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951 results for “Data release”
aghounshell/Extreme_Events: First release of code and data
<p>This is the first release of the associated code and data. For questions or comments, please contact A. Hounshell at ahounshell10(at)gmail.com.</p>
aysunrhn/Adaptive-Soft-Sensor-Design: First release (data only)
<p>Released the simulation data for the research paper titled "Integrating Adaptive Moving Window and Just-in-Time Learning Paradigms for Soft-Sensor Design"</p>
Data for manuscript: The orbital anisotropy profiles of nearby globular clusters from Gaia Data Release 2
<p>We upload the data used in our paper here so that our results may be reproduced. We include the dataset of stars that survive our cuts, the profiles we plot, and the manual points selected as part of our CMD cut. See the paper for details. The first version of this paper is published on the arXiv with ID: arXiv:1903.11070. </p>
The data for the article entitled "Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem"
<p>The data for the article entitled "Soil carbon release responses to long-term versus short-term climatic warming in an arid ecosystem" Yu et al. </p>
Scattering of dark pions in Sp(4) gauge theory - Data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.06506"><em>Scattering of dark pions in Sp(4) gauge theory [2405.06506]</em></a><br>Further details are given in the file README.md.</p> <p>YD and FZ have been supported the Austrian Science Fund research teams grant STRONG-DM (FG1). FZ has been supported by the STFC Grant No. ST/X000648/1. The computations have been performed on the Vienna Scientific Cluster (VSC4).</p>
Data release for the "First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K"
<p>### On-/Off-Axis Data Release<br>#### (Version 1.0.1, dated 2024/08/12)</p> <p>This tar archive contains the data release for ‘First measurement of muon neutrino charged-current interactions on hydrocarbon without pions in the final state using multiple detectors with correlated energy spectra at T2K’. It contains the cross-section data points and supporting information in ROOT and text format, which are detailed below:</p> <p>+ `onoffaxis_xsec_data.root`<br>This ROOT file contains the extracted cross section and the nominal MC prediction as TH1D histograms for both the flattened 1D array of bins and in the angle binning for the analysis. The ROOT file also contains both the covariance and inverted covariance matrix for the result stored as TH2D histograms. The angle bin numbering and the corresponding bin edges are detailed at the end of the README.</p> <p>+ `flux_analysis.root`<br>This ROOT file contains the nominal and post-fit flux histograms for ND280 and INGRID. Two different binnings are included: a fine binned histogram (220 bins) and a coarse binned histogram (20 bins). The coarse binned histogram corresponds to the flux parameters detailed in the paper (and bin edges listed in the appendix).</p> <p>+ `xsec_data_mc.csv`<br>The extracted cross-section data points and the nominal MC prediction for each bin is stored as a comma-separated value (CSV) file with header row.</p> <p>+ `cov_matrix.csv` and `inv_matrix.csv`<br>The covariance matrix and the inverted covariance matrix are both stored as CSV files with each row stored as a single line and columns separated by commas (there is no header row). Matrix element (0,0) corresponds to the first number in the file.</p> <p>+ `nd280_analysis_binning.csv` and `ingrid_analysis_binning.csv`<br>The analysis bin edges are included as CSV files. The columns are labeled with a header row and denote the linear bin index and the lower and upper bin edge for the angle and momentum bins. The units are in cos(angle) for the angle bins and in MeV/c for the momentum bins.</p> <p>+ `calc_chisq.cxx`<br>This is an example ROOT script to calculate the chi-square between the data and the nominal MC prediction using the ROOT file in the data release. To run, open ROOT and load the script (`.L calc_chisq.cxx`) and execute the function `calc_chisq("/path/to/file.root")`.</p> <p>+ `calc_chisq.py`<br>This is an example Python script to calculate the chi-square between the data and the nominal MC prediction using the text/CSV files in the data release. The code requires NumPy as an external dependency, but otherwise uses built-in modules. To run, execute using a Python3 interpreter and give the file paths to the data/MC text file and the inverse covariance text file as the first and second arguments respectively -- e.g. `python3 calc_chisq.py /path/to/xsec_data_mc.csv /path/to/inv_matrix.csv`</p> <p>+ ND280 angle bin numbering<br> - 0: `-1.0 < cos(#theta) < 0.20`<br> - 1: `0.20 < cos(#theta) < 0.60`<br> - 2: `0.60 < cos(#theta) < 0.70`<br> - 3: `0.70 < cos(#theta) < 0.80`<br> - 4: `0.80 < cos(#theta) < 0.85`<br> - 5: `0.85 < cos(#theta) < 0.90`<br> - 6: `0.90 < cos(#theta) < 0.94`<br> - 7: `0.94 < cos(#theta) < 0.98`<br> - 8: `0.98 < cos(#theta) < 1.00`</p> <p>+ INGRID angle bin numbering<br> - 0: `0.50 < cos(#theta) < 0.82`<br> - 1: `0.82 < cos(#theta) < 0.94`<br> - 2: `0.94 < cos(#theta) < 1.00`<br> <br>### Changelog</p> <p>#### v1.0.1<br>Fix transcription error in INGRID momentum binning. The lowest momentum bin edge is at 350 MeV/c, not 300 MeV/c.</p>
