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10,735 results for “dependencies”
Classification of GTP-dependent K-Ras4B active and inactive conformational states
<p>Dataset for the paper: Classification of GTP-dependent K-Ras4B active and inactive conformational states.</p> <p>Cite as: J. Chem. Phys. 158, 000000 (2023); DOI: 10.1063/5.0139181<br> Submitted: 18 December 2022; Accepted: 13 February 2023; Published Online: 13 February 2023.</p> <p>All molecular dynamics and molecular docking data presented, analyzed, and discussed in this paper are available at reasonable<br> requests submitted to the corresponding author. The following data are also available online: (i) file KRas4B_pdbs.zip, a compressed<br> archive including coordinate (.pdb) and structure (.psf) files for KRas-4B WT and D33E proteins, (ii) KRas4B_WT_traj.zip,<br> a compressed archive including trajectory files (.trr) for the WT KRas-4B runs, including 120 "*.trr"-formatted trajectories each corresponding to 40 ns of MD simulation time, and (iii) a sample Python script to generate a free energy plot as shown in Fig. 2.</p>
Dataset for: Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus 'Methylomirabilis' in stratified lake basins
<p>Dataset containing treated 16S rRNA amplicon sequence data, accompanying the manuscript "Water column dynamics control nitrite-dependent anaerobic methane oxidation by Candidatus ‘Methylomirabilis’ in stratified lake basins" </p> <p>Files: </p> <p>- Mapping file</p> <p>- ASV table</p> <p>- Refseq file</p> <p>-Tree file</p> <p>- Relative abundances of dominant methanotrophs in the water column of Lake Lugano North Basin (used to create Fig. 6c)</p> <p>- Multi annual dataset of water chemistry data of the Lake Lugano North Basin</p> <p> </p> <p> </p> <p> </p>
Python code for "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"
<p>The file contains the Python code used to run the agent-based simulation of the selection-mutation model presented in "Evolutionary epidemiology consequences of trait-dependent control of heterogeneous parasites"</p>
Data to reproduce analysis in "Systematic analysis of transcriptional and epigenetic effects of genetic variation in Kupffer cells enables discrimination of cell intrinsic and environment-dependent mechanisms"
<p>Here you can find the datasets necessary to reproduce all analyses described in the Glass lab paper by <a href="https://www.biorxiv.org/content/10.1101/2022.09.22.509046v1">Bennett et al</a>. The python and R code for reproducing analysis and figures can be found on our linked <a href="https://github.com/HunterBennett/KupfferCell_NaturalGeneticVariation">github repository.</a></p> <p>Briefly, this paper explores the effect of natural genetic variation <em>in vivo</em>, using Kupffer cells as a model cell type. We collect and analyze transcriptional and epigenetic data (ATAC-seq, H3K27Ac ChIP-seq) to identify putative <em>trans</em> regulators driving differential gene expression across inbred strains of mice. Additionally, we provide evidence that <em>trans</em> effects control a majority of strain differential genes at homeostasis while <em>cis</em> effects dominate the transcriptional response to an external signal (lipopolysaccharide).</p> <p>References:</p> <p>Hunter Bennett, Ty D. Troutman, Enchen Zhou, Nathanael J. Spann, Verena M. Link, Jason S. Seidman, Christian K. Nickl, Yohei Abe, Mashito Sakai, Martina P. Pasillas, Justin M. Marlman, Carlos Guzman, Mojgan Hosseini, Bernd Schnabl, Christopher K. Glass bioRxiv 2022.09.22.509046; doi: <a href="https://doi.org/10.1101/2022.09.22.509046">https://doi.org/10.1101/2022.09.22.509046</a></p> <p> </p>
Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery
