Find research datasets worth reusing

Phlorest phylogeny derived from Grollemund et al. 2015 'Bantu expansion shows habitat alters the route and pace of human dispersals'

Cite the source of the dataset as: <blockquote> Grollemund R, Branford S, Bostoen K, Meade A, Venditti C & Pagel M. 2015. Bantu expansion shows habitat alters the route and pace of human dispersals. Proceedings of the National Academy of Sciences of the USA, 112(43), 13296-13301. </blockquote>

Phlorest phylogeny derived from Birchall et al. 2016 'A combined comparative and phylogenetic analysis of the Chapacuran language family'

Cite the source of the dataset as: <blockquote> Birchall, Joshua, Michael Dunn, and Simon J. Greenhill. 2016. A combined comparative and phylogenetic analysis of the Chapacuran language family. International Journal of American Linguistics 82 (3): 255–84. doi: 10.1086/687383 </blockquote>

Phlorest phylogeny derived from Hruschka et al. 2015 'Detecting regular sound changes in linguistics as events of concerted evolution'

Cite the source of the dataset as: <blockquote> Hruschka, D. J., Branford, S., Smith, E. D., Wilkins, J., Meade, A., Pagel, M., & Bhattacharya, T. (2015). Detecting regular sound changes in linguistics as events of concerted evolution. Current Biology, 25(1), 1-9. </blockquote>

Phlorest phylogeny derived from Bouckaert et al. 2018 'The origin and expansion of Pama–Nyungan languages across Australia'

Cite the source of the dataset as: <blockquote> Bouckaert RR, Bowern C & Atkinson QD. 2018. The origin and expansion of Pama–Nyungan languages across Australia. Nature Ecology and Evolution. 2: 741–749 </blockquote>

Phlorest phylogeny derived from Honkola et al. 2013 'Cultural and climatic changes shape the evolutionary history of the Uralic languages'

Cite the source of the dataset as: <blockquote> Honkola T, Vesakoski O, Korhonen K, Lehtinen J, Syrjänen K & Wahlberg N. 2013. Cultural and climatic changes shape the evolutionary history of the Uralic languages. Journal of Evolutionary Biology, 26(6):1244–1253. </blockquote>

Phlorest phylogeny derived from Dunn et al. 2011 'Evolved structure of language shows lineage-specific trends in word-order universals'

Cite the source of the dataset as: <blockquote> Dunn M, Greenhill SJ, Levinson SC & Gray RD. 2011. Evolved structure of language shows lineage-specific trends in word-order universals. Nature, 473(7345), 79-82. </blockquote>

Phlorest phylogeny derived from Kitchen et al. 2009 'Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East'

Cite the source of the dataset as: <blockquote> Kitchen A, Ehret C, Assefa S & Mulligan CJ. 2009. Bayesian phylogenetic analysis of Semitic languages identifies an Early Bronze Age origin of Semitic in the Near East. Proceedings of the Royal Society B: Biological Sciences, 270(1668), 2703-2710. </blockquote>

Phlorest phylogeny derived from De Filippo et al. 2012 'Bringing together linguistic and genetic evidence to test the Bantu expansion'

Cite the source of the dataset as: <blockquote> De Filippo, C., Bostoen, K., Stoneking, M., & Pakendorf, B. (2012). Bringing together linguistic and genetic evidence to test the Bantu expansion. Proceedings of the Royal Society B: Biological Sciences, 279(1741), 3256–3263. doi:10.1098/rspb.2012.0318 </blockquote>

opencc-by-4.0Oct 2023View details →

Data: "Using butterfly survey data to model habitat associations in urban developments", JEJ Cooper et al., (2023)

This data package has been used to examine the responses of UK butterfly species   to different features of the urban environment. 'JC_WCBSmodel.Rdata' presents the butterfly abundance data, and supporting information about   species and sites. This data can be fed through the script '04_model_builder.R', to   produce the models reported in the research article. '00_functions.R' is a script   containing functions which support the modelling process, which is loaded as part of   the 04_model_builder script.     Summaries of the resulting models are an output of that script -   'Butterfly_GAM_Outputs.xlsx'. These are represented graphically in the manuscript,   using scripts '06_01_Map'.R:'06_03_Cross_Validation'. '06_04_Model_Metric.R'   is a further summary of the .xlsx file, found in the Supplementary Materials.   '06_05_graphic_4_twitter.R' produces a condensed version of the figure resulting   from the script '06_02_Metric_Summary.R'   Dataset descriptions are found in the attached readme.txt ........................................................................................ We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!

