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FIGURE 2 in A new genus and species of Sieblosiidae from the Middle Miocene of Germany (Odonata: Epiproctophora)
FIGURE 2. Germanostenolestes lutzi gen. and sp. nov., holotype 59573, reconstruction of right forewing (scale bar represents 5 mm).
FIGURE 1 in A new genus and species of Sieblosiidae from the Middle Miocene of Germany (Odonata: Epiproctophora)
FIGURE 1. Germanostenolestes lutzi gen. and sp. nov., holotype 59573, photograph of habitus, photograph copyright P.Crabb (Natural History Museum, London).
FIGURE 3 in A new genus and species of Sieblosiidae from the Middle Miocene of Germany (Odonata: Epiproctophora)
FIGURE 3. Germanostenolestes lutzi gen. and sp. nov., holotype 59573, reconstruction of right hindwing and pterostigma (scale bar represents 5 mm).
Appendix 1 - 3 for Cooper et al., IN REVIEW - A new large †pachycormiform (Teleostei: †Pachycormiformes) from the Lower Jurassic of Germany, with affinities to the sus-pension-feeding clade and comments on the gastrointestinal anatomy of pachycormid fishes
<p>Contains Appendix 1 - 3 for paper titled: A new large †pachycormiform (Teleostei: †Pachycormiformes) from the Lower Jurassic of Germany, with affinities to the suspension-feeding clade and comments on the gastrointestinal anatomy of pachycormid fishes. </p> <p>Appendix 1 = supp. figs + phylongeny data </p> <p>Appendix 2 = Friedman 2012 phylogenetic matrix </p> <p>Appendix 3 = Gouiric-Cavalli and Arratia 2022 phylogenetic matrix </p>
Generation and emission data for main power plants in Germany (2015 - 2021)
<p>This dataset contains generation and emission data for German main power plants (2015 - 2021). The underlying code with additional analysis is available on <a href="https://github.com/INATECH-CIG/CO2_emissions_factors_DE">Github</a> (still under development).</p> <p><strong>"blocks.xlsx":</strong></p> <p>This file collects power plant information per unit from different data sources. The <a href="https://www.entsoe.eu/data/energy-identification-codes-eic/">EIC </a>is the identifier used by ENTSO-E for the per unit generation time series [1]. This identifier is matched with BNA-ID and MaStR-Nr used in the official power plant list published by the <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Bundesnetzagentur</a> [2]. Information about the plant (location, name, block name, electrical and heat power capacity, fuel type) has been collected from the power plant lists from <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Bundesnetzagentur </a>[2] and <a href="https://www.umweltbundesamt.de/dokument/datenbank-kraftwerke-in-deutschland">Umweltbundesamt</a> [3]. Fuel-specific emission factors have been assigned from <a href="https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0">data published by Umweltbundesamt</a> [4] according to plant type and location. The ETS ID is the identifier of the installation in the <a href="https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en">EU emissions trading system</a>. Note that EIC, BNA-ID and MaStR-Nr differ for each generation unit, whereas the ETS ID relates to a stationary installation which might contain several generation units.</p> <p><strong>"block-generation.xslx"</strong><br> This file contains yearly generation for each generation unit calculated from the per unit generation time series published by ENTSO-E. Generation units are identified by the EIC and the BNA-ID.