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3,136 results for “Terrestrialization”
Bornean Terrestrial Molluscs (myspecies): Resource (164) DwCA
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Marine, terrestrial and freshwater isopods (myspecies): Resource (372) DwCA
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Dataset for paper "Effects of Heavy Metal Accumulation Mediated by Floral Rewards on Key Stages of Growth and Development of Bumblebees (Bombus terrestris L.)"
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FIGURE 20 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 20. Morphology of Rhexinema sancta-tomea ACOI 592 grown on MiEB12 medium.
FIGURE 17 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 17. Morphology of Rhexinema sarcinoidea UTEX 1710 grown on MiEB12 medium.
FIGURE 7 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 7. Morphology of Paulbroadya prostrata SAG 23.92 grown on 1/2SWES medium.
FIGURE 6 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 6. Morphology of Pseudendoclonium commune SAG 2051 grown on 1/2SWES medium.
FIGURE 5 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 5. Morphology of Pseudendoclonium submarinum SAG 2237 grown on SWES medium.
FIGURE 18 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 18. Morphology of Rhexinema planctonica UTEX 1570 grown on MiEB12 medium.
FIGURE 23 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 23. Morphology of Vischerioclonium submersum UTEX 1913 grown on 3NBBM medium.
FIGURE 22 in Toward a monograph of non-marine Ulvophyceae using an integrative approach (Molecular phylogeny and systematics of terrestrial Ulvophyceae II.)
FIGURE 22. Morphology of Tupiella speciosa CCALA 423 grown on 3NBBM medium.
Leaf economic strategies drive global variation in phosphorus stimulation of terrestrial plant production
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FIGURE 1 in Pecteilis korigadensis (Orchidaceae: Orchidoideae), a new terrestrial orchid from the northern Western Ghats, India
FIGURE 1. Distribution of Pecteilis korigadensis in Maharashtra.
FIGURE 2 in Pecteilis korigadensis (Orchidaceae: Orchidoideae), a new terrestrial orchid from the northern Western Ghats, India
FIGURE 2. Pecteilis korigadensis in its natural habitat with inflorescence details.
Table 1 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
<p><b>Table 1.</b> Arrangement and annotation of the mitochondrial genome of Cubaris murina.</p><table><tbody><tr><th></th><th></th><th></th><th></th><th></th><th>Length</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>Name</th><td>Type</td><td>Start</td><td>Stop</td><td>Strand</td><td>(bp)</td><td>Start</td><td>Stop</td><td>Inter-genic space</td><td>Overlap</td></tr><tr><th>Cox1</th><td>Coding</td><td>1</td><td>1536</td><td>+</td><td>1536</td><td>ATG</td><td>TAA</td><td>1</td><td></td></tr><tr><th>trnL2(tta)</th><td>tRNA</td><td>1538</td><td>1598</td><td>+</td><td>61</td><td></td><td></td><td>48</td><td></td></tr><tr><th>Cox2</th><td>Coding</td><td>1647</td><td>2282</td><td>+</td><td>636</td><td>ATA</td><td>TAG</td><td></td><td>2</td></tr><tr><th>trnK(aaa)</th><td>tRNA</td><td>2281</td><td>2336</td><td>+</td><td>56</td