Find research datasets worth reusing

opencc-by-4.0Jul 2022View details →

Near infrared imaging data from brown rot decayed wood

This dataset contains near infrared imaging data from the following publication: Belt, T.; Awais, M.; Mäkelä, M. (2022) Chemical characterization and visualization of progressive brown rot decay of wood by near infrared imaging and multivariate analysis. Frontiers in Plant Science 13:940745. DOI: 10.3389/fpls.2022.940745. Details on the samples, the decay test, and the image collection parameters can be found in the publication. The “Sample IDs and mass losses.cvs” file contains the sample ID and mass loss due to decay of each sample in the dataset. The “C puteana.mat” and “R. placenta.mat” files contain the near infrared imaging data of samples exposed to the fungus Coniophora puteana and the fungus Rhodonia placenta, respectively, organised into cell arrays of sample IDs and corresponding image files. To generate the image files, a region of interest of 551 x 384 pixels was selected from the raw image files to produce an image that contains the sample surrounded by background. The spectral data were then converted to reflectance and corrected using the calibration reflectance target values.

opencc-by-4.0May 2024View details →

Data and script for "On the emergence of ecosystem decay: a critical assessment of patch area effects across spatial scales"

Data and R script necessary to replicate the results of Riva et al. 2024 ("On the emergence of ecosystem decay: a critical assessment of patch area effects across spatial scales"; minor revisions, Biological Conservation).

opencc-by-4.0Jul 2024View details →

Measurement of the bound-state beta decay of 205Tl(81+): intermediate and result data

The data presented here is the intermediate and result data from the measurement of the bound-state beta decay of 205Tl(81+), experiment G-20-0E121, which was performed at the Experimental Storage Ring (ESR) at the GSI Helmholtzzentrum für Schwerionenforschung, Darmstadt (Germany) in the frame of FAIR Phase-0. The experimental measurement was done from the 26th March 2020 to 6th April 2020. Intermediate Data: During the experiment, the ESR monitored the beam via three main detectors: the non-destructive 245 MHz Schottky resonator, the DC Current Transformer (DCCT), and a Multi-Wire Proportional Chamber (MWPC). In particular: <ol> <li>Schottky data: the integrated Schottky noise power density for the 205Tl(81+) peak and the 205Pb(82+) peak is provided for each storage measurement. The full Schottky spectrum can be made available on request. As described in the related works below, the Schottky data became saturated above a certain threshold due to a mismatched amplifier in the NTCAP DAQ. This results in non-exponential decay of Schottky peaks for high intensities. </li> <li>DCCT data: the entire beam current in the ring was monitored using the DCCT. The DCCT was recorded using a scalar counter and thus has a non-zero offset value, which we determined to be 28.199 µA from a period with no beam. The DCCT is intended to be used as a diagnostic tool, and is not as precise as other, purpose-built detectors.</li> <li>MWPC data: for most storage measurements, a MWPC detector was placed downstream of the gas target on the outside of the ring to detect electron recombination products. The provided data is the detection rate on the anode.</li> <li>The gas target density, as recorder by a scaler counter, is also included.</li> </ol> The intermediate data on all three of these detectors plus the gas target density is provided in the tar.gz repository, with individual ROOT files for each storage time. The ROOT files have the naming format "Storage time_MMDD_HH.root". Each file contains 5 TGraph objects. Result Data: The result data provides all the necessary individual measurement and correction values to extract a bound-state beta-decay rate from the corrected ratios. It is provided in two forms: <ol> <li>BSBD_205Tl-result_data.ods is an ODS table for easy visualisation.</li> <li>BSBD_205Tl-final_vals.txt is a text file used by the Monte Carlo analysis script provided in <a href="https://doi.org/10.5281/zenodo.11560338" target="_blank" rel="noopener">DOI 10.5281/zenodo.11560338</a>.</li> </ol> The "Ratio" column is the corrected 205Pb/205Tl decay ratio for each storage, following Equation (1) of <a href="https://www.nature.com/articles/s41586-024-08130-4" target="_blank" rel="noopener">Leckenby et al. (2024) Nature 635:321–326</a>. 1 sigma error bars, both including and not-including the estimated contamination variation, are provided. Please refer to the related works below or contact the authors for more details.

opencc-by-4.0Mar 2023View details →

Top quark pair production at the LHC, with all hadronic resolved decays for solving event combinatorics