</p> <p><strong>"installation-results-yearly"</strong></p> <p>This file contains yearly results for generation and emissions for each installation identified by the ETS-ID [5]. Each installation might contain several generation units. Power generation has been calculated from per unit generation time series (ENTSO-E) for the units associated with the installation. Total emissions represent verified emissions as published as part of the EU emission trading system. These total emissions have been split up into emissions associated with power generation and heat provisioning based on the free allocations granted to the installation, <span class="math-tex">\(E_i=E_i^{el}+E_i^{heat}\)</span>. From emissions associated with power generation and total generation, installation-specific emission factors have been calculated, whereas fuel-specific emission factors are used to estimate the utilization factor of the installation.</p> <p>Emissions associated with heat are calculated by <span class="math-tex">\(E_{i}^{heat}=H_i\cdot hb\)</span>, where <span class="math-tex">\(H_i\)</span> is the heat provided by the installation and <span class="math-tex">\(hb=62.3 tCO_2/TJ\)</span> is the heat benchmark used in the EU ETS as part of the process for determining <a href="https://climate.ec.europa.eu/system/files/2016-11/gd1_general_guidance_en.pdf">free allocations</a> [6,7,8]. We assume that free allocations are calculated from the heat as follows:</p> <p><span class="math-tex">\(A_i(k) = H_i\cdot hb\cdot\beta(k)\cdot[\sigma + (1-\sigma)\gamma(k)]\)</span></p> <p>Here <span class="math-tex">\(A_i(k)\)</span> denotes the free allocations in year <span class="math-tex">\(2013+k\)</span> and <span class="math-tex">\(\beta(k)=(1-k\cdot 0,0174)\)</span> is the linear reduction factor. The term <span class="math-tex">\(\gamma(k)=(0.8-(0.5/7)\cdot k)\)</span> represents the carbon leakage exposure factor for provisioning to installations exposed to no significant carbon leakage risk. This factor is <span class="math-tex">\(1\)</span> for heat provisioning to installations which show a significant risk for carbon leakage. The parameter <span class="math-tex">\(\sigma\)</span> denotes the share of heat provided to the latter sector (with risk of carbon leakage), whereas <span class="math-tex">\((1-\sigma)\)</span> gives the remaining share of heat provided to the sector without significant risk of carbon leakage. The parameter <span class="math-tex">\(\sigma\)</span> has been calculated by comparing the given data for free allocations for consecutive years <span class="math-tex">\(A_i(k+1)/A_i(k)\)</span> under the assumption that <span class="math-tex">\(H_i(k+1)=H_i(k)\)</span>. Values for <span class="math-tex">\(\sigma\)</span> are given in the file "<strong>sigma.xlsx</strong>". Note that no free allocations are given for heat provided to installations taking part in the EU ETS, so these emissions are not covered by this approach. Not for all installation consistent information for free allocations was available, in these cases no entry in the file "installation-results-yearly.xslx" is given, respectively.</p> <p>The utilization factor is calculated by <span class="math-tex">\(G_i/((E_i^{el}/e(f_i))\)</span>, where <span class="math-tex">\(G_i\)</span> denotes the power generation at location <span class="math-tex">\(i\)</span>, <span class="math-tex">\(E_i^{el}\)</span> the emissions associated with power generation, and <span class="math-tex">\(e(f_i)\)</span> the associated fuel-specific emission factor for the installation. The installation-specific emission factor is calculated by <span class="math-tex">\(E_i^{el}/G_i\)</span>.</p> <p>Sources:</p> <p>[1] ENTSO-E Transparency Platform, <a href="https://transparency.entsoe.eu/generation/r2/actualGenerationPerGenerationUnit/show">Actual Generation per Generation Unit</a>.<br> [2] Bundesnetzagentur, <a href="https://www.bundesnetzagentur.de/SharedDocs/Downloads/DE/Sachgebiete/Energie/Unternehmen_Institutionen/Versorgungssicherheit/Erzeugungskapazitaeten/Kraftwerksliste/Kraftwerksliste_CSV.html?nn=266908">Kraftwerksliste</a>.<br> [3] Umweltbundesamt, <a href="https://www.umweltbundesamt.de/dokument/datenbank-kraftwerke-in-deutschland">Datenbank Kraftwerke in Deutschland</a>.