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Fig. 2 in Updating the distributions of four Uruguayan hylids (Anura: Hylidae): recent expansions or lack of sampling effort?
Fig. 2. Occurrence of Dendropsophus minutus (n = 15), D. nanus (n = 15), Lysapsus limellum (n = 6), and Scinax nasicus (n = 21) in different types of environments. Crops include rainfed crops, rice, sugar cane, and Eucalyptus and/or Pinus afforestations; Natural includes the grasslands, wetlands, and native forests with low anthropic influence (i.e., extensive livestock farming); and Urban refers to urban and peri-urban areas, routes, or industrial plants.
Fig. 1 in Updating the distributions of four Uruguayan hylids (Anura: Hylidae): recent expansions or lack of sampling effort?
Fig. 1. Distribution of Dendropsophus minutus, D. nanus, Lysapsus limellum, and Scinax nasicus in Uruguay. Shaded areas correspond to estimated distributions according to Carreira and Maneyro (2019, yellow), and the closest national protected areas (green). Black dots indicate previous literature records from Gudynas and Rudolf (1983), Langone and Basso (1987), Olmos et al. (1997), Kolenc et al. (2003), Núñez et al. (2004), and Prigioni et al. (2011). New records in the present study are indicated in red. Department names are indicated as follows: AR, Departamento de Artigas; SA, Departamento de Salto; PA, Departamento de Paysandú; RN, Departamento de Río Negro; CL, Departamento de Cerro Largo; and TT, Departamento de Treinta y Tres.
Fig. 7 in The fossil record of early tetrapods: Worker effort and the end-Permian mass extinction
Fig. 7. Species discovery curves for several groups of fossil organisms show substantial differences in form. All discovery curves are shown as percentages, even though final totals, in 2003, are very different: trilobites (n = 4126), early tetrapods (n = 515), dinosaurs (n = 694), fossil birds (n = 221), and fossil mammals of North America (n = 3340). The horizontal line marks the "half life" of the discovery curve, the date by which half the currently valid taxa had accumulated. Data from these sources: trilobites (Tarver et al. 2007), dinosaurs (Benton 2008), fossil birds (Fountaine et al. 2008), fossil mammals (Alroy 2002).
Fig. 2 in The fossil record of early tetrapods: Worker effort and the end-Permian mass extinction
Fig. 2. Perceptions of early tetrapod diversity at three points in research time, 1900, 1950, and 2000. Total numbers of valid species are indicated per series; the 1900 data distribution differs significantly from those for 1950 and 2000, but the 1950 and 2000 distributions do not differ significantly (see text).
Fig. 1 in The fossil record of early tetrapods: Worker effort and the end-Permian mass extinction
Fig. 1. Discovery curve of valid early tetrapod species (i.e., tetrapods, excluding Lissamphibia and Amniota), plotted against publication year. Species determined as synonymous or dubious in recent revisions are excluded. The curves show proportions through time, rising to 100% of current knowledge, for all early tetrapods (n = 528) and two major sub−divisions, temnospondyls (n = 368), and lepospondyls (n = 85).
Fig. 6 in The fossil record of early tetrapods: Worker effort and the end-Permian mass extinction
Fig. 6. Cumulative discovery curve of species of early tetrapods showing the relative completeness for each of the eight stratigraphic series, divided into two panels, from Upper Devonian to Middle Permian (A), and Upper Permian to Upper Triassic (B), plotted against decades in research time. The horizontal line marks the "half life" of the discovery curve, the date by which half the currently valid taxa had accumulated. Numbers of taxa per series are: Upper Devonian (17), Lower Carboniferous (25), Upper Carboniferous (108), Lower Permian (125), Middle Permian (36), Upper Permian (20), Lower Triassic (100), Middle Triassic (46), Upper Triassic (5), Jurassic (5), Cretaceous (1).
Fig. 4 in The fossil record of early tetrapods: Worker effort and the end-Permian mass extinction
Fig. 4. Cumulative discovery curves of species of early tetrapods showing the relative completeness for each of the nine major geographic regions: North America, Europe, and Africa (A), South America, Greenland, and Australia (B), Asia, India, and Russia (C), plotted against decades in research time. The horizontal line marks the "half life" of the discovery curve, the date by which half the currently valid taxa had accumulated. Total numbers of taxa are given for each continent.
Fig. 3 in The fossil record of early tetrapods: Worker effort and the end-Permian mass extinction
Fig. 3. Histogram of the total number of valid early tetrapod species from each major geographic region. Totals are: Europe (171), North America (156), Russia (77), Africa (51), Australia (30), India (14), Asia excluding India (12), South America (11), and Greenland (9).