><td></td><td></td><td></td><td>8</td></tr><tr><th>trnD(gac)</th><td>tRNA</td><td>2329</td><td>2403</td><td>+</td><td>75</td><td></td><td></td><td></td><td>17</td></tr><tr><th>atp8</th><td>Coding</td><td>2387</td><td>2533</td><td>+</td><td>147</td><td>ATA</td><td>TAA</td><td></td><td>4</td></tr><tr><th>atp6</th><td>Coding</td><td>2530</td><td>3192</td><td>+</td><td>663</td><td>ATA</td><td>TAA</td><td>2</td><td></td></tr><tr><th>Cox3</th><td>Coding</td><td>3195</td><td>3989</td><td>+</td><td>795</td><td>ATG</td><td>TAG</td><td></td><td>2</td></tr><tr><th>trnR(cga)</th><td>tRNA</td><td>3988</td><td>4055</td><td>+</td><td>68</td><td></td><td></td><td>55</td><td></td></tr><tr><th>nad3</th><td>Coding</td><td>4111</td><td>4407</td><td>+</td><td>297</td><td>ATA</td><td>TAA</td><td></td><td>9</td></tr><tr><th>trnA(gca)</th><td>tRNA</td><td>4399</td><td>4446</td><td>+</td><td>48</td><td></td><td></td><td></td><td>8</td></tr><tr><th>nad1 CR putative</th><td>Coding</td><td>4439 5219</td><td>5218 5360</td><td>−</td><td>780 142</td><td>ATG</td><td>TAG</td><td></td><td>14</td></tr><tr><th>NCR1</th><td>tRNA</td><td>5361</td><td>5429</td><td>+</td><td>69</td><td></td><td></td><td></td><td>17</td></tr><tr><th>rrnS</th><td>rRNA</td><td>5413</td><td>6139</td><td>+</td><td>727</td><td></td><td></td><td>48</td><td></td></tr><tr><th>trnW(tga)</th><td>tRNA</td><td>6188</td><td>6244</td><td>+</td><td>57</td><td></td><td></td><td></td><td>7</td></tr><tr><th>trnS1(aga)</th><td>tRNA</td><td>6238</td><td>6296</td><td>−</td><td>59</td><td></td><td></td><td></td><td></td></tr><tr><th>NCR2</th><td></td><td>6297</td><td>6666</td><td></td><td>370</td><td></td><td></td><td></td><td></td></tr><tr><th>trnL1(cta)</th><td>tRNA</td><td>6667</td><td>6731</td><td>−</td><td>65</td><td></td><td></td><td>29</td><td></td></tr><tr><th>cob</th><td>Coding</td><td>6759</td><td>7907</td><td>−</td><td>1,149</td><td>ATA</td><td>TAG</td><td>38</td><td></td></tr><tr><th>trnT(aca)</th><td>tRNA</td><td>7946</td><td>8017</td><td>−</td><td>72</td><td></td><td></td><td>29</td><td></td></tr><tr><th>nad5</th><td>Coding</td><td>8047</td><td>9648</td><td>+</td><td>1,602</td><td>ATG</td><td>TAG</td><td></td><td>3</td></tr><tr><th>trnF(ttc)</th><td>tRNA</td><td>9646</td><td>9707</td><td>+</td><td>62</td><td></td><td></td><td></td><td>15</td></tr><tr><th>trnH(cac)</th><td>tRNA</td><td>9693</td><td>9758</td><td>−</td><td>66</td><td></td><td></td><td></td><td>23</td></tr><tr><th>nad4</th><td>Coding</td><td>9736</td><td>11,082</td><td>−</td><td>1,312</td><td>ATA</td><td>TAA</td><td>13</td><td></td></tr><tr><th>nad4L</th><td>Coding</td><td>11,096</td><td>11,374</td><td>−</td><td>279</td><td>ATA</td><td>TAA</td><td></td><td>13</td></tr><tr><th>trnP(cca)</th><td>tRNA</td><td>11,362</td><td>11,422</td><td>−</td><td>61</td><td></td><td></td><td>25</td><td></td></tr><tr><th>nad6</th><td>Coding</td><td>11,448</td><td>11,903</td><td>+</td><td>456</td><td>ATA</td><td>TAG</td><td></td><td>2</td></tr><tr><th>trnS2(tca)</th><td>tRNA</td><td>11,902</td><td>11,962</td><td>+</td><td>61</td><td></td><td></td><td>17</td><td></td></tr><tr><th>rrnL</th><td>rRNA</td><td>11,980</td><td>12,549</td><td>−</td><td>570</td><td></td><td></td><td></td><