Global Parkinson's Genetics Program Data Release 8
<p>In September 2024, GP2 announced the eighth data release on the Terra and the Verily® Workbench platforms in collaboration with AMP® PD. This release includes 5,481 additional whole genome sequences and 10,454 clinical exome sequences. Additional genotyping will be provided in the following release.</p> <ul> <li> <p>The whole genome sequencing (WGS) data now consists of a total of 7,734 sequenced participants (6,113 PD cases, 617 Controls, and 1,004 ‘Other’ phenotypes).</p> </li> <ul> <li> <p>When removing the locally-restricted samples, these now consist of 4,713 participants (4,098 PD cases, 390 Controls, and 225 ‘Other’ phenotypes).</p> </li> <li> <p>Of note, cases recruited via the Monogenic network are coded as ‘Other’</p> </li> </ul> <li> <p>Additionally, included in this WGS release is a partial release of whole genome sequences from two AMP-PD cohorts (BioFind and PPMI) that have been joint-called with GP2 WGS. Released samples can be linked back to the original AMP-PD IDs through an ID crosswalk file included with the release.</p> </li> <li> <p>This release also includes 10,454 joint-called clinical exome sequencing (CES) participants from the Parkinson’s Foundation.</p> </li> <li>This release includes a total of 62,087 individuals who have core clinical data available. Among these, 16,800 individuals have deep clinical phenotyping and genetic data available</li> </ul> <p>---</p> <p>Please see the accompanying blog for further description of this release. To obtain data access, please see <a href="https://amp-pd.org/researchers/data-use-agreement">https://amp-pd.org/researchers/data-use-agreement</a>. For any publications using data from this release, please reference the DOI number and the following statement: "<em>Data (DOI <strong>10.5281/zenodo.13755496</strong>, release 8) used in the preparation of this article were obtained from the Global Parkinson’s Genetics Program (GP2)."</em></p>
MAGIC Data Level 3 (DL3) Public Data Release 1 (PDR1)
<div>This data set constitutes the first release of MAGIC telescopes data to the public.</div> <div> </div> <div>The data are produced according to the <a href="https://gamma-astro-data-formats.readthedocs.io/en/v0.3/">Data formats for gamma-ray astronomy (GADF)</a> specifications, we recommend to examine the GADF documentation to become familiar with the description of the data content.</div> <div> </div> <div>This data set corresponds to 60 hours of observations of the Crab Nebula performed between 2013 and 2018 under different observational conditions. In the <code>dark</code> sample, we provide observations of the Crab at different positions in the MAGIC field of view (measured with the offset from the camera centre of the projected source position). In the <code>moon</code> sub-sample, we provide Crab Nebula data gathered under increasing level of night sky background (NSB) illumination.</div> <div> </div> <div>A more technical description of the repository content and usage is available in the <code>README.md</code> file, the user is also referred to the <a href="https://arxiv.org/abs/2409.18823">DL3 validation paper</a> for more details on these observations.</div>
CLS3 Gencode v47 data release
<p>GENCODE: massively expanding the lncRNA catalog through capture long-read RNA sequencing.</p>
CoronaNetDataScience/corona_tscs: Releasing new data fields in event dataset
<p>Original data collection of PHSM began on March 28, 2020. Since then, governments have implemented a wide variety of PHSM often with increasing nuance (e.g. with regards to the geographic or demographic targets of a given policy). While Version 1.0 of the dataset only released data from questions in the original survey, Version 1.1 releases data from new questions that have been added over the course of first year of data collection. For more information about the additional fields and options added to the dataset, please see our codebook</p> <ol> <li>update_level_var: More detailed information as to what dimension of a policy is being updated (i.e., strengthened or relaxed)</li> <li>pdf_link: Link to PDF of the original source used to document a policy</li> <li>institution_cat: Information as to whether a business or government service is considered essential or non-essential according to the government entity in charge of implementing a given PHSM</li> <li>institution_conditions: Information about what conditions a school, business or government service is allowed to open under (e.g., limited number of people allowed on premises)</li> <li>type_new_admin_coop: Information about the nature of a given cooperative effort if different governments decide to cooperate with each other (e.g. country A cooperates with country B)</li> <li>COVID-19 Vaccines: We have added new questions to capture various dimensions of the global COVID-19 Vaccine rollout including information on:</li> </ol> <ul> <li>The manufacturing firm (type_vac_cat)</li> <li>Whether vaccines are allowed to be mixed and matched (type_vac_mix)</li> <li>The regulatory status of a given COVID-19 vaccine (type vac reg)</li> <li>Information on the type of purchase order for COVID-19vaccines (type_vac_purchase)</li> <li>Information on the overall criteria used for deciding how to administer COVID-19 vaccines (type vac group).