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1,773 results for “Supplementary data,”
supplementary data Chemical and Microbial Leaching of Valuable Metals from PCBs and Tantalum Capacitors of Spent Mobile Phones
<p>Table S1: Chemical composition of waste PCBs and tantalum capacitors (VICs)without HF precious metals; Table S2: Leaching with organic acids; Table S3: Leaching with inorganic acids; Table S4: Bacterial leaching of PCBS varying pulp density; Table S5: Bacterial leaching of PCBS varying ferrous iron concentration; Table S6: Bacterial leaching of PCBS and tantalum capacitor scrap varying particle size; Table S7: Fungal leaching of metals by <em>A</em>. <em>niger</em> spores and filtrate.</p>
Supplementary Data and Figures for "Myomedin replicas of gp120 V3 loop glycan epitopes of PGT121 and PGT126 antibodies as non-cognate antigens for HIV-1 broadly neutralizing antibodies"
<p>Supplementary pymol sessions for "Myomedin replicas of gp120 V3 loop glycan epitopes of PGT121 and PGT126 antibodies as non-cognate antigens for HIV-1 broadly neutralizing antibodies". Pymol sessions contain the source data for Figure 3, panels A-K.</p>
FMPP and Testicular Germ Cell Tumors - Supplementary Data
<p>Objective: The purpose of this study is to report development of a malignant testicular<br> germ cell tumor (GCT) in two young adult males with Familial Male-limited Precocious<br> Puberty (FMPP) due to (LHCGR) pathogenic variants in two families. Secondary, to<br> study the possible relation between FMPP and testicular tumors and to investigate<br> whether FMPP might predispose to development of malignant testicular tumors in<br> adulthood a literature review is conducted. Methods: Data on six cases in two families<br> are obtained from the available medical records. In addition, a database search is<br> performed in Cochrane, Pubmed and Embase for studies that report on a possible link<br> between FMPP and testicular tumors. Results: The characteristics of six males with<br> FMPP based on activating luteinizing hormone receptor (LHCGR) germline pathogenic<br> variants are described, as well as details of the testicular GCTs. Furthermore, literature<br> review identified four more patients with signs of FMPP and a (precursor of a) testicular<br> GCT in adolescence or adulthood (age 15 to 35 years). Additionally, twelve patients<br> with signs of precocious puberty and, simultaneously, occurrence of a Leydig cell<br> adenoma or Leydig cell hyperplasia are reported. Conclusion: There is a strong<br> suggestion that FMPP might increase the risk of development of testicular GCTs in<br> early adulthood compared to the risk in the general population. Therefore, prolonged<br> patient monitoring from mid-pubertal age onwards including instruction for selfexamination<br> and periodic testicular ultrasound investigation in patients with a<br> germline LHCGRpathogenic variants might contribute to early detection and thus early<br> treatment of testicular GCT.</p> <p>This files contains the supplementary data. </p>
dRFEtools manuscript supplementary data
<p>This contains the supplementary data for the <a href="https://github.com/LieberInstitute/dRFEtools_manuscript">dRFEtools_manuscript</a>.</p>
Midnight Sun to Polar Night: A model of seasonal light in the Barents Sea - Supplementary Data
<p><strong>Supplementary Data</strong></p> <p>Midnight Sun to Polar Night: A model of seasonal light in the Barents Sea. Connan-McGinty, S., Banas, N.S., Berge, J., Cottier, F., Grant, S., Johnsen, G., Kopec, T.P., Porter, M., McKee, D. (2022).</p> <p>All data and Python code required to re-create figures from the above manuscript.</p>
Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment (SUPPLEMENTARY DATA FILES)