td></td></tr><tr><th>NCR3</th><td></td><td>12,550</td><td>12,753</td><td></td><td>204</td><td></td><td></td><td></td><td></td></tr><tr><th>trnE(gaa)</th><td>tRNA</td><td>12,754</td><td>12,812</td><td>−</td><td>59</td><td></td><td></td><td></td><td></td></tr><tr><th>NCR4</th><td></td><td>12,813</td><td>12,950</td><td></td><td>138</td><td></td><td></td><td></td><td></td></tr><tr><th>trnV(gta)</th><td>tRNA</td><td>12,951</td><td>13,019</td><td>−</td><td>69</td><td></td><td></td><td></td><td>5</td></tr><tr><th>trnQ(caa)</th><td>tRNA</td><td>13,015</td><td>13,077</td><td>−</td><td>63</td><td></td><td></td><td></td><td>6</td></tr><tr><th>trnM(atg)</th><td>tRNA</td><td>13,072</td><td>13,141</td><td>+</td><td>70</td><td></td><td></td><td>25</td><td></td></tr><tr><th>nad2</th><td>Coding</td><td>13,167</td><td>14,123</td><td>+</td><td>978</td><td>ATA</td><td>TAG</td><td></td><td>15</td></tr><tr><th>trnC(tgc)</th><td>tRNA</td><td>14,109</td><td>14,158</td><td>−</td><td>50</td><td></td><td></td><td></td><td></td></tr><tr><th>trnY(tac)</th><td>tRNA</td><td>14,159</td><td>14,205</td><td>−</td><td>47</td><td></td><td></td><td>7</td><td></td></tr></tbody></table>
Table 2 in Complete mitochondrial genome of the terrestrial isopod Cubaris murina Brandt, 1833: new family gene order and novel tRNA secondary structures
<p><b>Table 2.</b> Base composition (%) of nucleotide, AT content, and AT- and GC-skew of the mitochondrial genome of <i>Cubaris murina.</i> Values in bold indicate positive AT-skew.</p><table><tbody><tr><th></th><th></th><th></th><th>Base composition (%)</th><th></th><th></th><th></th><th></th></tr></tbody><tbody><tr><th>Total</th><td></td><td>A</td><td>C</td><td>G</td><td>T</td><td>%AT</td><td>AT skew</td><td>GC skew</td></tr><tr><th>14,212 bp</th><td>28.90%</td><td>15.80%</td><td>23.40%</td><td>31.90%</td><td>60.80%</td><td>−0.049</td><td>0.194</td></tr><tr><th></th><td></td><td></td><td>Base composition (%)</td><td></td><td></td><td></td><td></td></tr><tr><th>Gene Strand</th><td>A</td><td>C</td><td>G</td><td>T</td><td>%AT</td><td>AT skew</td><td>GC skew</td></tr><tr><th><i>cox1</i></th><td>(+)</td><td>22.4%</td><td>18.6%</td><td>24.2%</td><td>34.8%</td><td>57.2%</td><td>−0.217</td><td>0.131</td></tr><tr><th><i>cox2</i></th><td>(+)</td><td>20.6%</td><td>21.7%</td><td>27.7%</td><td>30.0%</td><td>50.6%</td><td>−0.186</td><td>0.121</td></tr><tr><th><i>atp8</i></th><td>(+)</td><td>21.1%</td><td>15.0%</td><td>34.7%</td><td>29.3%</td><td>50.4%</td><td>−0.163</td><td>0.396</td></tr><tr><th><i>atp6</i></th><td>(+)</td><td>20.4%</td><td>19.8%</td><td>29.1%</td><td>30.8%</td><td>51.2%</td><td>−0.203</td><td>0.190</td></tr><tr><th><i>cox3</i></th><td>(+)</td><td>17.2%</td><td>23.6%</td><td>28.6%</td><td>30.6%</td><td>47.8%</td><td>−0.280</td><td>0.096</td></tr><tr><th><i>nad3</i></th><td>(+)</td><td>20.2%</td><td>16.2%</td><td>32.3%</td><td>31.3%</td><td>51.5%</td><td>−0.216</td><td>0.332</td></tr><tr><th><i>nad1</i></th><td>(−)</td><td>31.7%</td><td>22.2%</td><td>27.6%</td><td>18.6%</td><td>50.3%</td><td><b>0.260</b></td><td>0.108</td></tr><tr><th><i>NCR1</i></th><td></td><td>28.9%</td><td>27.5%</td><td>19.7%</td><td>23.9%</td><td>52.8%</td><td>0.095</td><td>−0.165</td></