</li> <li>Information on the number of priority groups for COVID-19 Vaccine distribution, given that this is the criteria used for deciding how to administer COVID-19 vaccines (type_vac_group_rank)</li> <li>Information as to where COVID-19 vaccines are being administered (type_vac_loc)</li> <li>Information as to who is responsible for the economic cost of a given COVID-19 vaccine shot (type_vac_who_pays)</li> <li>Information as to what entity has been placed in primary charge for the COVID- 19 vaccination process (type_vac_dist_admin)</li> <li>Information as to the monetary resource devoted for a given COVID-19 vaccine policy ( typ_vac_cost_num, type_vac_cost_unit, type_vac_cost_scale, type_vac_gov_perc)</li> <li>Information as the volume of COVID-19 vaccines (e.g. shots) in question for a given COVID-19 vaccine policy (type<em>va</em> amt_num, type_vac_amt_unit, type_vac_amt_scale, type_vac_amt_gov_perc)</li> </ul> <ol> <li>target_init_same: Whether the geographic target of a policy is the same as the policy initiator (e.g. target init same ==0 if lockdown policy implemented by government A in country A while target init same ==1 if an external border restrictions is im- plemented by government A against country B)</li> <li>target_intl_org: Which international organization a policy is targeted to, if applicable. 11</li> <li>target_who_gen: Information as to what special populations (e.g. asylum seekers, in- digenous peoples) a policy targets, if applicable</li> <li>date_end_spec: Qualitative information on a policy's end date</li> </ol>
VPM Burst and Survey Data first release
<p>VPM Burst and Survey Data for Marshall et al [2021], submitted to Earth and Space Sciences</p> <p>This data covers survey data on 06/28/2020, and a burst data collection of 06/14/2020. This data has been approved for public release by AFRL specifically for this publication.</p> <p>Other VPM data will be released by AFRL in the near future.</p>
Data release for the "Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280"
<p>This data release is associated with the publication "Measurement of the charged-current electron (anti-)neutrino inclusive cross-sections at the T2K off-axis near detector ND280". It is currently available on arXiv and in JHEP:</p> <p><a href="https://arxiv.org/abs/2002.11986">arXiv:2002.11986 [hep-ex]</a> and <a href="https://doi.org/10.1007/JHEP10(2020)114">J. High Energ. Phys. 10, 114 (2020)</a></p> <p><strong>When citing this data release, please cite as well the paper.</strong></p> <p><em>The full author list and acknowledgements for the T2K collaboration are described in the article.</em></p> <p>The data release contains:</p> <ul> <li>cross-section measurements with NEUT 5.3.2 (fraction and total with covariances)</li> <li>cross-section measurements with GENIE 2.8.0 (fraction and total with covariances)</li> <li>smearing matrices for selected electron/positron momentum</li> </ul> <p><strong>Description:</strong></p> <p>The cross-section measurements are provided in the form of text files and a PDF summary. The detailed method and results are presented in the paper (especially section 8).</p> <p>The smearing matrices are provided as one ROOT file with two 2D histograms showing the electron/positron smearing matrices for momentum and angle, obtained using the selection from the ND280 nue CC inclusive analysis. It is similar to the figure 10 of the paper, but with more statistics and finer binning. They are accompanied with a README file presenting how to use these matrices and the related caveats. <strong>Please read it carefully.</strong></p> <p><strong>We strongly encourage any users of the matrices to present these caveats alongside any public comparison to T2K data.</strong></p> <p> </p> <p><strong>Full abstract:</strong></p> <p>The electron (anti-)neutrino component of the T2K neutrino beam constitutes the largest background in the measurement of electron (anti-)neutrino appearance at the far detector. The electron neutrino scattering is measured directly with the T2K off-axis near detector, ND280. The selection of the electron (anti-)neutrino events in the plastic scintillator target from both neutrino and anti-neutrino mode beams is discussed in this paper. The flux integrated single differential charged-current inclusive electron (anti-)neutrino cross-sections, dσ/dp and dσ/dcos(θ), and the total cross-sections in a limited phase-space in momentum and scattering angle (p>300 MeV/c and θ≤45<sup>∘</sup>) are measured using a binned maximum likelihood fit and compared to the neutrino Monte Carlo generator predictions, resulting in good agreement.</p>