R&D Datasets for solving event combinatorics in all hadronic top quark pair events at the LHC. Used in the development of Topographs: Topological Reconstruction of Particle Physics Processes using Graph Neural Networks   The datasets contain 5.8M ttbar events in the all hadronic decay channel, with jets matched to the truth partons in the top quark decays.   Event generation <ul> <li>Centre of Mass energy: 13 TeV</li> <li>MC Generator: MadGraph5_aMC@NLO v3.1.0, with MadSpin modelling the decays of the top quarks and W bosons.</li> <li>Parton Shower: Pythia v .243</li> <li>Detector response: Delphes v3.4.2 using ATLAS-like geometry</li> <li>Jets reconstructed with anti-kt algorithm, R=0.4, using FastJet</li> <li>b-Tagging corresponds to inclusive 70% b-jet efficiency</li> </ul> Event selection and truth matching <ul> <li>All events are required to have at least six reconstructed jets and exactly zero leptons (electrons or muons)</li> <li>Partons are matched to jets using $\Delta R$ matching, with $\Delta R < 0.4$</li> <li>Events with partons matched to multiple jets or jets to multiple partons are discarded</li> <li>Up to 16 jets are stored per event</li> </ul> In the training dataset 1,340,000 events have all partons from the ttbar decays matched to jets. In the validation dataset, 71,000 events have all partons from the ttbar decays matched to jets. In the testing dataset 76,000 events have all partons from the ttbar decays matched to jets. Dataset format The dataset is in h5 format and the key 'delphes' has the following numpy arrays: <pre><code>jets (16), jets_indices (16), matchability, nbjets, njets, partons (10) </code></pre> Jets structured numpy array per event: <ul> <li> <pre><code>(pt, eta, phi, energy, is_tagged)</code></pre> </li> </ul> Jets_indices: <ul> <li>Integer corresponding to the parton the jet is matched to</li> <li>From 0 to 5: b1 W1j1 W1j2 b2 W2j1 W2j2 (1= from top, 2=from antitop)</li> <li>-1 indicates not matched to a parton</li> <li>Properties of matched partons can be obtained from the partons array</li> </ul> matchability: <ul> <li>Which partons are matched to jets in event</li> <li>Binary representation with bits corresponding to each parton (length 6) 0b111111</li> <li>From left to right: b1 W1j1 W1j2 b2 W2j1 W2j2</li> <li>0b111000 (56) is one top fully matched, 0b000111 (7) is the other top fully matched, 0b111111 (63) is both tops fully matched</li> </ul> njets, nbjets: <ul> <li>How many jets/bjets in event</li> </ul> partons: <ul> <li>List of truth particles from ttbar decay: tops, Ws, quarks, ordered by top quark and its decays followed by anti-top and its decays</li> <li> <pre><code>PDGID, pt, eta, phi, mass</code></pre> </li> </ul>

opencc-by-4.0Apr 2023View details →

Dataset of paper "GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions"