Empirical data, qualitative codes, analysis: Schuur J.S. et al. Identifying levers of urban neighbourhood transformation. npj Urban Sustainability (2023)

Please refer to the stand-alone "2023_SchuurJS_UrbanSustainabilityfinal.html" file where the analysis and results corresponding to the article titled: "Identifying levers of urban neighbourhood transformation using serious games" is presented. The underlying data sets and Rmarkdown script used for the analysis can be used to re-run the analysis. Ensure to read the "0_README.txt" file to build the appropriate folder structure to do so.

PISM model output data from Garbe et al. (Nature, 2020) publication

This dataset contains the <a href="https://www.pism.io">PISM</a> model output data of the Antarctic Ice Sheet hysteresis simulations published and discussed in<a href="https://doi.org/10.1038/s41586-020-2727-5">Garbe, J., Albrecht, T., Levermann, A., Donges, J. F., and Winkelmann, R. The hysteresis of the Antarctic Ice Sheet. Nature 585(7826), 2020.</a>A detailed description of the individual file contents is given in `README.txt` below. The corresponding PISM model code used for these simulations is archived <a href="https://doi.org/10.5281/zenodo.3956431">here</a>.In case of questions, feel free to contact me at <a href="mailto:julius.garbe@pik-potsdam.de">julius.garbe@pik-potsdam.de</a>.

opencc-by-4.0Nov 2023View details →

S1Data: ChIP-seq Data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"

ChIP data from Ferrie et. al. "p300 Is an Obligate Integrator of Combinatorial Transcription Factors Inputs"

Selected properties of galaxy, MBHs and MBHBs populations (Izquierdo-Villalba et al. 2022)

<pre>This is a catalogue of galaxies, massive black holes (MBHs) and massive black hole binaries (MBHBs)  generated with L-Galaxies semi-analytical model in the version of Izquierdo-Villalba et al. 2022 and the dark matter merger trees extracted from the Millennium simulation (Springel et al. 2005). The catalogue is created by making use of only 9 sub-volumes of the Millennium box (~2% of the whole simulation, Volume = 5562144.30 Mpc3) and it contains galaxies, MBHs and MBHBs at 51 different redshifts (0 < z < 12.5). This catalogue is suited for studying the population of MBHBs and their hosts. The properties stored in this catalogue are the following: Redshift: Redshift of the galaxy/MBH/MBHB SnapNum: Snapnum of the simulation Pos: Position of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [Mpc/h] Vel: Velocity of the galaxy/MBH/MBHB inside the comoving box. It is an array of dimension 3. [km/s] Mvir: Virial mass of the dark matter sub-halo [1e10 Msun/h] Rvir: Virial radius of the dark matter sub-halo [Mpc/h] Vvir: Virial velocity of the dark matter sub-halo [km/s] Vmax: Maximum circular velocity of the dark matter halo [km/s] HotRadius: Radius of the hot gas atmosphere that surrounds the galaxy [1e10 Msun/h] ColdGas: Cold gas component of the galaxy [1e10 Msun/h] BulgeMass: Stellar mass of the bulge component [1e10 Msun/h] DiskMass: Stellar mass of the disc component [1e10 Msun/h]. The total stellar mass of the galaxy should be BulgeMass+DiskMass HotGas: Hot gas component of the galaxy [1e10 Msun/h] BlackHoleMass: Mass of the primary MBH of the galaxy [1e10 Msun/h] Lbol: Bolometric luminosity of the primary MBH of the galaxy [1e40 erg/s] fEDD: Ratio between the Lbol of the primary and the Eddington luminosity (<=1) [No dimensions] spin: Spin of the primary MBH [0,1] M_dot_acc: Accretion rate of the primary MBH of the galaxy [Msun/yr] BlackHoleMassSec: Mass of the secondary MBH (if exists) of the galaxy [1e10 Msun/h] LbolSec: Bolometric luminosity of the secondary MBH (if exists) of the galaxy [1e40 erg/s] fEDDSec: Ratio between the Lbol of the secondary MBH (if exists) and the Eddington luminosity (<=1) [No dimensions] spinSec: Spin of the primary MBH (if exists) [0,1] M_dot_acc_sec: Accretion rate of the secondary MBH (if exists) of the galaxy [Msun/yr] BinarySemiMajorAxis: Semi-major axis of the MBHB [Mpc/h] BinaryEccentricity: Eccentricity of the MBHB Sfr: Star formation rate of the galaxy [Msun/yr] BulgeSize: Size of the bulge stellar component [Mpc/h] StellarDiskRadius: Scale length of the disc stellar component [Mpc/h] GasDiskRadius: Scale length of the disc gas component [Mpc/h] The file can be read as follows: import h5py hf = h5py.File('LGal_IzquierdoVillalba2022_SubVol_0_9.h5', 'r')</pre>

Data for "How do ecologists estimate occupancy in practice?" by Goldstein et al.