<p>SUPPLEMENTARY DATA FILES for the paper entitled: "Airborne spectral reflectance dataset of submerged plastic targets in a coastal environment." Data are organized in three categories:</p> <p>- orthophoto maps (date in YYYY,MM,DD e.g. 20210615_SPOTS_Ortho_UTM_35N)</p> <p>- Hyper Cubes UTM_35N NO Normalized (date in YYYY,MM,DD_Flight number_Height_UTM zone e.g. 20210615_F47_H25_UTM_35N)</p> <p>Hyper Cubes UTM_35N Normalized (date in YYYY,MM,DD_Flight number_Height_UTM zone_NORM e.g. 20210615_F47_H25_UTM_35N_NORM)</p>
Supplementary data for "Land tenure drives Brazil's deforestation rates across socio-environmental contexts"
<p>File name: Supplementary Data 1</p> <p>Description: Pre-processed data that contain all the information required to run the statistical analyses that produce our final results. This includes information on the land parcel identification number corresponding to the Imaflora dataset (gid), the covariates, and forest conversion to agriculture for the respective temporal scale. Note, each spatiotemporal scale and tenure regime comparison should be derived from the column “stScale”.</p> <p>File name: Supplementary Data 2</p> <p>Description: Full regression results reporting estimated effects each spatial-temporal scale considered, compared against both an undesignated/untitled public lands and private lands counterfactual. Average Marginal Effects (AME) are reported for each specific compared tenure regime at different spatial and temporal scales (“stScale”), with recorded number of observations in matched sample (n_pop), the standard error (SE), p-value, and lower and upper confidence intervals.</p> <p>File name: Supplementary Data 3</p> <p>Description: Rosenbaum bounds reporting upper and lower bounds for both Hodges-Lehmann estimates and p-values at each spatial-temporal scale considered.</p> <p>For more information on this study please see: see https://doi.org/10.31223/X5D31J and https://doi.org/10.5281/zenodo.7062285</p> <p> </p>
Supplementary data: "Open modeling of electricity and heat demand curves for all residential buildings in Germany"
<p>This repository contains supplementary data for the paper <a href="https://doi.org/10.1186/s42162-022-00201-y"><em> "Open modeling of electricity and heat demand curves for all residential buildings in Germany"</em></a>.</p> <p>See <em>README.md</em> / <em>README.pdf</em> for further details.</p> <p><strong>Citing</strong></p> <p>Please cite as:</p> <p><em>Büttner, C., Amme, J., Endres, J. et al. Open modeling of electricity and heat demand curves for all residential buildings in Germany. Energy Inform 5 (Suppl 1), 21 (2022).</em></p> <p><strong>Funding</strong></p> <p>The authors thank the Federal Ministry for Economic Affairs and Climate Action for funding the research project eGon (funding code: 03EI1002).</p> <p> </p>
Supplementary data for article "Reduced B-cell antigenicity of Omicron lowers host serologic response"
<p>This repository contains five supplementary data files for the research article "<strong>Reduced B-cell antigenicity of Omicron lowers host serologic response</strong>". For more information, please refer to the article preprint https://doi.org/10.1101/2022.02.15.480546 and upcoming article at Cell Reports.</p> <p> </p> <ol> <li><strong>Table_hCoV229E.xlsx:</strong> list of hCoV229E RBD sequences, with associated isolate and collection date identifiers, and ScanNet antigenicity score for reproducing <strong>Figure 3A</strong>. Aligned sequences and templates are also provided.</li> <li><strong>table_artificial_variants.csv</strong>: List of artificial RBD sequences generated by an evolutionary-based sequence generative model for reproducing <strong>Figure 3B</strong>, <strong>Supplementary Figure S6H</strong>.</li> <li><strong>MSA_RBD.fasta:</strong> Multiple Sequence Alignment of RBD sequences and sample weights used for training the sequence generative model used in <strong>Figure 3B</strong>, <strong>Supplementary Figures S2C, S6</strong>.</li> <li><strong>table_antibody_hit_rate_RBD.csv</strong>: Empirical epitope distribution for the RBD, as determined from the Protein Data Bank and raw data for <strong>Supplementary Figure S1</strong>).</li> <li><strong>RBD_virtual_DMS.xlsx</strong>: virtual Deep Mutational Scan performed with ScanNet and the sequence generative model shown in <strong>Supplementary Figure S2</strong>.</li> </ol> <p> </p> <p> </p>