DFEI dataset The full description can also be found in README.md. The dataset was used in the paper “GNN for Deep Full Event Interpretation and hierarchical reconstruction of heavy-hadron decays in proton-proton collisions”. The project describes a full event interpretation at the LHCb experiment, situated at the Large Hadron Collider in CERN, Geneva. An “event” consists of detector responses that were converted to tracks - each track represents a particle. The aim of the algorithm is to make sense of the tracks and bundle together tracks coming from the same origin, as well as interpreting their decay hierarchy. Generated events The events in this dataset are based on simulation generated with <a href="https://www.pythia.org/">PYTHIA8</a> and <a href="https://evtgen.hepforge.org/">EvtGen</a>, in which the particle-collision conditions expected for the LHC Run 3 are replicated as shown in the table. <table> <thead> <tr> <th>LHCb period</th> <th>Num. vis. pp collisions</th> <th>Num. tracks</th> <th>Num. b hadrons</th> <th>Num. c hadrons</th> </tr> </thead> <tbody> <tr> <td>Runs 3-4 (Upgrade I)</td> <td> &sim; 5</td> <td> &sim; 150</td> <td> ≪ 1</td> <td> &sim; 1</td> </tr> </tbody> </table> Additionally, an approximate emulation of the LHCb detection and reconstruction effects is applied, as described in the paper in the appendix “Simulation”. In the generated dataset, each event is required to contain at least one b-hadron, which is subsequently allowed to decay freely through any of the standard decay modes present in PYTHIA8. On average, 40% of those events contain more than one b-hadron decay, with a maximum b-hadron decay multiplicity of five. Only charged stable particles that have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region (as defined in the paper) are included in the datasets. Datasets The datasets are divided in three categories Training and testing The file <code>Dataset_InclusiveHb_Training.root</code> contains the training dataset (40,000 events) test dataset (10,000 events) of inclusive decays. Evaluation The inclusive dataset <code>Dataset_InclusiveHb_Evaluation.root</code> contains the evaluation events (50,000). Exclusive decays In addition to this inclusive dataset, several other smaller samples (of few thousand events each) have also been generated, requiring that all the events in each sample contained a specific (exclusive) type of b-hadron decay. The specific modes have been chosen to be representative of the most common classes of decay topologies of physics interest for LHCb. These samples contain only events in which all the particles originating from each of the considered exclusive decays have been produced inside the LHCb geometrical acceptance and in the Vertex Locator region. The datasets contained are: <ul> <li><code>Dataset_Bd_DD.root</code></li> <li><code>Dataset_Bd_Kpi.root</code></li> <li><code>Dataset_Bd_Kstmumu.root</code></li> <li><code>Dataset_Bs_Dspi.root</code></li> <li><code>Dataset_Bs_Jpsiphi.root</code></li> <li><code>Dataset_Bu_KKpi.root</code></li> <li><code>Dataset_Lb_Lcpi.root</code></li> </ul> More information on them can be found in the paper. Loading the data The dataset is saved in the binary ROOT format with a key-array mapping. It can be loaded using the <a href="https://github.com/scikit-hep/uproot5#readme">uproot</a> Python library to convert it to a pandas DataFrame or similar. An example snippet is given here: <pre><code>import uproot # treename = "Particles" treename = "Relations" with uproot.open('/path/to/file.root') as file: df = file[treename].arrays( # we can specify only a set of branches # ['EventNumber', "FromSamePV_true"], library='pd') # 'pd' for pandas </code></pre> The returned <code>file</code> behaves like a mapping that contains two different data holders. They are accessible with <code>Relations</code> or <code>Particles</code> that contain either the relations between the particles or the particles themselves. Regarding the <code>Relations</code>, only edges connecting two different particles are contained in the dataset. The edges are treated as not directional, so a single edge is considered for each pair of particles. Variables The relevant features used in the GNN are described in the following. A cartesian right-handed coordinate system is used, with the z axis pointing along the beamline, the x axis beinng parallel to the horizontal and the y axis being vertically oriented. When specified in the name of the variables, the suffix “_true” refers to ground-truth information, and the suffix “_reco” refers to the output of the emulated LHCb reconstruction. <ul> <li> General: <ul> <li>EventNumber: unique number to identify the event that the entry belongs to.</li> </ul> </li> <li> Node variables: <ul> <li> ParticleKey: unique number to identify each particle in a given event. </li> <li> Identity (ID): numerical code identifying the type of particle, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>. </li> <li> FromPrimaryBeautyHadron: boolean variable indicating whether the particles has been produced in a beauty hadron decay or not. </li> <li> Transverse momentum (pT): component of the three-momentum transverse to the beamline, i.e. the x and y component combined. </li> <li> Impact parameter with respect to the associated primary vertex (IP): distance of closest approach between the particle trajectory and its associated primary vertex (proton-proton collision point), defined as the one with the smallest IP for the given particle amongst all the primary vertices in the event. </li> <li> Pseudorapidity (η): spatial coordinate describing the angle of a particle relative to the beam axis, computed as η = arctanh(pz/∥p⃗∥). </li> <li> Charge (q): for the stable particles under consideration, the charge can take the value 1 or -1. </li> <li> Ox, Oy, Oz: cartesian coordinates of the origin point of the particle. </li> <li> px, py, pz: cartesian coordinates of the three-momentum. </li> <li> PVx, PVy, PVz: cartesian coordinates of the position of the associated primary vertex. </li> </ul> </li> <li> Edge variables: <ul> <li> FirstParticleKey: ParticleKey of one of the two particles connected by the edge. </li> <li> SecondParticleKey: ParticleKey of the other particle, verifying FirstParticleKey > SecondParticleKey. </li> <li> FromSamePrimaryBeautyHadron: boolean variable indicating whether the two particles originate from the same beauty hadron decay. </li> <li> Opening angle (θ): angle between the three-momentum directions of the two particles. </li> <li> Momentum-transverse distance (d &perp; P⃗): distance between the origin point of the two particles defined on a plane which is transverse to the combined three momentum of the two particles. </li> <li> Distance along the beam axis (Δz): difference between the z-coordinate of the origin points of the two particles. </li> <li> FromSamePV: boolean variable indicating whether the two particles share the same associated primary vertex. </li> <li> Order of the “topological” Lowest Common Ancestor (TopoLCAOrder): variable that can take the values 0, 1, 2 or 3, as explained in the paper. </li> <li> Identity of the “topological” Lowest Common Ancestor (TopoLCAID): numerical code identifying the particle type of the ancestor, following the <a href="https://pdg.lbl.gov/2019/reviews/rpp2019-rev-monte-carlo-numbering.pdf">Monte Carlo Particle Numbering Scheme</a>. </li> </ul> </li> </ul>