Data Release for "The curious case of GW200129: interplay between spin-precession inference and data-quality issues"
<p>Data Release associated with <a href="https://arxiv.org/abs/2206.11932">The curious case of GW200129: interplay between spin-precession inference and data-quality issues Data Release</a>. We release frame files and result file for selected parameter estimation runs in the paper.</p> <p> </p> <p>Each directory contains .json bilby result files for the PE run described by that directory. The specific channel names and frame files that we used in the PE runs are listed below. The frames for the PE runs with BayesWave glitch subtraction are included in this release and are in BW_frames/frame_{glitch label}.</p> <p> </p> <p>L1 data with glitch subtraction:</p> <p>Channel: L1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_P1800169_v4</p> <p>Link: https://zenodo.org/record/5546680/files/L-L1_HOFT_CLEAN_SUB60HZ_C01_P1800169_v4-1264314068-4096.gwf</p> <p> </p> <p>L1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (L1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080/L-L1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p> </p> <p>H1 data, no mitigation:</p> <p>Channel: H1:DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01 (H1:GWOSC-16KHZ_R1_STRAIN)</p> <p>Link: https://www.gw-openscience.org/archive/data/O3b_16KHZ_R1/1263534080HL-H1_GWOSC_O3b_16KHZ_R1-1264312320-4096.gwf</p> <p> </p> <p>Channels for runs with BayesWave glitch-subtracted frames--</p> <p>(Note you need to read in the correct gwf file to access each channel; for example the channel for BayesWave glitch A should be accessed after reading in the gwf file in `BW_frames/frame_A/`)</p> <p>BayesWave glitch A (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_30000</p> <p>BayesWave glitch B (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_28395</p> <p>BayesWave glitch C (applies only to L1): DCS-CALIB_STRAIN_CLEAN_SUB60HZ_C01_BW_DEGLITCHED_32752</p>
Data Release: Spin it as you like: the (lack of a) measurement of the spin tilt distribution with LIGO-Virgo-KAGRA binary black holes
<p>This is the data release associated with <strong>Vitale et al <a href="https://arxiv.org/abs/2209.06978">2209.06978</a></strong></p> <p><strong>Samples.zip: </strong>Contains all of the hyper posterior samples for the runs listed in Tables G.1.</p> <p>The files are in json format. Bilby offers a dedicated routine to read them in</p> <p> </p> <blockquote> <p>import bilby<br> data= bilby.core.result.read_in_result(path_to_json)</p> </blockquote> <p> </p> <p>See the <a href="https://lscsoft.docs.ligo.org/bilby/">Bilby documentation </a>for what is contained in the result object. </p> <p>For each run, we report the posterior hyper samples for the mass model, reshift model, spin magnitude model, spin tilt model and merger rate [Gpc^-3 yr^-1]</p> <p>Here the name used to store and a short description of each parameter (Follow the references in the Method section of the paper for a description of each sub-model):</p> <ol> <li>Primary mass model (Power Law + Peak for all runs) <ol> <li>power_law_slope_m1, slope of the primary mass power law component</li> <li>minmass_m1, minimum BH mass</li> <li>maxmass_m1, maximum BH mass</li> <li>low_end_smoothing_m1, smoothing at the low-mass end</li> <li>peak_branchingratio_m1, branching ratio between Gaussian peak and power law (1= 100% peak)</li> <li>peak_mean_m1, mean of the Gaussian peak</li> <li>peak_sigma_m1, sigma of the Gaussian peak </li> </ol> </li> <li>Mass ratio model (power law for all runs) <ol> <li>power_law_slope_mass_ratio, slope of the mass ratio </li> </ol> </li> <li>Redshift (power law for all runs) <ol> <li>power_law_slope_redshift, slope of the redshift</li> </ol> </li> <li>Spin magnitude (IID beta distributions for all runs) <ol> <li>alpha_chi, first argument of beta distribution</li> <li>beta_chi, second argument of beta distribution</li> </ol> </li> <li>Cosine of tilt angle <ol> <li>Gaussian models <ol> <li>mu_0_costilt, for Gaussian models w/o correlation, the mean of the left (or only) Gaussian</li> <li>sigma_0_costilt, for Gaussian models w/o correlation, the sigma of the left (or only) Gaussian</li> <li>mu_1_costilt, for Gaussian models w/o correlation, the mean of the right Gaussian</li> <li>sigma_1_costilt, for Gaussian models w/o correlation, the sigma of the right Gaussian</li> <li>mu_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian mean</li> <li>mu_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian mean</li> <li>sigma_a_costilt, for Gaussian model with correlation, the constant part of the Gaussian sigma</li> <li>sigma_b_costilt, for Gaussian model with correlation, the coefficient of the