Data from: Wasted efforts impair random search efficiency and reduce choosiness in mate-pairing termites. American Naturalist
<div> <h1>README</h1> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#readme"></a></div> <div> <h2>Article Information</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#article-information"></a></div> <p>This repository provides access to the data and source code used for the manuscript</p> <div> <h3><strong>Wasted efforts impair random search efficiency and reduce the level of choosiness in mate-pairing termites</strong></h3> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#wasted-efforts-impair-random-search-efficiency-and-reduce-the-level-of-choosiness-in-mate-pairing-termites"></a></div> <div> <h4>Nobuaki Mizumoto, Naohisa Nataya, Ryusuke Fujisawa</h4> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#nobuaki-mizumoto-naohisa-nataya-ryusuke-fujisawa"></a></div> <p>Contact, Nobuaki Mizumoto: <a href="mailto:nzm0095@auburn.edu">nzm0095@auburn.edu</a></p> <p>This paper is accepted at The American Naturalist.<br>Preprint is available at <a href="http://img.shields.io/badge/DOI-10.1101/2024.02.01.578198.svg" rel="nofollow">bioRxiv</a>.</p> <p>This study examines how movement patterns of mate searchers of a termite <em>Reticulitermes speratus</em> changes according to time. Then investigated how this change in movement patterns affect random search efficiency and mate choice behavior.<br>This includes tracking data, R codes to analyze it, and Cpp code for simulations.</p> <div> <h2>Table of Contents</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#table-of-contents"></a></div> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/README.md">README</a></li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts">scripts</a> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/output.R">output.R</a> - output all results</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/processing.R">processing.R</a> - data processing of coordinates obtained from servosphere</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/simulations.R">simulations.R</a> - for data-based simulations</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/scripts/onesim.cpp">onesim.cpp</a> - functions for simulations</li> </ul> </li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/output">output</a> - all outputs are stored</li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data">data</a> <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/raw">raw</a> - raw data in .csv <ul> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/raw/ANTAM_4day">ANTAM_4day</a> - directory includes raw data obtained from servosphere. Ignore the first four columns. col4: x, col5: y, col6: time</li> <li><strong>colonyfoundation.csv</strong> - data for colony foundation experiments.</li> <li><strong>tandem_sum.csv</strong> - the number of observations of individual units in each experiment. units include solomale (single male), solofemale (single female), heterotandem (female-male tandem), male tandem (male-male tandem), female tandem (female-female tandem), and tandem3 (tandem run with >= 3 individuals).</li> <li><strong>tandem_timedevelopment.csv</strong> - breakdown of tandem_sum.csv. The measurement was not summarized but for each observation bouts (5: 0-5 min, 10: 5-10 min, ...).</li> <li><strong>termite_weight.csv</strong> - Termite weight measurement. fresh: fresh weight in mg.</li> </ul> </li> <li><a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/blob/main/analysis/data/fmt">fmt</a> - formatted data created in processing.R and simulations.R. The formatted data will be used for output.R. The all formatted data are in .rda files. The .csv files with the same contents are also generated for reviewing purpose. <ul> <li><strong>df_all.rda</strong> - The processed data of trajectories for furthur analysis. It has three datafrmaes named df_all (trajectories with traveled distance information for each frame), df_MSD (MSD data for each individual), df_pause (duration of pauses for each pausing events for each individual). The corresponding csv files are df_all.csv, df_MSD.csv, and df_pause.csv</li> <li><strong>df_sum.rda</strong> - The file further processed df_all.rda to summarize all parameters for each individual. df_sum.csv is corresponding.</li> <li><strong>df_sim.rda</strong> - The simulation results that record encounter time for each searching attempts. encounter_time = 1501 indicates the failure to encounter. df_sim.csv is corresponding.</li> </ul> </li> </ul> </li> </ul> <div> <h2>Session information</h2> <a href="https://github.com/nobuaki-mzmt/termite-mate-search-cost/tree/main#session-information"></a></div> <div> <pre><code>R version 4.3.1 (2023-06-16 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 11 x64 (build 22621) Matrix products: default locale: [1] LC_COLLATE=English_United States.utf8 [2] LC_CTYPE=English_United States.utf8 [3] LC_MONETARY=English_United States.utf8 [4] LC_NUMERIC=C [5] LC_TIME=English_United States.utf8 time zone: Asia/Tokyo tzcode source: internal attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] CircStats_0.2-6 boot_1.3-28.1 MASS_7.3-60 [4] stringr_1.5.0 survival_3.5-5 survminer_0.4.9 [7] ggpubr_0.6.0 Rcpp_1.0.10 PupillometryR_0.0.5 [10] rlang_1.1.1 dplyr_1.1.2 viridis_0.6.3 [13] viridisLite_0.4.2 ggplot2_3.4.2 Rmisc_1.5.1 [16] plyr_1.8.8 lattice_0.21-8 exactRankTests_0.8-35 [19] car_3.1-2 carData_3.0-5 lme4_1.1-34 [22] Matrix_1.6-1 data.table_1.14.8 </code></pre> </div>
Figure 1 in The first DNA barcodes for the Australian platypus tick Ixodes ornithorhynchi Lucas, 1846 (Acari: Ixodidae) to facilitate conservation efforts for a declining parasite and its host
Figure 1 Ventral view of Ixodes ornithorhynchi specimens from which DNA barcodes were generated. A – Adult female; B – Nymph.