<br> [4] Umweltbundesamt, <a href="https://www.umweltbundesamt.de/publikationen/co2-emissionsfaktoren-fuer-fossile-brennstoffe-0">CO2-Emissionsfaktoren für fossile Brennstoffe</a>, Aktualisierung 2022.<br> [5] EU Union Registry, <a href="https://climate.ec.europa.eu/eu-action/eu-emissions-trading-system-eu-ets/union-registry_en#european-union-transaction-log">Verified Emissions 2021</a>.<br> [6] European Commission, <a href="https://climate.ec.europa.eu/system/files/2016-11/gd1_general_guidance_en.pdf"> Guidance Document n°1 on the harmonized free allocation methodology for the EU-ETS<br> post 2012</a>.<br> [7] Agora Energiewende, <a href="https://www.agora-energiewende.de/veroeffentlichungen/die-deutsche-braunkohlenwirtschaft-1/">Die deutsche Braunkohlenwirtschaft</a> (2017)<br> [8] Jan Frederick Unnewehr, Anke Weidlich, Leonhard Gfüllner, Mirko Schäfer, <a href="https://www.sciencedirect.com/science/article/pii/S2772783122000176">Open-data based carbon emission intensity signals for electricity generation in European countries – top down vs. bottom up approach</a>, Cleaner Energy Systems 3, 100018 (2022).</p> <p><br> </p> <p><br> </p>
Figure 1 in A new putative moss bug (Insecta: Hemiptera) from the lower Permian of the Saar-Nahe Basin, SW Germany, and the age of Coleorrhyncha
Figure 1. Approximate geographical and stratigraphical position of the Grügelborn locality (marked by asterisk) with rough absolute ages indicated (slightly modified from Nel and Poschmann 2020).
Figure 4. Peloridium hammoniorum Breddin 1897 in A new putative moss bug (Insecta: Hemiptera) from the lower Permian of the Saar-Nahe Basin, SW Germany, and the age of Coleorrhyncha
Figure 4. Peloridium hammoniorum Breddin 1897, an extant moss bug for comparison. Parque Nacional de Chiloé, Rancho Grande, Chile (leg. D. Burckhardt). Photograph with wing venation labelled. Scale bar = 0.2 mm.
Figure 3 in A new putative moss bug (Insecta: Hemiptera) from the lower Permian of the Saar-Nahe Basin, SW Germany, and the age of Coleorrhyncha
Figure 3. Permoridium fresenaci gen. et sp. nov., holotype UGKU 1096, explanatory sketch drawing with venation labelled. Scale bar = 5 mm.
Research data related to the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)"
<p><strong>Research Data related to the publication "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023) published in <em>Water Resources Research</em> </strong></p> <p>Dear reader,</p> <p>research data are provided for the article "Paleo-Hydrogeological Modeling to Understand Present-Day Groundwater Salinities in a Low-Lying Coastal Groundwater System (Northwestern Germany)" by Seibert et al. (2023). The authors hope that the research data allows for a better understanding of the paleo-modeling workflow. Feedback on the model files or questions regarding the modeling approach etc. can be addressed to the authors of the article, see contact details below. The research data comprises the following files:</p> <ul> <li>files related to the parameter estimation procedure using PEST (Doherty, 2021a,b) (see subfolder "<em>parameter_estimation</em>")</li> <li>iMOD-Python (Visser and Bootsma, 2019) scripts to create the iMOD-WQ (Verkaik et al., 2021) input files for each model variant. Note that model variants consist of several time slice models, indicated by the corresponding file names, e.g., '<em>Model_BC_slice_01.py'</em> etc. (see '<em>scripts.zip</em>' in the subfolders 'Model BC', 'Model CP', 'Model NE-ND-NP', 'Model NE-NP', 'Model NG', 'Model NP', 'Model R1', 'Model R2', 'Model R3', 'Model R4', 'Model R5', 'Model R6', 'Model SS')</li> <li>simulation output files, including concentration and head data for each model stress period (3-D), mean/max. concentration and head data for each model stress period (2-D), as well as depth [mbgs] of different salinity interfaces (2-D), i.e., marking the transitions from fresher to more saline groundwater using thresholds of 0.45 ('<em>depth_interface_mbgs</em>'), 1, 5, 10 and 20 g TDS L<sup>-1</sup>, respectively (see subfolders '<em>output/npy_arrays'</em> within each model variant subfolder). Moreover, sea levels, time slice names and stress period numbers are provided in the '<em>output/npy_arrays'</em> subfolders as well as final concentrations and heads (3-D) for each time slice model of each model variant (e.g., '<em>Model_BC_slice_01_final_concentrations.npz</em>' and '<em>Model_BC_slice_01_final_heads.npz</em>'; see '<em>output.zip'</em> in the model variant subfolders)</li> <li>iMOD-Python (Visser and Bootsma, 2019) input files, such as digital elevation models, geologic models etc. (see subfolder '<em>imod_input'</em>). However, in most cases no consent for re-distribution of these data sets exists, and they cannot be made freely available through this publication. Please, consult the corresponding meta-data files or get in touch with one of the authors for further information</li> <li>bash scripts for the execution of iMOD-Python .py- and iMOD-WQ .run-files in a linux environment (see subfolder '<em>bash_scripts'</em>)</li> <li>figure files as well as the corresponding .py and .m scripts and shape-files, where applicable (see subfolder '<em>figures'</em>); note that consent for re-distribution for some figure input files doesn't exist, compare corresponding meta-data files</li> <li>videos presenting the concentration evolution of the different model variants (vertically averaged concentrations & cross-sectonal view, see subfolder '<em>videos'</em>)</li> </ul> <p>Meta-data files are usually provided with data files in the different subfolders for clarification.</p> <p>iMOD-WQ (Verkaik et al., 2021) input data and .run-files were executed on the University Oldenburg High-Performance Cluster 'Carl', running simulations in parallel with 32 computational cores.</p> <p>Further information on the iMOD suite can be found here: https://deltares.github.io/iMOD-Documentation/</p> <p>Literature:</p> <p>Doherty, J. E., (2021a). PEST Model-Independent Parameter Estimation User Manual Part I: PEST, SENSAN and Global Optimisers. Watermark Numerical Computing. p.394.</p> <p>Doherty, J. E. (2021b). PEST Model-Independent Parameter Estimation User Manual Part II: PEST Utility Support Software. Watermark Numerical Computing. p.274.</p> <p>Verkaik, J., Hughes, J. D., van Walsum, P. E. V., Oude Essink, G. H. P., Lin, H. X., & Bierkens, M. F. P. (2021). Distributed memory parallel groundwater modeling for the Netherlands Hydrological Instrument. Environmental Modelling & Software, 143, p.105092.</p> <p>Visser, M., & Bootsma, H. (2019). iMOD-Python: Work with iMOD MODFLOW models in Python. Retrieved from https://imod.xyz/</p> <p><strong>If you have further questions, please, contact one of the following authors</strong>: Stephan L. Seibert (stephan.seibert@uol.de), Janek Greskowiak (janek.greskowiak@uol.de) or Gudrun Massmann (gudrun.massmann@uol.de)</p>
FIGURE 3. Rubus lentianus. A, B in Rubus lentianus, a new species from Austria and Germany-another piece of the puzzle from a European hotspot of Rubus series Radula
FIGURE 3. Rubus lentianus. A, B: middle section of first-year stem with leaves; C: leaf of first-year stem (upper side); D: leaf of first-year stem (lower side); E: section of first-year stem; F: inflorescence; G: part of inflorescence with a close-up of a flower; H: young collective fruit. Austria, Upper Austria, A–G: Tarsdorf, 7 July 2021, H: Aschach a. d. Donau, 7 July 2020, phot. G. Király.
FIGURE 2 in Rubus lentianus, a new species from Austria and Germany-another piece of the puzzle from a European hotspot of Rubus series Radula
FIGURE 2. Rubus lentianus: A: section of leafed first-year stem; B: detail of first-year stem; C: margin of terminal leaflet; D: infructescence; E: inflorescence axis; F: peduncle; G: flower; H: petal; I: young carpel. Del. J. Táborská.