Supplementary data of Figures (S1) and Tables (S1 & S2)
<p><strong>Figure S1. Comparison of early flower mutant recovered from irradiated PLBs at 10 Gy dose with the flower of ‘Emma White’ mother plant</strong></p> <p><strong>Table S1: Mean DNA intensity and nuclei count of gamma irradiated PLBs after five months of irradiation treatment</strong></p> <p><strong>Table S2: RAPD primers used for genetic study of gamma irradiated mutants</strong></p>
Data and supplementary materials from: Large genetic divergence underpins cryptic local adaptation across ecological and evolutionary gradients
<p><span>Environmentally covarying local adaptation is a form of cryptic local adaptation in which the covariance of the genetic and environmental effects on a phenotype obscures the divergence between locally adapted genotypes. Here, we systematically document the magnitude and drivers of the genetic effect (V<sub>G</sub>) for two forms of environmentally covarying local adaptation: counter- and cogradient variation. Using a hierarchical Bayesian meta-analysis, we calculated the overall effect size of V<sub>G</sub> as 1.05 and 2.13 for populations exhibiting countergradient or cogradient variation, respectively. These results indicate that the genetic contribution to phenotypic variation represents a 1.05 to 2.13 standard deviation change in trait value between the most disparate populations depending on if populations are expressing counter- or cogradient variation. We also found that while there was substantial variance among abiotic and biotic covariates, the covariates with the largest mean effects were temperature (2.41) and gamete size (2.81). Our results demonstrate the pervasiveness and large genetic effects underlying environmentally covarying local adaptation in wild populations and highlights the importance of accounting for these effects in future studies.</span></p>
Supplementary data for "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"
<p>Supplementary data for the article "Mapping global dynamics of benchmark creation and saturation in artificial intelligence"</p>
Supplementary data for: Hybridisation has shaped a recent radiation of grass-feeding aphids
<p><strong>Orthogroups and species tree</strong></p> <p>Proteomes included in the analysis: proteomes.tar.gz<br> Orthogroups: Orthogroups.txt<br> Gene counts per orthogroup, per species: Orthogroups.GeneCount.tsv<br> Single copy conserved orthogroups used for species tree: Orthogroups_SingleCopyOrthologues.txt<br> Protein alignment used for species tree reconstruction: SpeciesTreeAlignment.fa<br> Species tree: SpeciesTree_rooted.txt</p> <p><strong>Whole genome alignment of <em>S. avenae</em>, <em>S. miscanthi,</em> <em>M. dirhodum</em> and <em>A. pisum</em></strong></p> <p>Cactus whole genome alignment (hal format): Siave_Simis_Medir_Acpis.hal.gz</p> <p><strong>Haplotype divergence analysis (whole genome sequences)</strong></p> <p>VCF files of HapCUT2 phased variants for <em>S. miscanthi </em>Langfang-1 chromosomes (Simis_v2 assembly scaffolds 1 to 9):<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_1.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_2.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_3.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_4.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_5.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_6.