linearly evolving part of the Gaussian sigma</li> </ol> </li> <li>Beta models <ol> <li>alpha_a_costilt, for all Beta models, the constant part of the first parameter of the Beta distribution</li> <li>alpha_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the first parameter of the Beta distribution</li> <li>beta_a_costilt, for all Beta models, the constant part of the second parameter of the Beta distribution</li> <li>beta_b_costilt, for all Beta models, the coefficient of the linearly evolving part of the second parameter of the Beta distribution</li> </ol> </li> <li>Tukey models: <ol> <li>tukey_x0, the center of the Tukey as defined in appendix E of the paper</li> <li>tukey_k, Tk as defined in appendix E of the paper</li> <li>tukey_r, Tk as defined in appendix E of the paper</li> </ol> </li> <li>Branching ratios: <ol> <li>spin_mixture_0, for 2-component models, this is the branching ratio of the non-isotropic component</li> <li>spin_mixture_1, for Isotropic + Gaussian + Tukey and Isotropic + Gaussian + Beta this is the branching ratio of the <strong>Gaussian</strong> component; for Isotropic + 2 Gaussian this is the branching ratio of the <strong>Gaussian on the right.</strong></li> </ol> </li> </ol> </li> <li>Merger rate <ol> <li>rates, merger rate per unit Gpc cubed per unit year</li> </ol> </li> </ol> <p>Note that some of the parameters for the tilt models might not be used, but still stored (and fixed to - usually - zero). This can be checked by verifying what priors were used for each parameter. For example the <em>Isotropic</em> run was obtained from the <em>Isotropic + Gaussian </em>model by setting the branching ratio of the Gaussian component to zero (at which point the values of mu and sigma costitl are irrelevant) </p> <blockquote> <p>> data['prior']<br> [...]<br> <strong> 'spin_mixture_0': DeltaFunction(peak=0, name=None, latex_label=None, unit=None),</strong><br> </p> </blockquote> <p> </p> <p><strong>Figures.zip:</strong> Contains PDFs for all figures in the paper, plus individual figures for p(costau) and dR/dcostau for each model.</p> <p>Drop me (Salvatore Vitale) an email if anything doesn't work, is missing, or if you spot issues. Thanks! </p> <p> </p>
Gene family data from the PhyloGenes (release version 4.0, phylogenes.org)
<p>The data files were generated from the PhyloGenes 4.0 release (see release notes <a href="https://conf.arabidopsis.org/display/PHGSUP/About+PhyloGenes">here</a>).</p> <p>About the two zip files: </p> <p>1. phyloXML.zip (these are different and updated from the immediately preceding PhyloGenes 3.2 release)</p> <p>PhyloGenes gene family trees in PhyloXML format, one file per family (e.g. <family_ID>.xml).</p> <p>The following information is provided for each node of a tree:<br> 1) leaf node:<br> branch length<br> name <gene_id><br> taxonomy scientific_name<br> sequence accession <UniProt ID></p> <p>2) non-leaf node:<br> branch length<br> events <duplication or speciation></p> <p><br> 2. CSV.zip</p> <p>Functional information of family members in CSV format, one file per family (e.g. <family_ID>.csv). </p> <p>A CSV file includes the following columns:<br> Uniprot ID<br> Gene <Gene name. If none then Gene ID><br> Gene ID<br> Gene name<br> Organism<br> Subfamily name</p> <p>The columns displayed after 'Subfamily name', if any, are GO annotations. Each column is a GO molecular function or biological process term that is annotated to at least one member of the gene family AND the annotation is supported by an experimental evidence (indicated by 'EXP') or phylogenetic inference (indicated by 'IBA'). A '0' indicates absence of either annotations.</p>
Data from: Seed dormancy revisited: dormancy-release pathways and environmental interactions
<p>1. Many internal (inherent) and environmental (imposed) factors control seed dormancy and germina-tion from which we can derive three basic dormancy-release pathways: Maternal structures and embryo physiology control inherent dormancy that is broken by various types of scarification and physiological changes, followed by imposed-dormancy release when replaced by certain 'standard' environmental conditions that stimulate germination (pathway 1); imposed dormancy prevails even if inherent dorman-cy is broken or not applicable that is released when replaced by certain 'standard' environmental condi-tions which stimulate germination (pathway 2); release from inherent dormancy by light/dark or cold stratification is contingent on existing presence of certain 'standard' environmental conditions that stim-ulate germination (pathway 3).</p> <p>2. On-plant seed storage (serotiny) and frugivorous seeds are recognized here as representing special types of physical dormancy, as their properties are consistent with those of hard diaspores. Warm stratification does not require seeds to be moist as it is just a physical response. Heat may promote germination of non-hard, as well as hard, seeds as it may also increase their permeability.