opencc-by-4.0Jun 2023View details →

Dataset for "Light Scalar Meson and Decay Constant in SU(3) Gauge Theory with Eight Dynamical Flavors"

Decoding File Names: Consider the file name f8l24t48b48m00889_S0.csv.  We will break down the meaning of the various pieces of the filename <ul> <li>"f8" means 8 Dirac flavors.</li> <li>"l24t48" means 243×48 lattice.</li> <li>"b48" means beta=4.8, related to the inverse bare gauge coupling.</li> <li>"m00889" means fermion mass m=0.00889.</li> <li>"S" means flavor-singlet scalar meson. Other options are "P" for flavor non-singlet pseudoscalar meson and "C" for flavor non-singlet scalar meson.</li> <li>"0" an integer from 0 to 4 proportional to the squared length of the spatial momentum vector of the correlation function.</li> </ul> Columns of the CSV files: Each line of the CSV file should contain 41 entries, separated by commas. Refer to the Eq. (8) which defines model A in the accompanying paper to understand the physical interpretation of these parameters. <ol> <li>Model number: 1 is model A, 2 is model B, 3 is model C.</li> <li>nmax: the number of non-oscillating states in the fit.</li> <li>jmax: the number of oscillating states in the fit.</li> <li>tmin: the minimum t value used in the fit.</li> <li>tmax: the maximum t value used in the fit.</li> <li>𝜒2 of the fit.</li> <li>$\log\ p\left(\left.M\right|D\right)$: log of model probability used in Bayesian model averaging.</li> <li>fit value for c0 (model A) or $\overline{c}_0$ (model B).</li> <li>fit error for c0 (model A) or $\overline{c}_0$ (model B).</li> <li>fit value for c1.</li> <li>fit error for c1.</li> <li>fit value for c2.</li> <li>fit error for c2.</li> <li>fit value for c3.</li> <li>fit error for c3.</li> <li>fit value for c4.</li> <li>fit error for c4.</li> <li>fit value for $c_1^\prime$.</li> <li>fit error for $c_1^\prime$</li> <li>fit value for $c_2^\prime$.</li> <li>fit error for $c_2^\prime$.</li> <li>fit value for $c_3^\prime$.</li> <li>fit error for $c_3^\prime$.</li> <li>fit value for $c_4^\prime$.</li> <li>fit error for $c_4^\prime$.</li> <li>fit value for $\log(E_2 - E_1)$.</li> <li>fit error for $\log(E_2-E_1)$.</li> <li>fit value for $\log(E_3-E_2)$.</li> <li>fit error for $\log(E_3-E_2)$.</li> <li>fit value for $\log(E_4-E_3)$.</li> <li>fit error for $\log(E_4-E_3)$.</li> <li>fit value for $\log(E_2^\prime - E_1^\prime)$.</li> <li>fit error for $\log(E_2^\prime - E_1^\prime)$.</li> <li>fit value for $\log(E_3^\prime - E_2^\prime)$.</li> <li>fit error for $\log(E_3^\prime - E_2^\prime)$.</li> <li>fit value for $\log(E_4^\prime - E_3^\prime)$.</li> <li>fit error for $\log(E_4^\prime - E_3^\prime)$.</li> <li>fit value for $\log(E_1)$.</li> <li>fit error for $\log(E_1)$.</li> <li>fit value for $\log(E_1^\prime)$.</li> <li>fit error for $\log(E_1^\prime)$.</li> </ol>