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_7.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_8.hap.phased.VCF.gz<br> Langfang1.Hapcut2_PB_plus_HiC.scaffold_9.hap.phased.VCF.gz</p> <p>VCF files of HapCUT2 phased variants for <em>S. avenae </em>JIC1<em> </em>chromosomes (Siave_v2.1 assembly scaffolds 1 to 9):<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_1.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_2.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_3.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_4.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_5.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_6.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_7.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_8.hap.phased.VCF.gz<br> JIC1.Hapcut2_IL_plus_HiC.scaffold_9.hap.phased.VCF.gz</p> <p>Haplotype resolved assemblies of <em>S. avenae</em> JIC1 and <em>S. miscanthi </em>Langfang-1 based on HapCUT2 phasing results:<br> JIC1_H1.Hapcut2.fa<br> JIC1_H2.Hapcut2.fa<br> Langfang1_H1.Hapcut2.fa<br> Langfang1_H2.Hapcut2.fa</p> <p>SibeliaZ whole genome alignment of <em>S. avenae </em>JIC1 and <em>S. miscanthi </em>Langfang-1 haplotypes: alignment.filtered.ordered.stranded.sorted.maf</p> <p><strong>Filtered VCF files used for population genomics analysis</strong></p> <p><em>S. avenae</em> and <em>S. miscanthi</em> GBS samples + JIC1 and Langfang1 WGS samples variant calls: freebayes.q30_dp2_biallelic.mm_75.indv_max_30pc_missing.recode.vcf<br> <em>S. avenae</em> and <em>S. miscanthi</em> GBS samples + JIC1 and Langfang1 WGS samples phased variant calls: freebayes.q30_dp2_biallelic.mm_75.indv_max_30pc_missing.recode.fix_mis.beagle.vcf<br> <em>S. avenae</em> and <em>S. miscanthi</em> GBS samples + JIC1, Langfang1 and <em>M. dirhodum </em>WGS samples variant calls: with_Medir.merged.q30_dp2_biallelic.mm_90.recode.vcf<br> <em>S. avenae</em> and <em>S. miscanthi </em>GBS samples + JIC1, Langfang1 and <em>M. dirhodum</em> WGS samples phased variant calls: with_Medir.merged.q30_dp2_biallelic.mm_90.recode.fix_mis.beagle.vcf<br> <em>S. miscanthi </em>GBS samples + JIC1 and Langfang1 WGS samples variant calls: China_plus_JIC1.merged.q30_dp2_biallelic.mm_90.recode.vcf<br> <em>S. miscanthi </em>GBS samples + JIC1 and Langfang1 WGS samples variant calls: China_plus_JIC1.merged.q30_dp2_biallelic.mm_90.recode.fix_mis.beagle.vcf</p> <p><strong>SNAPP phylogenetic analysis configuration file and trees</strong></p> <p>SNAPP configuration file: snapp.xml<br> SNAPP log file: ut.log<br> SNAPP posterior sample of trees: ut.trees<br> SNAPP maximum clade credibility tree with 10% burn in: ut.trees.max_cred_burn_10pc</p> <p> </p> <p> </p>
Supplementary Data for "Adoption of Transparency and Openness Promotion (TOP) guidelines across journals"
<p>Supplementary data for: https://zenodo.org/record/7129250</p>
Supplementary data for "Upward leaders from instrumented lightning rods competing to connect a downward leader during a lightning attachment process"
<p>High-speed videos of the lightning attachment to lightning rods observed by the Vision Reserch Phantom V12 and V711 cameras. See Instructions file to play the videos.</p>
Supplementary Data: Kapitza thermal resistance across individual grain boundaries in graphene
<p>The data here are the numerical bi-crystalline graphene samples used in Ref. [1].</p> <p>There are 13 folders named "1", "2", ..., "13". Each folder contains a file named "xyz.in". They are in the format of the "xyz.in" file used in the GPUMD package [2]. From folder "1" to "13", the tilt angle increases monotonically. See Table 1 of Ref. [1] for the values of the tilt angles.</p> <p>[1] Khatereh Azizi, Petri Hirvonen, Zheyong Fan, Ari Harju, Ken R Elder, Tapio Ala-Nissila, and S Mehdi Vaez Allaei,<br> Kapitza thermal resistance across individual grain boundaries in graphene,<br> Carbon 125, 384 (2017).<br> https://doi.org/10.1016/j.carbon.2017.09.059<br> https://arxiv.org/abs/1709.09529</p> <p>[2] Zheyong Fan, GPUMD,<br> https://github.com/brucefan1983/GPUMD<br> http://doi.org/10.5281/zenodo.995667</p> <p> </p>