<p><strong>Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery</strong></p> <p>This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the <em>claims.csv</em> file, the <em>AirportCode</em> implies <em>AirportName</em>, as each code should be unique for a given airport.</p> <p>The file <em>ground_truth.csv</em> is a comma separated file containing approximate functional dependencies. <em>table</em> describes the relation we refer to, <em>lhs</em> and <em>rhs</em> reference two columns of those relations where semantically we found that <em>lhs</em> implies <em>rhs</em>.</p> <p>The file <em>excluded_candidates.csv</em> and <em>included_candidates.csv</em> list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the <em>g3_prime</em> value was too small.</p> <p><strong>Dataset References</strong></p> <ul> <li><em>adult.csv</em>: Dua, D. and Graff, C. (2019). <a href="http://archive.ics.uci.edu/ml">UCI Machine Learning Repository</a>. Irvine, CA: University of California, School of Information and Computer Science.</li> <li><em>claims.csv</em>: TSA Claims Data 2002 to 2006, <a href="https://www.dhs.gov/tsa-claims-data">published by the U.S. Department of Homeland Security</a>.</li> <li><em>dblp10k.csv</em>: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. <a href="https://hpi.de/naumann/projects/repeatability/datasets/dblp-dataset.html">Made available as DBLP Dataset 2</a>.</li> <li><em>hospital.csv</em>: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>t_biocase_...</em> files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> <li><em>tax.csv</em>: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. <a href="https://owncloud.hpi.de/s/j6Z0yvXC0qhtGCk/download">Made available as part the dataset collection to that paper.</a></li> </ul>
Missing data in the analysis of multilevel and dependent data (Examples)
<p>Example data sets and computer code for the book chapter titled "Missing Data in the Analysis of Multilevel and Dependent Data" submitted for publication in the second edition of "Dependent Data in Social Science Research" (Stemmler et al., 2015). This repository includes the computer code (".R") and the data sets from both example analyses (Examples 1 and 2). The data sets are available in two file formats (binary ".rda" for use in R; plain-text ".dat").</p> <p>The data sets contain simulated data from 23,376 (Example 1) and 23,072 (Example 2) individuals from 2,000 groups on four variables:</p> <p><code>ID</code> = group identifier (1-2000)<br> <code>x</code> = numeric (Level 1)<br> <code>y</code> = numeric (Level 1)<br> <code>w</code> = binary (Level 2)</p> <p>In all data sets, missing values are coded as "NA".</p>
Density-dependent effects of exotic brook trout on aquatic communities in mountain lakes revealed by environmental DNA and morphological taxonomy
Invasion of non-native fishes threatens freshwater biodiversity worldwide. Yet, detailed estimates of population demography for invasive species, that estimate population size and body size of the invasive species, are rarely integrated in evaluating aquatic community responses. Our study capitalized on detailed brook trout population demographic data collected for a replicated whole lake ecosystem experiment involving experimental harvesting of exotic brook trout in nine mountain lakes. We applied environmental DNA (eDNA) metabarcoding and morphological taxonomy to examine the response of crustacean zooplankton and macroinvertebrate communities to gradients in brook trout effective density and lake elevation. Density-dependent effects of brook trout on crustacean zooplankton and macroinvertebrate communities were detected even decades after their first introductions (between 1926 and 1980). However, they were moderated by environmental factors such as elevation, lake maximum depth and dissolved organic carbon. Elevation was important in structuring crustacean zooplankton and macroinvertebrate community composition. While there were differences in explanatory variables when describing communities characterized by eDNA metabarcoding and morphological taxonomy, the principal environmental factors that structured the communities were similar. Our paper highlights persisting density-dependent impacts of exotic trout on invertebrate communities even decades after first introduction, and it considers the conservation implications for lake restoration.