opencc-by-4.0Nov 2022View details →

Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions - data

Data set pertaining to the manuscript "Radiation damage hot spots formed by two-step electron transfer mediated decay of solvated ions", accepted for publication in Nature Chemistry. Files with extension .h5 are hdf5-files structured according to the NeXus standard v2022.07, see https://www.nexusformat.org/ https://fairmat-experimental.github.io/nexus-fairmat-proposal/50433d9039b3f33299bab338998acb5335cd8951/mpes-structure.html NeXus data files can be opened with any software capable of opening hdf5-structured files. The following viewers are adapted to the specifics of the NeXus data format: * nexpy (distributed with python) * https://h5web.panosc.eu/h5wasm (web-based NeXus viewer maintained by the European Photon and Neutron Open Science Cloud-consortium) In each NeXus file-entry, two types of spectra are shown: 1. Sweep-averaged spectra, integrated over the non-dispersive coordinate of our detector ('data') if applicable. 2. As-measured data ('raw'). Files with extension .csv are comma-separated ascii-files, designed to be opened with a spreadsheet programme. The following files are provided: Photoemission data pertaining to ETMD measurements: alcl3-K-etmd.h5        (ETMD after Al K-shell photoionization) alcl3-L23-etmd.h5        (ETMD after Al L-shell photoionization) Calculated energies of the ETMD final states after 1s ionization. The energies were calculated at the CAS-CI/cc-pVDZ level. The states were shifted so that the lowest-energy state corresponds to the LC-ωPBE/aug-cc-pVTZ and aug-cc-pCVTZ value obtained in a polarizable continuum: Dataset_ETMD_after_1s_ionization.csv Dataset_ETMD_after_2p_ionization.csv Geometrical coordinates of the clusters that were used for energy calculation: clusters.dat clusters_small.dat Contact: Uwe Hergenhahn, uhe@fhi.mpg.de .   Version history: v3 - Al L2,3 data: Orientation of the analyser hemisphere corrected. Direction of the linear polarization vector added. All other data unchanged. v2 - cluster coordinates added, all other data unchanged. v1 - initial upload.

opencc-by-4.0Sep 2023View details →

Flavor-violating Higgs decays and stellar cooling anomalies in axion models

We study a class of DFSZ-like models for the QCD axion that can address observed anomalies in stellar cooling. Stringent constraints from SN1987A and neutron stars are avoided by suppressed couplings to nucleons, while axion couplings to electrons and photons are sizable. All axion couplings depend on few parameters that also control the extended Higgs sector, in particular lepton flavor-violating couplings of the Standard Model-like Higgs boson h. This allows us to correlate axion and Higgs phenomenology, and we find that BR(h → τe) can be as large as the current experimental bound of 0.22%, while BR(h → μμ) can be larger than in the Standard Model by up to 70%. Large parts of the parameter space will be tested by the next generation of axion helioscopes such as

openCC (other)Jun 2018View details →

Long-term log decay experiments at the Andrews Experimental Forest, 1985 to 2185

These data are collected from six sites within the H. J. Experimental Forest and are part of a 200-year experiment on the decomposition of logs in a terrestrial environment. The taxa being examined in the experiment are Douglas-fir, western hemlock, western redcedar, and Pacific silver fir. The data start in 1985 and are periodically updated as samples are taken. The last collection of data is planned in 2185. Periodically logs are sampled by measuring their diameters and lengths as well as bark cover and the fraction of their length in contact with the ground. Cross-sections are removed with a chainsaw and dissected with a table saw or other means to determine the density of bark and wood samples. These samples systematically sample each cross-section and can be used to reconstruct the spatial pattern of decomposition. Subsamples are ground and nutrient concentrations are determined to calculate nutrient stores. Twenty one data tables are associated with this study. A general description of the logs (length, diameters, bark cover) is found in td01401 and can be used to estimate how the log has fragmented over time. The areal extent of tissue types (outer bark, inner bark, sapwood, and heartwood) and zones of rotten wood from cross-sections is documented in td01418 and td01419. The position of the pith in each cross-section, a useful indicator of volume loss, is documented in td01420. The density (dry mass/green volume) of samples is documented in td01403 and based on the weights of individual samples (td01404) as well as their dimensions (td01405). The arrangement of samples into transects (top to bottom, left to right) within the cross-sections is documented in td01406. To help explain why some samples may be more dense than others, the area of the sample cross-section covered by knots is documented in td01407 (knots are more decay resistant and of greater density than wood). The radial thickness of inner and outer bark of the undecayed logs is documented in td014