</p> <p>3. Levels of germination gauge the net effect of inherent- and imposed-dormancy release so that it only possible to identify the extent of inherent-dormancy release when conditions for germination are optimal (imposed dormancy has been annulled). While imposed dormancy may be protracted after inherent dormancy is broken by heat or chilling during the dry or cold seasons, release from both states may effectively coincide if smoke chemicals or light are received during the (wet) growing sea-son.</p> <p>4. We suggest reserving the term secondary dormancy for seeds that return to (inherent or imposed) dormancy due to changed environmental conditions. Under seasonal climates, fluctuations in envi-ronmental conditions can lead to secondary dormancy and even dormancy cycling.</p> <p>5. We recognize four types of functional interactions between any two environmental factors that induce inherent-dormancy release: binary interactions are either ineffective, only one effective, non-additive or additive/synergistic. Two environmental stimuli that individually break dormancy but have no additive effect must be affecting the same process; this was demonstrated here for some interac-tions between heat and smoke.</p> <p>6. The three dormancy-release pathways, together with internal, seasonal and stochastic interact ions, are coordinated by the non-dormant seed to ensure maximum germination under optimal conditions. To ignore any aspect outlined here leads to an impoverished understanding of the disparate seed ecol-ogy of species adapted to different stressful and disturbance-prone habitats.</p>
Data from: Assessing the deep carbon release in an active volcanic field using hydrochemistry, δ13CDIC and Δ14CDIC
<p><span>Volcanic activities have great implications on geological carbon cycle, and ascertaining the deep carbon contribution in earth surface that run along the volcanic edifices is important to understand the relationship between earth degassing and global climate change. This study reports analytical results of major dissolved ions, stable carbon isotope (δ<sup>13</sup>CDIC) and radiocarbon (Δ<sup>14</sup>CDIC) of dissolved inorganic carbon (DIC) of rivers, cold springs and hot springs from Changbaishan volcanic area, Northeast China. </span></p>
Data from: Mesopredator release among invasive predators: controlling red foxes can increase feral cat density and alter their behaviour
<p>The mesopredator release theory predicts that the density of subordinate predators will increase as dominant predators decline. Persistent debate around mesopredator release in part reflects the lack of robust, replicated experiments to test this theory, and the use of population indices which confound changes in mesopredator density and detectability. This uncertainty has immediate impacts for conservationists who are faced with managing sympatric invasive predators.</p> <p>We used replicated experimental designs and spatially-explicit models to examine whether mesopredator release of the feral cat <em>Felis catus</em> occurs in response to targeted control of the introduced red fox <em>Vulpes vulpes</em>. We surveyed three Control-Impact paired landscapes in a region with long-term fox control (1080 poison baiting), and conducted a Before-After Control-Impact Paired-Series experiment in another region. We used fox occurrence as a simple metric of fox populations and estimated feral cat density with spatial mark-resight models.</p> <p>Lethal fox control had varying effects on fox occurrence, consistent with variation in the duration and intensity of poison baiting. Correspondingly, responses in feral cat density ranged from negligible to a 3.7-fold higher density in fox-baited landscapes. At a fine spatial scale (200 m<sup>2</sup>), feral cat density was negatively associated with fox occurrence probability across both regions. These results were consistent with mesopredator release, although uncertainty was high in the region where fox control had only recently commenced.</p> <p>Feral cat detectability also varied across the (artificially-manipulated) gradients of fox occurrence probability. In one region, nonlinear models indicated that feral cats had lower detection and increased movement rates when foxes were uncommon, giving way to density suppression at high fox occurrence probabilities.</p> <p><em>Synthesis and applications.</em> Our study provides replicated, experimental evidence that dominant predator suppression can be associated with a higher mesopredator density. Mesopredator release can manifest as changes in both behaviour and density, distorting inference if these processes are not distinguished. Our results may help explain why fox control does not consistently improve native prey persistence, suggesting integrated pest management may be necessary to improve conservation outcomes.</p>
Data from: Phosphorus limitation determines the quality of dissolved organic matter released by marine heterotrophic prokaryotes