Data for review of occupancy estimtaion methods by Goldstein et al.   Please see the accompanying manuscript for full methodology. We will link to it when the manuscript is published.   This upload contains three datasets: "binary_scores_phase1.csv", "binary_scores_phase2.csv" and "modsel_results.csv". Each is a .csv file containing data from a survey of occupancy estimation practices. Each row represents one peer-reviewed paper, while each column represents a characteristic of the paper. Most columns are TRUE/FALSE, indicating whether or not the paper satisfied the relevant criterion.   One additional raw resource is provided. The .zip file "all_papers_2022-04-07.zip" contains 6 .xls files giving the full set of papers returned by the original Web of Science search. These are unmodified from the initial search.   All datasets contain the column: ID - A unique ID for each paper; it most cases, a DOI. When Web of Science returned an invalid DOI, the ID is set to the paper title instead.   Note that all papers in Phase 2 are also in Phase 1, and all model selection papers are in both Phase 1 and Phase 2. The ID column can be used to join datasets.   Across both datasets, if all options in a category are FALSE or if a field is NA, that may mean that the review team was unable to determine what choices the authors made.   binary_scores_phase1.csv gives the results of Phase 2 of the review. It contains the following columns:   coll_newdata - Did the authors analyze newly collected data? coll_existing - Did the authors analyze existing, previously published data? coll_longterm - Did the authors analyze data produced by a long-term monitoring program? coll_particip - Did the authors analyze participatory science data? eco_frshwtr - Was the study system a freshwater ecosystem? eco_marine - Was the study system a marine ecosystem? eco_terra -  Was the study system a terrestrial ecosystem? region_USA -  Were the data collected in the USA? region_NoAm -  Were the data collected in North America? region_CenAm -  Were the data collected in Central America? region_SoAm -  Were the data collected in South America? region_Africa -  Were the data collected in Africa? region_Eur -  Were the data collected in Europe? region_Asia -  Were the data collected in Asia? region_Oceania -  Were the data collected in Oceania? framework_MLE -  Did the authors estimate models in a maximum likelihood framework? framework_ML -  Did the authors estimate models in a machine learning framework? framework_Bayes -  Did the authors estimate models in a Bayesian framework? gof_AUC -  Did the authors use area-under-the-curve to evaluate their models? gof_CV -  Did the authors use cross validation to evaluate their models? gof_PPC -  Did the authors use poserior predictive checks to evaluate their models? gof_parboot -  Did the authors use parametric bootstrapping to evaluate their models? gof_bayespv -  Did the authors use Bayesian p-values to evaluate their models? gof_any -  Did the authors conduct any model checking? taxon_mammal - Were some or all of the study species mammals? taxon_bird -  Were some or all of the study species birds? taxon_herp - Were some or all of the study species herptiles (reptiles and amphibians)? taxon_fish -  Were some or all of the study species fish? taxon_arthro -  Were some or all of the study species arthropods? taxon_othinv -  Were some or all of the study species non-arthropod invertebrates? soft_unmarked -  Did the authors estimate models using the software unmarked? soft_PRESENCE -  Did the authors estimate models using PRESENCE-family software? soft_MARK -  Did the authors estimate models using MARK-family software? soft_JAGS -  Did the authors estimate models using JAGS-family software? soft_lme4 -  Did the authors estimate models using the software lme4? soft_MaxEnt -  Did the authors estimate models using the software MaxEnt? soft_baseR -  Did the authors estimate models using custom models written in base-R? soft_NR -  Did the authors fail to clearly report what software they used to estimate models? soft_other -  Did the authors estimate models using some other software? nspecies -  How many species did the authors study? nspec_1 -  Did the authors analyze data on exactly 1 species? nspec_2 -  Did the authors analyze data on exactly 2 species? nspec_3_5 -  Did the authors analyze data on 3-5 species? nspec_6_10 -  Did the authors analyze data on 6-10 species? nspec_11_20 -   Did the authors analyze data on 11-20 species? nspec_20plus -   Did the authors analyze data on more than 20 species? compare_avg -   Did the authors conduct model averaging? compare_sel -   Did the authors conduct model selection? compare_other - Did the authors compare multiple models without averaging or selecting between them? modsel_AICc - Did the authors use AICc in model selection or averaging? modsel_AIC -  Did the authors use AIC in model selection or averaging? modsel_other -  Did the authors use another information criterion in model selection or averaging? dattype_DND - Were some or all of the data collected as detection-nondetection data? dattype_count -  Were some or all of the data collected as count data? dattype_PO -  Were some or all of the data collected as presence-only data? dattype_other -  Were some or all of the data collected in some other form? survey_visual -  Were some or all of the data collected using in-person visual surveys? survey_audio -  Were some or all of the data collected using in-person audio surveys? survey_camtrap -  Were some or all of the data collected using camera trap surveys? survey_capture -  Were some or all of the data collected using animal capture surveys? survey_sign -  Were some or all of the data collected using sign surveys? survey_passaudio -  Were some or all of the data collected using passive acoustic surveys? survey_DNA -  Were some or all of the data collected using eDNA surveys? survey_other -  Were some or all of the data collected using some other survey protocol? modtype_SSOM -  Did the authors analyze data with an SSOM? modtype_DynOcc -  Did the authors analyze data with a dynamic occupancy model? modtype_GLM -  Did the authors analyze data with a GLM? modtype_cooccur -  Did the authors analyze data with a multispecies co-occurrence model? modtype_community -  Did the authors analyze data with a multispecies community model? modtype_MaxEnt -   Did the authors analyze data with a MaxEnt model? modtype_other -  Did the authors analyze data with another model? affil_acad -  Did any of the authors have an academic affiliation? affil_govt -  Did any of the authors have a government affiliation? affil_other - Did any of the authors have a private or NGO affiliation?     binary_scores_phase2.csv gives the results of Phase 2 of the review. It contains the following columns:   code_avail - Did we determine that the authors published their model fitting code? data_avail - Did we determine that the authors published their data? nsite - At how many sites were data collected? nsite_lt20 - Were data collected at 20 or fewer sites? nsite_21_50 - Were data collected at 20-50 sites? nsite_51_100 - Were data collected at 51-100 sites? nsite_101p - Were data collected at more than 100 sites? time_pds - Over how many primary time periods (e.g. sampling seasons) were data collected? time_pds_1 - Were data collected during a single time period? time_pds_2_3 - Were data collected during 2-3 time periods? time_pds_4p - Were data collected during 4 or more time periods? nrepl - Roughly how many replicate surveys were collected per site? nrepl_1 - Was only one replicate survey conducted per site? nrepl_2_3 - Were 2-3 replicate surveys conducted per site? nrepl_4_5 - Were 4-5 replicate surveys conducted per site? nrepl_6p - Were 6 or more replicate surveys conducted per site? window - What sampling window was used to discretize continuous-time sampling? (continuous-time studies only; otherwise NA) det_window_lt_day - Was a sampling unit of less than one day used to discretize sampling? det_window_1day - Was a sampling unit of one day used to discretize sampling? det_window_2_6day - Was a sampling unit of 2-6 days used to discretize sampling? det_window_7_14day - Was a sampling unit of l7-14 days used to discretize sampling? det_window_15p_day - Was a sampling unit of 15 days or more used to discretize sampling? homerange_is_bigger - Did the authors describe their survey area per site as bigger than the target species' home range? homerange_is_smaller -Did the authors describe their survey area per site as smaller than the target species' home range? informative_priors - Did the authors use informative priors in a Bayesian analysis? time_in_mod_as_covar - Did the authors include primary time periods in