Data from: Influence of abiotic and biotic factors on benthic marine community composition, structure and stability: a multidisciplinary approach to molluscan assemblages from the Miocene of northern Germany
<p><span>The Miocene mica-clay deposits of Groß Pampau (northern Germany) are well known for their diverse assemblages of marine mammals, particularly whales. Despite numerous systematic and biostratigraphic studies, an in-depth palaeoecological analysis of its diverse molluscan assemblages and a comprehensive palaeoenvironmental reconstruction are lacking. Here, we integrated new faunal, sedimentological, and geochemical data to reconstruct the marine palaeo-ecosystem of the late Miocene sedimentary succession of Groß Pampau, and to identify the drivers controlling the composition, ecological structure and temporal dynamics of its macrobenthic molluscan assemblages. Fossil evidence, coupled with analyses of clay mineral composition, grain size distribution, and geochemical data (total organic carbon, total nitrogen, δ<sup>13</sup>C, δ<sup>18</sup>O, δ<sup>15</sup>N of sediment and shells), suggest a warm-temperate, mesotrophic, low-energy, offshore marine setting mostly below storm wave base and a pronounced surface-to-bottom water temperature gradient. Low variability in sedimentological and geochemical signals indicates generally stable physico-chemical conditions, whereas occurrences of opportunistic species (<em>Varicorbula</em> <em>gibba</em>) point at </span><span>occasionally less favourable bottom conditions, possibly related to transient hypoxia</span><span>. Canonical correspondence analysis reveals that the distribution of molluscan assemblages correlates with total organic carbon and nitrogen content, suggesting organic matter availability at the seafloor as a controlling factor. A pattern of repetitive punctuated stasis of molluscan assemblages is defined by the temporal persistence in taxonomic and ecological composition, occasionally interrupted by shifts to a different faunal configuration. We suggest that both stable environmental conditions and biotic interactions, i.e. the top-down control exerted by carnivorous gastropods and environmental modification by ubiquitous burrowing deposit feeders, probably contributed to the observed temporal stability. Whereas comparison with Miocene molluscan assemblages of Gram, Denmark, revealed differences in the presence and relative abundance of genera, functional congruence indicates that offshore benthic ecosystems of the southern North Sea Basin functioned similarly.</span></p>
Fig. 4 in The first Palaeozoic spider (Arachnida: Araneae) from Germany
Fig. 4 Reconstruction of the possible appearance of Arthrolycosa wolterbeeki sp. nov., in life. Anterior region of carapace based on other Arthrolycosa material; leg spination draws on comparisons with modern mesothele spiders
Fig. 3 in The first Palaeozoic spider (Arachnida: Araneae) from Germany
Fig. 3 Details of the specimen shown in Fig. 1. a Setose distal ends of walking legs I and II. b Setae adorning the pedipalp. c Putative spinneret show- ing evidence of segmentation (arrowed). Scale bars equal 1 mm
Fig. 1 in The first Palaeozoic spider (Arachnida: Araneae) from Germany
Fig. 1 Arthrolycosa wolterbeeki sp. nov., the oldest fossil spider (Arachnida: Araneae) from Germany, from the late Carboniferous of Piesberg near Osnabruck, Lower Saxony. a Part. b Counterpart. Scale bar equals 5 mm
Fig. 5 in Genetic and chemical diversity of the toxic herb Jacobaea vulgaris Gaertn. (syn. Senecio jacobaea L.) in Northern Germany
Fig. 5. Principal co-ordinate analysis (PCoA) ordination of the genetic similarity of 75 individuals of J. vulgaris from Northern Germany. Coordinate 1 explains 29.47% of the shown variability and coordinate 2 explains 10.00% of the shown variability. Colours indicate membership to the different populations.