openCC (other)Aug 2018View details →

Fine wood decay studies at the H.J. Andrews and other Forests across the world, 1989 to 2006

This study was established to examine the loss of mass and changes in nutrient content of fine woody debris in various ecosystems. Also examined was the effect of piece size, position (aboveground, belowground, or suspended), and species on loss of mass over time.

openCC (other)Nov 2023View details →

Interactions between plants and fungi and their roles in decay rates and CO2 release in five tropical leaf species

A microcosm experiment was used to test for the effects of interactions between particular plant and fungal decomposer species on rates of leaf decomposition. Each microcosm contained one species of leaf that was sterilized with gamma irradiation and then inoculated with a single fungus. Five plant species and ten fungal species (two dominants from each of the litter types) were used in all possible combinations. Plant species were selected for pair-wise comparisons based on phylogenetic relationships and litter quality characteristics. Decomposition was measured by both mass loss and CO2 release. Differences in weight loss and CO2 evolution were highly significant for plants, fungal species, and their interactions. Mass loss was positively correlated with CO2 evolution. Contrary to our hypotheses, however, microfungal dominants did not decompose their source leaves faster than microfungal dominants from other leaf species, nor were responses to other types of specificity detected. Matching of fungi to leaf substrates by their source, by phylogenetic relationships, or by chemical, physical and structural characteristics was not associated with consistent increases in decomposition. Although previously documented differences in microfungal species composition and dominance among decomposing leaves of different trees were confirmed in this study, such differences apparently do not directly affect the rates of ecosystem processes. The presence in a few of the microcosms of a generalist basidiomycete that had ligninolytic enzymes, Melanotus eccentricus, significantly accelerated the rate of decomposition. Non-specific basidiomycetes may therefore have a stronger effect on early stages of leaf litter decomposition than host-selective microfungi. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Pue

openCC (other)Apr 2025View details →

Wood to Soil 0-10 cm data and Wood to Soil 10-20 cm data to detect the imprint of decaying logs (30-80 cm diameter) from two hurricane cohorts (Hugo, 1989, and Georges, 1998)

Many trees fell during Hurricanes Hugo (1989) and Georges (1998) in Puerto Rico. A debris removal experiment suggested that coarse woody hurricane debris slowed canopy recovery by fueling microbial nitrogen immobilization. We analyzed C, N, microbial biomass C and root length in paired soil samples taken under versus 20-50 cm away from large trunks of two species felled by Hugo and Georges three times during wet and dry seasons during the two years after Georges. Data on soil P and other nutrients have not yet been analyzed. Soil microbial biomass, C and N were higher under than near logs of both age cohorts. Frass from wood boring beetles may induce the early effects. Root length was greater under logs at 0-10 cm depth during the dry season, and away from logs in the wet season, but varied independently of microbial biomass. Thus decaying wood can provide resources exploited by tree roots. Percent soil C and N were significantly higher under than near logs in both the 0-10 and 10-20 cm samples. Microbial biomass C varied significantly among seasons at 0-10 cm depth but differences between positions (under vs away) were only suggestive. Surface soil on the upslope side of the logs had significantly more N and microbial biomass, likely from accumulation of leaf litter above the logs on steep slopes. This study shows that C and N accumulate significantly more in soil under than near decaying logs, even in logs that had only decayed for 7 months, and thus contributes to soil heterogeneity. Tree roots track and exploit resource and nutrient hotspots as they change locations between seasons, so the soil heterogeneity in soil fertility is important for forest productivity. Soil phosphorus (P) availability is most often the most limiting nutrient in wet tropical forests. Total soil P was measured by complete digestion in samples from the upper 10 cm; Olsen extractable P (available) was also measured. Total soil P concentrations were significantly greater under than away fr

opencc-by-4.0Dec 2019View details →

Preliminary Supplementary Information for "Kinetics of Deoxyribose-1-Phosphate Decay in Aqueous Solution"