the model as a covariate? time_in_mod_separate_model - Did the authors use separate models to analyze data collected during different primary time periods? time_in_mod_other - Did the authors include primary time periods in the model in some other way? ncovar_det - How many covariates were included in the best model's detection submodel? ncovar_det_zero - Did the detection submodel include 0 covariates? ncovar_det_1_3 -  Did the detection submodel include 1-3 covariates? ncovar_det_4p -  Did the detection submodel include 4 or more covariates? ncovar_occ - How many covariates were included in the best model's occupancy submodel? ncovar_occ_zero -  Did the occupancy submodel include 0 covariates? ncovar_occ_1_3 -  Did the occupancy submodel include 1-3 covariates? ncovar_occ_4p -  Did the occupancy submodel include 4 or more covariates? covars_both_mods - Were any variables considered in both submodels? model_ranefs - Did the authors include any random effects? model_expl_spatial - Did the model have an explicit spatial component? motivating_q_range - Was range estimation a main goal motivating the study? motivating_q_drivers - Was identifying drivers of occupancy a main goal motivating the study? motivating_q_trends - Was identifying trends in occupancy a main goal motivating the study? motivating_q_predict - Was predicting occupancy under new conditions a main goal motivating the study? motivating_q_theoretical - Was advancing ecological theory a main goal motivating the study? motivating_q_field_method - Was evaluation of a field method a main goal motivating the study? motivating_q_model_method - Was evaluation of a modeling method a main goal motivating the study? context_conservation - Did the authors contextualize their study as relevant to conservation? context_management - Did the authors contextualize their study as relevant to wildlife management? context_natural_hist - Did the authors contextualize their study as relevant to studying natural history of target species? context_methodology - Did the authors contextualize their study as advancing methodology? detdensity - Did the authors mention the possibility that detection and animal density were confounded? interp_top_mod_as_biol - Did the authors interpret model selection results as evidence for a biological process? nmod_reported_best - Did the authors report only the best model from a model selection workflow? nmod_reported_some - Did the authors report multiple model results from a model selection workflow? nmod_reported_all - Did the authors report all model results from a model selection workflow? priors_reported - Did the authors report their priors in a Bayesian workflow? violation_nonindependence - Do the authors acknowledge violating the assumption of independent data? violation_movement - Do the authors acknowledge violating the assumption of no animal movement? violation_demography - Do the authors acknowledge violating the assumption of no demographic change? violation_det_heterogeneity - Do the authors acknowledge violating the assumption of no unmodeled heterogeneity in detection? violation_yes_other - Do the authors acknowledge violating another assumption? violation_explicit_no - Do the authors state that all assumptions were met? hypotheses_all - Do the authors provide hypotheses for the effect of all covariates? hypotheses_some - Do the authors provide hypotheses for the effect of some covariates? interpret_det_literal - Do the authors interpret detection literally? interpret_det_biol - Do the authors interpret detection as confounded with biology? interpret_vars_significant - Do the authors interpret some variables as significant based on p-values? interpret_vars_credible - Do the authors interpret some variables as meaningful based on Bayesian credible intervals?     modsel_results.csv describes the model selection choices made by 64 Phase 2 papers that conducted model selection. In addition to ID, it contains two columns:   Submodel approach - Did the authors use a separate-by-submodel approach to model selection, did they only conduct model selection on one submodel, or did they perform variable selection on both submodels simultaneously? Candidate set approach - Did the authors conduct model selection among a set of a priori candidate models, or did they select between arbitrary models based on combinations of all covariates?

