for Advancing Translational Sciences  UL1 TR003107 and the National Cancer Institute, Contract No. 75N91019D00024, Subcontract 20X023F.

opencc-zeroMar 2018View details →

IML workshop challenge on jet mass regression

This dataset is associated with the LPCC IML (Lhc Physics Center at Cern Inter-experimental Machine Learning) working group.  It was produced for the second IML annual workshop (April 2018). This dataset is part of a machine learning "challenge" on jet mass regression at future circular collider (FCC) conditions.  Further details can be found on the challenge page, here: <a href="https://gitlab.cern.ch/IML-WG/IML_challenge_2018/wikis/home">https://gitlab.cern.ch/IML-WG/IML_challenge_2018/wikis/home</a>

opencc-by-4.0Aug 2018View details →

SCIAMACHY NO regression fit MCMC samples

SCIAMACHY mesosphere NO data and regression model samples SCIAMACHY mesosphere daily zonal mean NO data and Markov-Chain Monte-Carlo samples from the regression coefficient distributions, derived from and for use with the <a href="https://github.com/st-bender/sciapy"><code>sciapy</code></a> regression module. This data set contains the following files: <ul> <li><code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float32.nc</code>, <code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float64.nc</code> - samples from the regression coefficient distributions (single and double precision)</li> <li><code>NO_regress_quantiles_pGM_Lya_ltcs_exp1dscan60d_km32.nc</code> - the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions</li> <li><code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> - the SCIAMACHY daily zonal mean NO data</li> <li><code>sciapy_regress_tutorial.ipynb</code> - example ipython notebook</li> </ul> MCMC Samples The files <code>NO_regress_output..._float32.nc</code> and <code>NO_regress_output..._float64.nc</code> contain MCMC samples of the model as single and double precision floats. The file <code>NO_regress_quantiles....nc</code> contains the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions and is provided for convenience. The files contain the following parameters: <ul> <li><code>kernel:log_sigma</code>, <code>kernel:log_rho</code> - the "strength" and "lengthscale" of the Matérn-3/2 Gaussian Process kernel</li> <li><code>mean:offset:value</code> - the constant offset of the NO model in [\(10^6\) cm\(^{-3}\)]</li> <li><code>mean:Lya:amp</code> - the Lyman-\(\alpha\) coefficient of the mean model in [\(10^6\) cm\(^{-3}\) / Lyman-\(\alpha\)]</li> <li><code>mean:GM:amp</code> - the geomagnetic coefficient (AE) in [\(10^6\) cm\(^{-3}\) / nT]</li> <li><code>mean:GM:tau0</code> - the constant lifetime of the geomagnetic lifetime in [d]</li> <li><code>mean:GM:taucos1</code>, <code>mean:GM:tausin1</code> - cosine and sine amplitudes of the yearly geomagnetic lifetime variation in [d]</li> </ul> Daily zonal mean NO data The model was trained on the <a href="http://doi.org/10.5281/zenodo.1009078">SCIAMACHY mesosphere NO dataset</a>, binned into 10° geomagnetic latitude bins using the provided <code>gm_lat</code> variable and using the standard error of the mean as data uncertainties. The data are uploaded as <code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> and were prepared by running (after installing <code><a href="https://github.com/st-bender/sciapy">sciapy</a>)</code>: <pre><code class="language-bash">bash> scia_daily_zonal_mean.py -g -b'-90:90:10' -o <daily_zonal_mean_NO.nc> </path/to/SCIAMACHY_NO_NOM_orbits_20??_v6.2.1.nc></code></pre> Regression sampling The samples were generated by running the following command: <pre><code class="language-bash">bash> python -m sciapy.regress <daily_zonal_mean_NO.nc> --proxies Lya:<Lyman-alpha_file.dat>,GM:<AE_file.dat> -A <altitude> -L <geomag_latitude_bin> -w 14 -b 800 -p 1400 -F \"\" -I GM --fit_annlifetimes GM --positive_proxies GM --lifetime_scan=60 --lifetime_prior exp -k -K Mat32 -O0 -m "nom_pGM_Lya_ltcs_exp1dscan60d_km32" -P</code></pre>

opencc-by-4.0Dec 2018View details →

Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method

This dataset contains a GitHub repository containing all the data, analysis, Nextflow workflows and Jupyter notebooks to replicate the manuscript titled "Fast and accurate large multiple sequence alignments with a root-to-leaf regressive method". It also contains the Multiple Sequence Alignments (MSAs) generated and well as the main figures and tables from the manuscript. The repository is also available at GitHub (https://github.com/cbcrg/dpa-analysis) release `v1.2`. For details on how to use the regressive alignment algorithm, see the T-Coffee software suite (https://github.com/cbcrg/tcoffee).

opencc-by-4.0Sep 2021View details →

swmm-nrtestsuite: Regression Test Suite for OWA SWMM

Open Water Analytics (OWA) Stormwater Management Model (SWMM) Project Link:    https://github.com/OpenWaterAnalytics/swmm-nrtestsuite