<p>We determined phosphorus (P) limitation effect on the quantity and quality of dissolved organic matter (DOM) released by heterotrophic prokaryotes (HP). We grew 2 single bacterial strains, Photobacterium angustum and Sphingopyxis alaskensis, and natural HP communities collected in fall and spring from the Mediterranean Sea, on glucose under 2 treatments: P-replete vs. P-limiting. DOM release by HP comprised up to 30 % of the initial carbon provided for growth. P availability influenced carbon allocation to different cellular processes (respiration vs. growth), but did not significantly affect the quantity of DOM released by HP. However, using fluorescence spectroscopy, we demonstrated an effect of P-limitation on DOM quality, with a predominance of humic-like compounds under P-limitation but protein-like compounds under P-repletion. Our results suggest that P-limitation could determine the fate of HP-derived DOM in the ocean, thus affecting the microbial carbon pump.</p>
Data release for "Measurements of neutrino oscillation parameters from the T2K experiment using 3.6E21 protons on target"
<p>This archive contains the electronic version in ROOT format of the measurements of oscillation parameters in the paper "Measurements of neutrino oscillation parameters using 3.6 \times 10^{21} protons on target with the T2K experiment". Its arxiv identifier is <a href="https://arxiv.org/abs/2303.03222">arXiv:2303.03222 [hep-ex]</a>, and Published in <a href="https://doi.org/10.1140/epjc/s10052-023-11819-x"><em>Eur. Phys. J. C</em> <strong>83</strong>, 782 (2023)</a>.</p> <p>**************************************<br>***** Results included in this release<br>**************************************<br>Both Bayesian and frequentist results are provided, with details of each analysis provided in the paper. All published oscillation parameters are provided, with 2D confidence/credible regions and 1D DeltaChi^2 and posterior probability density distributions. The Bayesian and frequentist results are separated in two different files ("Bayesian_DataRelase.root" and "Frequentist_DataRelease.root"), and an a tag in the TGraph and histogram names also allow to differentiate them: "cred" for credible interval from the Bayesian analysis, "conf" for confidence interval from the frequentist analysis. For the 1D distributions, the posteriors are Bayeisan results and the DeltaChi^2 are frequentist results.</p> <p>Results for each mass hierarchy hypothesis are provided, denoted "NH" for normal hierarchy and "IH" for inverted hierarchy. The Bayesian file also includes the results marginalised over the mass hierarchy, denoted by the tag "both" in the object names.<br>The Bayesian and frequentist results use different conventions for the mass splitting in the inverted hierarchy: the Bayesian results are in term of #Deltam^{2}_{32} for both normal (NH) and inverted (IH) hierarchies, whereas the frequentist results are plotted versus #Deltam^{2}_{32} for the NH, and |#Deltam^{2}_{31}| for the IH.</p> <p>When employed, the constraint on theta13 from reactor experiment results corresponds to the value in the PDG 2019 summary table: sin^2(theta_13)=(2.18+-0.07) x 10^{-2}. This is commonly referred to as "the reactor constraint".<br>Results marked "woRC" are without this reactor constraint, and "wRC" are with the reactor constraint.</p> <p>A glossary is provided at the end of this readme.</p> <p>Two example ROOT macros ("Bayesian_example.cpp" and "Frequentist_example.cpp") showcase how to extract information from the data release. These produce pdf files of the results that can be directly compared to the "*ref.pdf" files for validation.</p> <p>**************************************<br>***** Objects inside the ROOT files<br>**************************************<br>The ROOT objects contained inside the files are named first with an identifier of which parameter(s) are being shown, followed by the reactor constraint tag, followed by the mass hierarchy tag.<br>For the frequentist results, there's an additional "FC" tag, marking if critical DeltaChi^2 values have been computed with Feldman-Cousins ("FC") or using Wilks' theorem (constant DeltaChi^2).</p> <p>**************************************<br>*** 2D regions<br>**************************************<br>Objects of the form<br>gr2D_varX_varY_<wRC,woRC>_<NH,IH,both>_<conf,cred><68,90,955,997>(_N)<br>are TGraphs corresponding to the 2D confidence ("conf") or credible ("cred") regions for the 2 variables (varX, varY). N is the iterator for different TGraphs corresponding to the same region; these occur when confidence regions are discontinuous (for example when deltaCP loops over from +pi to -pi).<br>68, 90, 955, 997 are the percentage credible/confidence levels.