CLDF dataset derived from Carroll et al. "Yamfinder: The Southern New Guinea Lexical Database"

Cite the source of the dataset as: <blockquote> Carroll, Matthew J., Barth, Wolfgang, Nicholas Evans, I Wayan Arka, Christian Döhler, Eri Kashima, Volker Gast, Tina Gregor, Kate L. Lindsey, Julia Miller, Emil Mittag, Bruno Olsson, Dineke Schokkin, Jeff Siegel, Charlotte van Tongeren, Kyla Quinn. [DATE ACCESSED]. Yamfinder: Southern New Guinea Lexical Database. Available online at: http://www.yamfinder.com </blockquote>

11 April 2019 MRMS Data Subset for Harkema et al. (2024)

Subset of Multi-Radar Multi-Sensor (MRMS) data: 3D Reflectivity, Composite Reflectivity, and Isothermal Reflectivity. More specifically, the subset data is between 00 and 16 UTC 11 April 2019. Note: Some time(s) and file(s) maybe be missing between the subset period.

Supplementary data to Frasson et al. 2024

Supplementary data and code for Frasson et al. 2024  accepted for publication in Solid Earth. The data consist of full snapshots of the geoids, topography, CMB heat flux, and composition at the CMB for the six cases of the study; the PCA outputs for the six cases; and movies showing the time evolutions of the outputs in the mantle convection models. The scripts can be ran on the snapshots to compute the principal component analysis (PCA) and the true polar wander (TPW).   The full snapshots are given in HDF5 format, with one file per time step. They can be found in the archives MF.zip and MC.zip for the MF model and the MC model respectively. The files contain: <ul> <li>The fields in the physical space. The first coordinate is the latitude, the second coordinate is the longitude (dataset "data").</li> <li>The latitudes and longitudes associated with the fields (datasets "latitude" and "longitude").</li> <li>Some information regarding the snapshots (attributes "Time (Myr)", "Model", "Case", "Correction", and "Unit").</li> </ul> The files are called "name_xxxx.h5", where xxxx stands for the time in Myr before the end of the simulation.The names of the files are: <ul> <li>qcmb: CMB heat flux</li> <li>geoid: Total geoid</li> <li>geoid_nolvv: No LVVs geoid</li> <li>topo: Topography</li> <li>prim: Composition at the CMB</li> </ul>   The PCA outputs are given in HDF5 format. Each file has an "outputs" and a "grid" group. The "outputs" section gives: <ul> <li>The average heat flux pattern. The first coordinate is the latitude, the second coordinate is the longitude (dataset "average").</li> <li>The list of patterns for each PCA component in physical space. The first coordinate is the latitude, the second coordinate is the longitude (dataset "pattern").</li> <li>The time-dependent weight of the components. The first coordinate is the time, the second coordinate is the component number (dataset "weights").</li> <li>The singular values of the components  (dataset "singular values").</li> <li>The time in Myr (dataset "Time").</li> <li>The component numbers (dataset "components").</li> </ul> The "grid" group gives the latitudes (dataset "latitude") and the longitudes (dataset "longitude") associated with the patterns.   Description of the movies: <ul> <li>outputs_MF: As in Fig. 2 in Frasson et al. 2024.</li> <li>outputs_MC: As in Fig. 3 in Frasson et al. 2024.</li> <li>qcmb_MF: CMB heat flux in cases MF0, MF1, MF2, and MF* in a Mollweide projection. Black lines delineate the edges of basal chemical piles.</li> <li>qcmb_MC: CMB heat flux in cases MC0 and MC1 in a Mollweide projection. Black lines delineate the edges of basal chemical piles.</li> </ul>   Description of the scripts <ul> <li>PCA.py: Computes the PCA of the field stored in the path provided as argument for the given model.</li> <li>inertia.py: Computes the successive positions of the inertia axis. This script includes functions to rotate the outputs according to the positions of the inertia axis.</li> </ul>

Supplemental Files for Schlegel et al., Nature (2024)

This repository contains supplemental files for the paper "Whole-brain annotation and multi-connectome cell typing of Drosophila" - Schlegel et al., Nature (2024). <ul> <li>nblast_flywire_all_right_aba_comp.feather contains all-by-all NBLAST score for all FlyWire neurons where neurons from the left hemisphere have been mirrored to the right</li> <li>nblast_flywire_hemibrain_min_comp.feather  contains NBLAST scores for FlyWire versus "hemibrain" neurons</li> <li>nblast_flywirre_mirrored_hemibrain_comp.feather contains NBLAST scores for FlyWire versus "hemibrain" neurons where all FlyWire neurons have been mirrored</li> <li>sk_lod_783_healed_ds2.parquet contains skeletons in SWC format for all FlyWire neurons (generated from lod 1 meshes and 2X downsampled, coordinates are in nanometres); the raw data can be read with e.g. the Python <a href="https://pypi.org/project/pyarrow/">pyarrow</a> package (see <a href="https://arrow.apache.org/docs/python/parquet.html">documentation</a> for examples). Alternatively, you can use the <a href="https://github.com/navis-org/navis">navis</a> Python package to read the contents into neuron objects (see <a href="https://navis-org.github.io/navis/reference/navis/#navis.read_parquet">navis.read_parquet).</a></li> </ul> Additional notes: <ul> <li>all root IDs refer to the 783 release of FlyWire</li> <li>for NBLAST files: <ul> <li>columns/indices for FlyWire neurons are given as "{root_id},{supervoxel_id}", where the supervoxel ID represents an anchor that can be used to map this neuron to different materialization versions</li> <li>scores were compressed by rounding to the 4th decimal and clipping values below 0</li> </ul> </li> </ul> For neuron annotations and further details please see <a href="https://github.com/flyconnectome/flywire_annotations">https://github.com/flyconnectome/flywire_annotations</a>.  The proofreading and FlyWire resource are described in our companion paper (Dorkenwald et al., Nature, 2024).

Data for 'Rapport de données sur les enquêtes menées entre 2009 et 2020'

Data (all_data.csv) and R analyses code for the findings in the ‘Rapport de données sur les enquêtes menées entre 2009 et 2020’. Updated version also includes HTML file descriptive analysis code embedded and the corresponding output.