Influence of cockle bioturbation on microphytobenthic primary producers: habitat and density-dependent effect
The purpose of this study was to better understand the non-trophic interactions of benthic macrofauna, especially through their bioturbation activity, on microphytobenthos (MPB), which remain poorly studied and understood. For this purpose, a mesocosm experiment was performed, using the common cockles Cerastoderma edule. This species plays a key role in coastal ecosystem, especially impacting sediment characteristics and biogeochemistry through their bioturbation, including sediment reworking and bioirrigation. For the first time, bioturbation rates, biogeochemical fluxes at the sediment-water interface and MPB biomass and photosynthetic variables were measured at the same time. The effect of cockles density and sediment type were also investigated. This mesocosm experiment took place at the marine station of Arcachon. Experimental units consisted of PVC tubes filled with 2 types of sediment (medium sand or fine sand; samples in Arachon Bay and Baie des Veys in France). Then, 4 density of cockles were added in triplicate (0, 288, 720 and 1,297 ind. m-2), for each sediment type. The whole design was repeated twice, because in one of them luminophores were added at the top to measure sediment reworking, and could interfere with fluorescence measurement of MPB variables. All experimental units were incubated 6 days into a big tank, with artificial tide and light. Dissolved tracers were added into the natural seawater (close system) to measure bioirrigation rates of cockles. After 6 days, regarding experimental units with luminophores, they were slices and porewater was extracted to quantify bioturbation rates. Regarding units without luminophores, surface biomass and photosynthetic parameters of MPB were first measured in all unit with an IMAGING PAM. Then, oxygen and nutrient fluxes at the sediment water interface were measured using incubations. And finally, the first centimeter was sliced to measure total MPB biomass. This study demonstrated that bioturbation intens
Long-term nitrogen fertilization inhibits carbon and nitrogen loss during late stage fungal necromass decomposition depending on necromass chemistry
Fungal necromass is increasingly recognized as a key component of in soil carbon (C) and nitrogen (N) cycling. However, how C and N loss from fungal necromass during decomposition are impacted by global change factors such as anthropogenic N addition and changes to soil C supply (e.g. via changing root exudation and rhizosphere priming) remains unclear and understudied relative to plant tissues. To address these gaps, we conducted a year-long decomposition experiment with four species of fungal necromass incubated across four forested sites in plots that had received inorganic N and/or labile C fertilization for decades in Minnesota, USA. We found that necromass chemistry was the primary driver of C and N loss from fungal necromass as well as response to fertilization. Specifically, N addition suppressed late-stage decomposition, but this effect was weaker in melanin-rich necromass, contrary to the hypothesis based on plant litter dynamics that N addition should suppress decomposition of more complex organic molecules. Labile C addition had no effect on either the early or late stages of necromass decomposition. Nitrogen release from necromass also varied among species, with N-poor necromass having lower N release after controlling for differences in mass loss via regression. The relatively minor effects of N fertilization on the proportion of initial necromass N released suggests that N demand by decomposers is the primary control on N loss during fungal necromass decomposition. Together, our results stress the importance of the afterlife effects of fungal chemical composition to forest soil C and N cycles. Further, they demonstrate that C and N release from this critical pool can be reduced by ongoing anthropogenic N addition.
Context dependency of effect of fungal connections between plants and biocrusts
Conceptual context: Species interactions may couple the resource dynamics of different primary producers and may enhance productivity by reducing loss from the system. In low-resource systems, this biotic control may be especially important for maintaining productivity. In drylands, the activities of vascular plants and biological soil crusts can be decoupled in space because biocrusts grow on the soil surface but plant roots are underground, and decoupled in time due to biocrusts activating with smaller precipitation events than plants. Soil fungi are hypothesized to functionally couple the plants and biocrusts by transporting nutrients. We studied whether disrupting fungi between biocrusts and plants reduces nitrogen transfer and retention and decreases primary production as predicted by the fungal loop hypothesis. Additionally, we compared varying precipitation regimes that can drive different timing and depth of biological activities. Methodological approach: We used field mesocosms in which the potential for fungal connections between biocrusts and roots remained intact or were impeded by mesh. We imposed a precipitation regime of small, frequent or large, infrequent rain events. We used 15N to track fungal-mediated nitrogen (N) transfer. We quantified microbial carbon use efficiency and plant and biocrust production and N content.
Density-dependent demography of creosote bush (Larrea tridentata) along grass-shrub ecotones.