This is the dataset for our upcoming publication tenatively titled "Kinetics of Deoxyribose-1-Phosphate Decay in Aqueous Solution" and may serve as a preliminary Supplementary Information. We employed high-throughput UV spectroscopy-based monitoring of the apparent conversion of deoxyribosyl nucleoside phosphorolysis to access the kinetics of deoxyribose-1-phosphate hydrolysis in aqueous solution at different pH values and temperatures. Please see the files below for a general description of this entry and the full dataset(s).

openmit-licenseDec 2023View details →

Supplementary Data for Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay

This dataset contains the interpolation tables for use with the DM21cm code release as part of "Inhomogeneous Energy Injection in the 21-cm Power Spectrum: Sensitivity to Dark Matter Decay." For details on usage, see the public github repository at: https://github.com/yitiansun/DM21cm.

opencc-by-4.0Oct 2023View details →

Additional Data: Poised PABP-RNA hubs implement signal-dependent mRNA decay in development

This repository contains processed data resulting from iCLIP experiments that were analysed in the following paper:"Poised PABP-RNA hubs implement signal-dependent mRNA decay in development" The paper is published at Nature Structural and Molecular BIology. <h2> Archived data</h2> Data archived in this repository include: <ol> <li>Data derived from iCLIP experiments targeting LIN28A, PABPC1, and PABPC4, that were analysed in the manuscript (see iCLIP.zip). Raw data is available from ENA, with the accession code PRJEB60519. <ol> <li>Sample descriptions are given in iCLIP-SampleAnnotation.csv</li> <li>Crosslink files in BED6 format (individual replicates and merged replicates)</li> <li>Peak files generated with the Clippy peak caller in BED6 format</li> <li>K-mer enrichment around high-confidence crosslink sites in the 3'-UTRs, calculated by the PEKA software</li> </ol> </li> <li>Expression values (salmon quantfiles)  for 3'-seq experiments, specified in "QuantseqExperimentsAnnotation.tsv", are available in "SalmonQuantfiles.zip". Raw data is available from ENA, with the accession code PRJEB60519.</li> <li>Source code of the nextflow pipeline, which was used on the iMaps webserver to analyse iCLIP data and produce the files archived here (see imaps-nf-0.30.zip).</li> <li>A list of naive genes, that were analysed in the manuscript (see NaiveGeneIds.csv).</li> </ol> <h2>Details on iCLIP data generation</h2> iCLIP data for LIN28A-WT (in 2iL and FGF2 treated cells), LIN28A-S200A (in FGF2 treated cells) as well as for PABPC1 and PABPC4 (in LIN28A KO cells with and without LIN28A overexpression), were analysed on iMaps Goodwright server (<a href="https://imaps.goodwright.com/">https://imaps.goodwright.com/</a>). The LIN28A iCLIPs were analysed on 18th of July, 2022; the PABPC iCLIPs were analysed on 26th of December, 2022. The code and settings used in the pipeline (release v0.30) can be viewed at <a href="https://github.com/goodwright/imaps-nf">https://github.com/goodwright/imaps-nf </a>, and is also archived here - (imaps-nf-0.30.zip)   <ul> <li>First, reads were demultiplexed using Ultraplex and barcodes were trimmed from the reads. The default Ultraplex settings were applied, as denoted below:</li> </ul> <blockquote> adapter='AGATCGGAAGAGCGGTTCAG' adapter2='AGATCGGAAGAGCGTCGTG' barcodes='barcode.csv', final_min_length=20 fiveprimemismatches=1 ignore_no_match=False ignore_space_warning=False inputfastq='MOD4878A1-merged.fastq.gz', keep_barcode=False, min_trim=3, outputprefix='demux', phredquality=30, phredquality_5_prime=0, sbatchcompression=False, threads=10, threeprimemismatches=0, ultra=False </blockquote>   <ul> <li>TrimGalore was used to run FASTQC and quality trim the reads and remove reads with length less than 10 nt:</li> </ul> <blockquote> trim_galore --fastqc --length 10 -q 20 --cores 8 --gzip file.fastq.gz </blockquote>   <ul> <li>Reads were then premapped to rRNA, tRNA sequences referred to as small RNA, smRNA, using mouse genome build (GRCm39 GENCODE M28 annotation) with Bowtie v1.3.0 (Langmead et al., 2009)</li> </ul> <blockquote> bowtie --threads 12 --sam -x $INDEX -q --un file.unmapped.fastq -v 2 -m 100 --norc --best --strata file.fq.gz 2 </blockquote>   <ul> <li>Reads that did not map with Bowtie were then aligned with STAR v2.7.9a (Dobin et al., 2013) to mouse genome build (GRCm39 GENCODE M28 annotation).