Fig. 3 in Genetic and chemical diversity of the toxic herb Jacobaea vulgaris Gaertn. (syn. Senecio jacobaea L.) in Northern Germany
Fig. 3. PA content and composition of J. vulgaris. A) Averaged PA content in mg/kg dry weight of the individuals per population (± SEM). At first total PA content of every individual was calculated and afterwards the mean content of all individuals per population. Significant differences to the total averaged PA content (dotted line) are indicated by *. B) PA diversity of the populations shown by mean numbers of different PAs detected in populations (± SEM). Significant differences to the total averaged number of PA (dotted line) are indicated by *. C) Categorisation of J. vulgaris chemotypes: Difference in relative abundance of erucifoline and jacobine per population. Bars below 0 indicate jacobine type and bars over 0 indicate erucifoline type populations. *p <0.05, **p <0.01, ***p <0.001.
FIGURE 6. A, B in Epibiontic life on intertidal Setaphyes kielensis and S. dentatus (Kinorhyncha, Pycnophyidae) from Sylt, North Sea, Germany, with a description of a new species of Trematosoma (Ciliophora, Acinetidae) and a redescription of Cothurnia buetschlii (Ciliophora, Vaginicolidae)
FIGURE 6. A, B. Drawing of Cothurnia buetschlii (specimen 2) ventrally on segment 10 of Setaphyes kielensis (ZMB 12486) as longitudinal optical section (A) and lorica in cross-section (B). Abbreviations: an, annulation on surface of zooid; en, endostyle with striae; inf, withdrawn infundibulum with cilia; lo, lorica; ma, macronucleus; mi, micronucleus; po, pore in cementing mass; sc, spherical cavity; st, stalk with internal striae; zo, zooid. Scale bar 30 µm.
FIGURE 4 in Epibiontic life on intertidal Setaphyes kielensis and S. dentatus (Kinorhyncha, Pycnophyidae) from Sylt, North Sea, Germany, with a description of a new species of Trematosoma (Ciliophora, Acinetidae) and a redescription of Cothurnia buetschlii (Ciliophora, Vaginicolidae)
FIGURE 4. Setaphyes kielensis (A, ZMB 12469; B, ZMB 12489; C, ZMB 12486; D, E, ZMB 12492; F, ZMB 12488) with specimens of Trematosoma husselae sp. nov., SEM (A), and DIC (B–F). A. Segment 1, empty shells of epibionts, ventral view. B. Segments 1–11, optical section. C. Epibionts with capitated tentacles arranged in left and right row. Extended focus image of three images. D. Epibiont with developing swarmer. Arrowheads mark membrane of swarmer. E. Epibiont with swarmer recently freed from its parent. F. Segments 4–6, ventral view. Note thickened lorica in basal two thirds of epibionts. Arrow marks longitudinal striation of stalk of epibiont. Abbreviations: ca, basal cavity in lorica; co, specimen of Cothurnia buetschlii; lo, lorica; lts, lateral terminal spine; ma, macronucleus; mi, micronucleus; s, segment, followed by segment number; su, suctorian epibiont; sw, swarmer; te, tentacle. Scale bar in A, D, E 20 µm, in B 100 µm, in C 30 µm, and in F 50 µm.
FIGURE 8 in Epibiontic life on intertidal Setaphyes kielensis and S. dentatus (Kinorhyncha, Pycnophyidae) from Sylt, North Sea, Germany, with a description of a new species of Trematosoma (Ciliophora, Acinetidae) and a redescription of Cothurnia buetschlii (Ciliophora, Vaginicolidae)
FIGURE 8. Setaphyes kielensis (A, ZMB 12468; B, ZMB 12474) with epibionts, SEM. A. Segment 6, left side. Arrow marks laterally recessed anterior margin of lorica of Cothurnia buetschlii. B. Segments 8–10, dorsal view. Abbreviations: co, Cothurnia buetschlii; fp, filamentous prokaryotes; s, segment, followed by segment number; su, suctorian epibiont; te, tentacles. Scale bar in A 10 µm and in B 20 µm.
ScienceDex guides
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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.