The encroachment of woody plants into grasslands is a global phenomenon with implications for biodiversity and ecosystem function. Understanding and predicting the pace of expansion and the underlying processes that control it are key challenges in the study and management of woody encroachment. Theory from spatial population biology predicts that the occurrence and speed of population expansion should depend sensitively on the nature of conspecific density dependence. If fitness is maximized at the low-density encroachment edge then shrub expansion should be "pulled" forward. However, encroaching shrubs have been shown to exhibit positive feedbacks, whereby shrub establishment modifies the environment in ways that facilitate further shrub recruitment and survival. In this case there may be a fitness cost to shrubs at low density causing expansion to be "pushed" from behind the leading edge. We studied the spatial dynamics of creosotebush (Larrea tridentata), which has a history of encroachment into Chihuahuan Desert grasslands over the past century. We used demographic data from observational censuses and seedling transplant experiments to test the strength and direction of density dependence in shrub fitness along a gradient of shrub density at the grass-shrub ecotone. We also used seed-drop experiments and wind data to construct a mechanistic seed dispersal kernel, then connected demography and dispersal data within a spatial integral projection model (SIPM) to predict the dynamics of shrub expansion. The SIPM predicted that, contrary to expectations based on potential for positive feedbacks, the shrub encroachment wave is "pulled" by maximum fitness at the low-density front. However, the predicted pace of expansion was strikingly slow (ca. 8 cm/yr), and this prediction was supported by independent re-surveys of the ecotone showing little to no change in spatial extent of shrub cover over 12 years. Encroachment speed was acutely sensitive to seedling recruitment,
SIRAH-CoV2 initiative: RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 (PDB id:7BTF)
<p>This dataset contains the trajectory of a 10 microseconds-long coarse-grained molecular dynamics simulation of SARS-CoV2 RNA-dependent RNA polymerase in complex with cofactors Nsp7 and Nsp8 and Zinc (PDB id: 7BTF). Simulations have been performed using the SIRAH force field running with the Amber18 package at the Uruguayan National Center for Supercomputing (ClusterUY) under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Zinc ions were parameterized as reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jcim.0c00160">Klein et al. 2020</a>.</p> <p>The files 7BTF_SIRAHcg_rawdata_0-2us.tar, 7BTF_SIRAHcg_rawdata_2-6us.tar, and 7BTF_SIRAHcg_rawdata_6-10us.tar, contain all the raw information required to visualize (on VMD), analyze, backmap, and eventually continue the simulations using Amber18 or higher. Step-By-Step tutorials for running, visualizing, and analyzing CG trajectories using <a href="https://academic.oup.com/bioinformatics/article/32/10/1568/1743152">SirahTools</a> can be found at www.sirahff.com.</p> <p>Additionally, the file 7BTF_SIRAHcg_10us_prot.tar contains only the protein coordinates, while 7BTF_SIRAHcg_10us_prot_skip10ns.tar contains one frame every 10ns.</p> <p>To take a quick look at the trajectory:</p> <p>1- Untar the file 7BTF_SIRAHcg_10us_prot_skip10ns.tar</p> <p>2- Open the trajectory on VMD 1.9.3 using the command line:</p> <p>vmd 7BTF_SIRAHcg_prot.prmtop 7BTF_SIRAHcg_prot.ncrst 7BTF_SIRAHcg_prot_10us_skip10ns.nc -e sirah_vmdtk.tcl</p> <p>Note that you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. </p> <p>This dataset is part of the SIRAH-CoV2 initiative.</p> <p>For further details, please contact Martin Soñora (msonora@pasteur.edu.uy) Sergio Pantano (spantano@pasteur.edu.uy).</p>
The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering - Supporting Data
<p>Supporting code and data for the paper: </p> <p><em>The Effects of Asymmetric Dark Matter on Stellar Evolution I: Spin-Dependent Scattering</em></p> <p>Raen (2020)</p> <p><strong>Supporting code</strong> includes `run_star_extras.f`, inlist templates, and our dark matter module (to be used in conjunction with MESA: <a href="http://mesa.sourceforge.net/index.html">Modules for Experiments in Stellar Astrophysics</a>). The full source code used in the production of this paper is available at <a href="https://github.com/troyraen/DM-in-Stars/">github.com/troyraen/DM-in-Stars</a> in the Raen2020 branch. (The master branch is intended for use by those wishing to use our module to explore DM effects beyond the scope of this paper.) We used MESA version 12115, and MESA SDK version 20190830.</p> <p><strong>Model data</strong> includes MESA history and profile data for the models highlighted in the paper (<span class="math-tex">\(1.0\ \mathrm{M}_\odot\)</span> and <span class="math-tex">\(3.5\ \mathrm{M}_\odot\)</span> models with <span class="math-tex">\(\Gamma_B = 0\)</span> (no dark matter), <span class="math-tex">\(\Gamma_B = 10^4\)</span>, and <span class="math-tex">\(\Gamma_B = 10^6\)</span>). The specific inlists used to generate the models are also included. Additional data will be shared on reasonable request to the paper's corresponding author.</p>