</p> <p>The best fit markers are also provided for the 2D results:<br>gr2D_varX_varY_<wRC,woRC>_<NH,IH,both>_bestfit</p> <p>The best fit markers and contour lines are computed for each MH *separately*, i.e. assuming DeltaChi^2 is 0 at the minimum or that the total posterior probability integrates to 1 in the mass hierarchy considered. There is only one exception, some 2D regions for (sin^2(theta_23), dcp) are also provided using a best fit over both MH to allow for comparisons with other experiments using this convention. This special set of contours has an extra tag "globalMH" in its name to distinguish it from the others.</p> <p>For larger confidence/credible exclusion regions (e.g. 99.7%) and when the Bayesian analysis shows the result for dm2 for both hierarchies, the regions may be split in to discontinuous regions. They are named "_0" and "_1", and the value on the y-axis denotes dm^{2}_{23}, from which the hierarchy can be deduced. The examples show examples of how this can be acheived.</p> <p>**************************************<br>*** 1D plots<br>**************************************<br>Objects of the form<br>h1D_var<chi2,posterior>_<wRC,woRC>_<NH,IH><br>are TH1D of the DeltaChi^2 ("chi2") or posterior probability ("posterior") for oscillation parameter "var".</p> <p>The Bayesian and frequentist results use different conventions with respect to the mass hierarchy:<br>- 1D DeltaChi^2 plots use a global minimum over both hierarchies<br>- Each 1D posterior probability plot integrates to unity *individually*</p> <p>**************************************<br>***** Additional notes for frequentist results<br>**************************************<br>Most of the 2D frequentist regions were computed using the standard DeltaChi^2 values (from the Gaussian case), and not the Feldman-Cousins method. They therefore have only approximate coverage.<br>For the 2D distributions, only {sin^2(theta_23), deltaCP} with reactor constraint were computed using the Feldman-Cousins method, and are expected to have proper coverage. To distinguish them from other confidence regions, a tag "FC" is included in the name of the corresponding TGraph.<br>Additionally, those extra regions using Feldman-Cousins method are provided with two conventions regarding the best fit used to evaluate them. The TGraphs with an extra tag "globalMH" use a best fit over both MH hypothesis. The ones without this extra tag use the best fit obtained in each MH to compute the confidence regions for this MH.</p> <p>For the 1D plots, critical delta chi2 values obtained with the Feldman-Cousins method are provided for theta23 and deltaCP (with reactor constraint "wRC" case only):<br>grCritical_{variable}chi2_wRC_{MH}_conf{CL}<br> variable: th23, dCP<br> MH: NH, IH<br> CL: 68, 90, 955, 997</p> <p>To obtain the FC-corrected confidence interval in those 2 cases for a given confidence level, take the intersection of grCritical with the corresponding 1D histogram. This is shown in the example macros.</p> <p>**************************************<br>***** Additional notes for Bayesian results<br>**************************************<br>For plots involving the mass splitting, the choice of hierarchy is given by the sign:<br> dm32>0 is normal hierarchy (Delta m^2_{32} > 0)<br> dm32<0 is inverted hierarchy (Delta m^2_{32} < 0)</p> <p>For the Jarlskog invariant, the prior on deltaCP is either flat in deltaCP, or flat in sindeltaCP ("flatsindcp")</p> <p>Note that the posteriors have not been smoothed, and may contain small discontinuities due to MCMC statistical uncertainties, e.g. in "h1D_dCPposterior_wRC_IH" around delta CP=-1.47.</p> <p>Plots with "_bestfit" appended signify the point in the space with the highest posterior density, and is not necessarily the global minimum of the test-statistic.</p> <p>For the 1D posterior distributions, the user can freely calculate credible intervals from the distributions. It is recommended to start at the point of the highest posterior density, and moving down in posterior density to produce asymmetric credible intervals. The root macro "Bayesian_example.cpp" shows a method to do this.</p> <p>**************************************<br>***** Glossary<br>**************************************</p> <p>"RC" - Reaction Constraint from PDG 2019 sin^2(theta_13)=(2.18+-0.07) x 10^{-2}.<br>"wRC" - With Reactor Constraint<br>"woRC" - Without Reactor Constraint<br>"FC" - Feldman-Cousins<br>"NH" - Normal Hierarchy<br>"IH" - Inverted Hierarchy<br>"both" - Marginalised over normal and inverted hierarchy<br>"cred" - Credible interval<br>"conf" - Confidence interval<br> "68" - 68% (1 sigma)<br> "90" - 90%<br> "955" - 95.5% (2 sigma)<br> "997" - 99.7% (3 sigma)<br>"chi2" - DeltaChi^2 (-2lnL) for parameter<br>"Critical" - Critical DeltaChi^2 computed with Feldman-Cousins</p> <p>"th13" - sin^2(theta_13)<br>"th23" - sin^2(theta_23)<br>"dCP" - delta CP<br>"dm2" - Delta m^2_{23} (NH), |Delta m^2_{13} (IH)| for confidence intervals; used in frequentist analysis.<br>"dm32" - Delta m^{2_{23} regardless of hierarchy; in the Bayesian analysis Delta m^2_{23} is always plotted.<br>"jarlskog" - Jarlskog invariant, only in Bayesian analysis<br>"flatsindcp" - Flat in sin delta CP</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.