</li> </ul> <blockquote> STAR \ --genomeDir star \ --readFilesIn file.unmapped.fastq.gz \ --runThreadN 12 \ --outFileNamePrefix 1_R1. \ \ --sjdbGTFfile Homo_sapiens_filtered.gtf \ --outSAMattrRGline 'ID:1_R1' 'SM:1_R1' \  --readFilesCommand zcat --outSAMtype BAM SortedByCoordinate --quantMode TranscriptomeSAM --outFilterMultimapNmax 1 --outFilterMultimapScoreRange 1 --outSAMattributes All --alignSJoverhangMin 8 --alignSJDBoverhangMin 1 --outFilterType BySJout --alignIntronMin 20 --alignIntronMax 1000000 --outFilterScoreMin 10 --alignEndsType Extend5pOfRead1 --twopassMode Basic </blockquote>   <ul> <li>PCR-duplicates were removed using UMI-tools (Smith, Heger and Sudbery, 2017)</li> </ul> <blockquote> java -jar /UMICollapse/umicollapse.jar \   bam \   -i file.Aligned.sortedByCoord.out.bam \   -o file.dedup.bam \   --umi-sep rbc: </blockquote>   <ul> <li>The nucleotide preceding each sequencing read was assigned as the crosslink event.</li> </ul>   <ul> <li>Peaks of crosslinking signal were identified with Clippy v1.4.1, using the default settings.</li> </ul>   <ul> <li>Obtained peaks and crosslink sites were used to run PEKA v1.0.0 (Kuret et al., 2022), using the default settings.</li> </ul>   <ul> <li>For Clippy and PEKA, the GENCODE primary assembly annotation M28 was filtered to retain only entries with transcript support level 1 or 2, in genes where such transcripts were available, and used to produce a segmentation file with the get_segments function from the iCount tool (Curk, 2019).</li> </ul>   <ul> <li>All files generated during data processing are available from the iMaps Goodwright webserver for analysis of CLIP data (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively).</li> </ul> <h2>Source data</h2> Raw sequencing reads, from which the data enclosed here were derived, are accessible at ENA (PRJEB60519). The raw sequencing reads and all data produced by the analysis pipeline is also available at the iMaps webserver (see <a href="https://imaps.goodwright.com/collections/882635250203/">https://imaps.goodwright.com/collections/882635250203/</a> and <a href="https://imaps.goodwright.com/collections/340215254997/">https://imaps.goodwright.com/collections/340215254997/</a> for LIN28A and PABPC1/4 iCLIPs, respectively); and on the updated Flow webserver (see <a href="https://app.flow.bio/projects/882635250203/">https://app.flow.bio/projects/882635250203/</a> and <a href="https://app.flow.bio/projects/340215254997/">https://app.flow.bio/projects/340215254997/ </a>for LIN28A and PABPC1/4 iCLIPs, respectively). <h2>Downstream computational analysis of enclosed data</h2> The code, used to analyse the data enclosed here and train the CNN to predict transcript stability in naive-to-primed transition based on 3'UTR nucleotide sequence, is available at GitHub (<a href="https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics">https://github.com/ulelab/LIN28A_RNPreassembly_bioinformatics</a>) and archived on Zenodo (<a href="../doi/10.5281/zenodo.10054297">https://zenodo.org/doi/10.5281/zenodo.10054297</a>).

opencc-by-4.0Feb 2022View details →

Proton collision producing top pair, decaying hadronically via bottom quarks and W bosons

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opencc-by-4.0May 2022View details →

Supplementary Data: Cosmological constraints on decaying axion-like particles: a global analysis

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opencc-by-4.0Oct 2022View details →

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opencc-by-4.0Jul 2019View details →

Datasets for ``Dynamo effect in decaying helical turbulence''

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