Water availability and temperature scenarios for water-dependent power plants in the Danube river basin and the Iberian Peninsula
<p>The dataset is composed by 12 files reporting the water availability and temperature scenarios for 167 water-dependent power plants in the Danube river basin and the Iberian Peninsula.</p> <p>The dataset is split into multiple files by region (Danube river basin (Danube) or Iberian Peninsula (IP)), variable (discharge or river temperature) and scenario (baseline, RCP26 or RCP85) considered.</p> <p>The title of each file is composed by the variable reported (discharge or river temperature) and the scenario considered (baseline: 1951-2004, RCP26: 2006-2100, RCP85: 2006-2100). The first row is used to report the fields considered: the first three columns report the day, the month and the year. The remaining columns report the name of the power plant considered in each region (57 for the Daube river basin and 110 for the Iberian Peninsula). In each row day, month, year and streamflow or river temperature values are reported for every water-dependent power plant examined in the study.</p> <p>Temperature is reported as daily average temperature in degrees Celsius (°C) while water availability is reported as daily average streamflow in cubic meters per second (m^3/s).</p> <p>For a description on how these files were obtained, please refer to <a href="https://doi.org/10.2777/135510">https://doi.org/10.2777/135510</a>.</p>
AGO2 localises to cytokinetic protrusions in a p38 dependent manner and is needed for accurate cell division.
<p>Argonaute 2 (AGO2) is an indispensable component of the RNA-induced silencing complex, operating at the transcriptional or posttranscriptional level. It is compartmentalized into structures such as GW- and P-bodies, stress granules and adherens junctions as well as the midbody. Here we show using immunofluorescence, image- and bioinformatic analysis and cytogenetics that AGO2 also resides in membrane protrusions such as open- and close-ended tubes. The latter are cytokinetic bridges where AGO2 colocalizes at the midbody-arms with cytoskeletal components such as α-Τubulin and Aurora B and various kinases. AGO2, phosphorylated on serine 387 is located together with Dicer at the midbody ring in a manner dependent on p38 MAPK activity. We further show that AGO2 is stress sensitive and important to ensure the proper chromosome segregation and cytokinetic fidelity. We suggest that AGO2 is part of a regulatory mechanism triggered by cytokinetic stress to generate the appropriate micro-environment for local transcript homeostasis.</p> <p><br> Statement: AGO2 resides in open-ended tunneling nanotubes and close-ended cytokinetic bridges. At the latter location AGO2 colocalises with cell division components and the authors show that AGO2 deregulation impairs cell division fidelity.</p>
Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects
<p>The dataset contains analyzed projects and modules for the paper "Can We Trust Tests To Automate Dependency Updates? A Case Study of Java Projects". The contents are the following:</p> <ul> <li><a href="/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/manual-studied-modules.csv?versionId=8258b59c-3f88-487b-a4a3-007cb362a44a">manual-studied-modules.csv</a>: Manually analyzed Maven modules mentioned in Section 5.2</li> <li><a href="https://zenodo.org/api/files/f0b463e1-7c71-4f10-8aa4-aa4ed963bd9e/projects.zip">projects.zip</a>: Instrumented and Mutated Github Projects. Projects list applied mutation changes, and their dynamic and static call graph.</li> </ul>
Präzi: From Package-based to Call-based Dependency Networks