Supplementary Data: Real-time optimal flood control decision making under uncertainty
<p>The files in this record contain data for real-time optimal flood control decision making and risk propagation under multiple uncertainties considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>Pubugou Reservoir data;</li> <li>Source code and results of the Martingale Model of Forecast Evolution (MMFE);</li> <li>Source code and results of the SMAA-2 model;</li> <li>Source code and results of SMAA-TOPSIS model;</li> <li>Source code and results of the stochastic programming with recourse model.</li> </ul>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
Supplementary Data for "New Constraints on Gliese 876 - Exemplar of Mean-Motion Resonance"
<p>This repository contains supplementary material to the paper<br> Millholland, S., Laughlin, G., Teske, J., et al. "New Constraints on Gliese 876 - Exemplar of Mean-Motion Resonance." 2018, AJ, in press</p> <p>The file, "posterior_samples.csv", contains DE-MCMC posterior parameter samples for the four-planet coplanar radial velocity fit, which is described in Section 4 and summarized in Table 4 of the paper. The epoch of fit is 2450602.09311 BJD. <br> To fit within file size limitations, the posterior samples were thinned by uniqueness. The columns of the files are provided below. For the planet parameters, the columns are in orbital period order (i.e. planets "d", "c", "b", "e"). </p> <p>0-3 Periods (days) <br> 4-7 Semi-major axes (AU) <br> 8-11 RV semi-amplitudes (m/s) <br> 12-15 Masses (Earth masses) <br> 16-19 Eccentricities <br> 20-23 Arguments of periastron (degrees) <br> 24-27 Mean anomalies (degrees) <br> 28-31 Arg. of peri. plus mean anom. (degrees) <br> 32-35 e cos(arg. peri.)<br> 36-39 e sin(arg. peri.)<br> 40-43 Longitude of the ascending node (degrees) <br> 44-47 Inclination (degrees) <br> 48-53 RV zero-point offsets (m/s) <br> 54-59 Sigma jitter terms (m/s) <br> 60 h (m/s) <br> 61 w <br> 62 logP_rot (log days) <br> 63 Lambda (days) <br> 64 log-likelihood </p> <p>Feel free to direct questions or comments to sarah [dot] millholland [at] yale [dot] edu.</p>
New Constraints on Gliese 876 - Exemplar of Mean-Motion Resonance: Supplementary Data
<p>Gliese 876 - Posterior Samples from RV Fit</p> <p>This respository contains supplementary material to the paper <br> Millholland, S., Laughlin, G., Teske, J., et al. <em>New Constraints on Gliese 876 - Exemplar of Mean-Motion Resonance. </em>2018, AJ, in press</p> <p>The file "posterior_samples_4pl_fit.csv" contains DE-MCMC posterior parameter samples for the four-planet coplanar radial velocity fit, which is described in Section 4 and summarized in Table 4 of the paper. The epoch of fit is 2450602.09311 BJD. <br> To fit within file size limitations, the posterior samples were thinned by uniqueness. The columns of the files are provided below. For the planet parameters, the columns are in orbital period order (i.e. planets "d", "c", "b", "e").</p> <p>0-3 Periods (days) <br> 4-7 Semi-major axes (AU) <br> 8-11 Masses (Earth masses) <br> 12-15 Eccentricities <br> 16-19 Arguments of periastron (degrees) <br> 20-23 Mean anomalies (degrees) <br> 24-27 Arg. of peri. plus mean anom. (degrees) <br> 28 Inclination (degrees) <br> 29-34 RV zero-point offsets (m/s) <br> 35-40 Sigma jitter terms (m/s) <br> 41 h (m/s) <br> 42 w <br> 45 logP_rot (log days) <br> 46 Lambda (days) <br> 47 log-likelihood </p> <p>Feel free to direct questions or comments to sarah [dot] millholland [at] yale [dot] edu.</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.