Fig. 1 in Sampling effort and fish species richness in small terra firme forest streams of central Amazonia, Brazil
Fig. 1. Fish species accumulation curves estimated from samples obtained in 1st, 2nd, and 3rd order streams reaches located in the study areas of Biological Dynamics of Forest Fragments Project, Manaus, Amazonas State. The curves represent extrapolations from five reaches sampled in each stream segment.
The coevolution of effort and replication, recreated, replicated and corrected
<p>We replicated “The natural selection of bad science” by Paul Smaldino and Richard McElreath (2016). The replication was successful with one exception. We find that selection acting on scientist’s propensity for replication frequency caused a brief period of exuberant replication not observed in the original paper due to a coding error. This difference does not, however, change the authors’ original conclusions.</p> <p>The three panels displayed here concern Figure 5 – titled “The coevolution of effort and replication” – of the original study. Panel (a) is based on the original data and is a recreation of the original figure. Panel (b) is the result of a replication based on the same coding error and panel (c) displays the corrected result.</p> <p>While effort, false positive rate (α), and false-discovery rate converge to the originally reported values when the simulated steps are extended beyond the original 1e6 time steps, the replication rate’s progress and convergence are different from the original.</p> <p>The results of this corrected model show the following pattern: Starting with a high effort, low effort replications are more attractive than conducting novel research (that is, employing this strategy received higher payoffs), which results in the replication rate reaching nearly 100% after ~730,000 steps. At this point the decline of effort has made low-effort novel research more attractive than low-effort replications (because publishing a novel positive result is associated with a higher payof than publishing a replication) and consequently the replication rate decreases again. With the decline of effort, alpha rises up to 0.67, comparable with the value reported in the study by Smaldino and McElreath (2016).</p> <p>Smaldino, P. E., & McElreath, R. (2016). The natural selection of bad science. <em>Royal Society Open Science</em>, <em>3</em>(9), 160384. <a href="https://doi.org/10.1098/rsos.160384">https://doi.org/10.1098/rsos.160384</a></p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
Making The Taxonomic Effort: Data on Index Fungorum and IPNI new species and new combinations author gender
<p>Data on plant and fungal names and new combinations extracted in 2018 from the International Plant Names Index (IPNI) and Index Fungorum (IF) on names published from 1931 onwards. The gender of the authors of the names and combinations have been assigned from another data set on Zenodo here:</p> <p><strong>Lindon, Heather, Gardiner, Lauren, Vorontsova, Maria, & Brady, Abigail. (2020). Gendered Author List International Plant Names Index 2020 (Version 2) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.3911077">https://doi.org/10.5281/zenodo.3911077</a></strong></p> <p>The set of data here was used for an analysis of names and new combinations published by women for a paper in the Linnean Society entitled 'Making the Taxonomic Effort' Published April 2023 https://www.linnean.org/our-publications.</p>
Energetic trade-offs in migration decision-making, reproductive effort, and subsequent parental care in a long-distance migratory bird
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SSP: An R package to estimate sampling effort in studies of ecological communities
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Experimental evolution under varying sex ratio and behavioral plasticity in response to perceived competitive environment independently affect calling effort in male crickets
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Data from: Selectivity of invasive species suppression efforts influences control efficacy
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Piecewise continuous sampling: a method for minimizing bias and sampling effort for estimated metrics of animal behavior
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A new Double Observer based census framework to improve abundance estimations in mountain ungulates and other gregarious species with a reduced effort
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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.