<p>The data is originally derived from commit 6c550c8 of <a href="https://web.archive.org/web/20210129124622/https://github.com/rust-lang/crates.io-index">https://github.com/rust-lang/crates.io-index</a>. The dataset includes the following files:</p> <ul> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/releases.csv?versionId=0fbac7f6-820c-4411-80ee-26967eda0652">releases.csv</a>: extracted package releases.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/docsrs.csv?versionId=b61f6277-0f19-4c21-bb54-04dcb19f8d43">docsrs.csv</a>: build status and compile toolchain of package releases scrapped from <a href="https://web.archive.org/web/20210118074327/https://docs.rs/">Docs.rs</a>.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/rustcg-corpus.tar.xz?versionId=914e7e03-0509-443f-8841-a76408f329d3">rustcg-corpus.tar.xz</a>: call graphs and type hierarchies corpus of <a href="https://web.archive.org/web/20210125175834if_/https://crates.io/">crates.io</a> in JSON format. Constructed using <a href="https://web.archive.org/web/20210129130825/https://github.com/ktrianta/rust-callgraphs">rust-callgraphs</a>.</li> <li><a href="/api/files/0386399e-bc22-4e16-a2e3-2d331696e1de/CDN.tar.xz?versionId=9e0d7baa-32c8-4594-b8b9-fb486eee7d11">CDN.tar.xz</a>: static call-based dependency network (CDN) and package-based dependency network (PDN) in JSON format. </li> </ul>
Background data 'Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis'
<p>Background data of the paper 'Effect of biotic dependencies in species distribution models: The future distribution of Thymallus thymallus under consideration of Allogamus auricollis'</p>
Goblin: Neo4J Maven Central dependency graph
<p>This repository contains a Neo4j dump of Maven Central dependency graph generated using <a href="https://github.com/Goblin-Ecosystem/goblinDependencyMiner">goblinDependencyMiner</a>.<br>To import this graph into neo4j, <strong>please use a version 4.x</strong>.</p> <p>Our dependency graph structure and metamodel are shown in images "goblin_dg_structure" and "metamodel".</p> <p>The latest available version dates from April 20, 2025, contains <span>16,939,391</span> nodes (712,509 libraries and 16,226,882 releases) and <span>152,434,085</span> edges (136,207,203 dependencies and 16,226,882 versioning edges).</p> <p>This repository contains two dump of the database:</p> <ul> <li><strong>goblin_maven_20_04_25.dump: </strong>This dataset contains the entire Maven Central dependency graph.</li> <li><strong>with_metrics_goblin_maven_20_04_25.dump</strong>: This dataset is the same as the previous one, but enriched with new “AddedValue” nodes (49,393,155 new nodes) representing the following metrics: CVE (dated may 13, 2025), freshness, popularity and speed. More information in this <a href="https://github.com/Goblin-Ecosystem/goblinTutorial">tutorial</a>.</li> </ul> <p>More details in the dedicated paper: <strong>Goblin: A Framework For Enriching And Querying the Maven Central Dependency Graph </strong>(https://doi.org/10.1145/3643991.3644879)<strong> </strong>- 21st International Conference on Mining Software Repositories (MSR'24).<br>If you use it, please <strong>cite</strong> this paper: <a href="https://dl.acm.org/doi/10.1145/3643991.3644879">https://dl.acm.org/doi/10.1145/3643991.3644879</a></p> <p>⚠️ This dataset is the subject of the <strong>Mining Challenge at the MSR 2025 conference</strong>, more information <a href="https://2025.msrconf.org/track/msr-2025-mining-challenge">here</a>.</p>
Gene family expansions underpin context-dependency of the oldest mycorrhizal symbiosis
<p>This Zenodo archive is associated with the manuscript:</p> <p>Hernandez, D.J., Pohlmann, G.B., Afkhami, M.E. (2025) <span>Gene family expansions provide molecular flexibility required for context-dependent species interactions.</span> Ecology Letters.</p> <p>Abstract:</p> <p>As environments worldwide change at unprecedented rates during the Anthropocene, understanding context-dependency – how species regulate interactions to match changing environments – is crucial. However, generalizable molecular mechanisms underpinning context-dependency remain elusive. Combining comparative genomics across 42 angiosperms with transcriptomics, genome-wide association mapping, and gene duplication origin analyses, we show for the first time that gene family expansions undergird context-dependent regulation of species interactions. Gene families expanded in mycorrhizal fungi-associating plants display up to 200% more context-dependent gene expression and double the genetic variation associated with mycorrhizal benefits to plant fitness. Moreover, we discover these gene family expansions arise primarily from tandem duplications with >2-times more tandem duplications genome-wide, indicating gene family expansions continuously supply genetic variation throughout plant evolution allowing fine-tuning of context-dependency in species interactions.</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.