tr><tr><th><i>NCR2</i></th><td></td><td>27.0%</td><td>22.2%</td><td>18.1%</td><td>32.7%</td><td>59.7%</td><td>−0.095</td><td>−0.102</td></tr><tr><th><i>cob</i></th><td>(−)</td><td>36.0%</td><td>12.0%</td><td>23.5%</td><td>28.5%</td><td>64.5%</td><td><b>0.116</b></td><td>0.324</td></tr><tr><th><i>nad5</i></th><td>(+)</td><td>28.5%</td><td>10.5%</td><td>21.7%</td><td>39.3%</td><td>67.8%</td><td>−0.159</td><td>0.348</td></tr><tr><th><i>nad4</i></th><td>(−)</td><td>38.4%</td><td>11.9%</td><td>22.8%</td><td>26.9%</td><td>65.3%</td><td><b>0.176</b></td><td>0.314</td></tr><tr><th><i>nad4L</i></th><td>(−)</td><td>41.9%</td><td>12.2%</td><td>17.6%</td><td>28.3%</td><td>70.2%</td><td><b>0.194</b></td><td>0.181</td></tr><tr><th><i>nad6</i></th><td>(+)</td><td>25.7%</td><td>10.5%</td><td>17.8%</td><td>46.1%</td><td>71.8%</td><td>−0.284</td><td>0.258</td></tr><tr><th><i>NCR3</i></th><td></td><td>36.8%</td><td>9.8%</td><td>19.1%</td><td>34.3%</td><td>71.1%</td><td>0.035</td><td>0.322</td></tr><tr><th><i>NCR4</i></th><td></td><td>35.5%</td><td>13.0%</td><td>15.2%</td><td>36.2%</td><td>71.7%</td><td>−0.010</td><td>0.078</td></tr><tr><th><i>nad2</i></th><td>(+)</td><td>28.9%</td><td>12.4%</td><td>22.6%</td><td>36.1%</td><td>65.0%</td><td>−0.111</td><td>0.291</td></tr></tbody></table>
Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated human-Earth System Models
<p>Soil and vegetation carbon densities play a critical role in global and regional human-Earth system models. These densities affect variables such as land use change emissions and also influence land use change pathways under climate mitigation scenarios where terrestrial carbon is assigned a carbon price. Recently, more spatially explicit, fine resolution data have become available for both soil and vegetation carbon. However, for models to effectively use these data the fine resolution data need to be reharmonized to initial land use and land cover conditions represented by these models. Without such reharmonization the carbon values may be very inaccurate for particular land types and places where the source data and the model disagree on the land use/cover type. Here we present reharmonized soil and vegetation carbon densities both at the grid cell level at 5 arcmin resolution and also aggregated to 235 water sheds for 4 different land use and 15 land cover types. These data are particularly useful as initial land carbon conditions for global Multisectoral Dynamic Models (MSD). Moreover, these data include six different statistical states calculated using distinct resampling methods for each of the land use, land cover types. These statistical states are used to define a range of possible carbon values for each land classification, and any state can be used for defining initial conditions of soil and vegetation carbon in MSD models. We make use of these statistical states to calculate spatially distinct uncertainties in the carbon densities by land type. We have implemented these data in a state-of-the-art multi sector dynamics model, namely the Global Change Analysis Model (GCAM), and show that these new data improve several land use responses in the model, especially when terrestrial carbon is assigned a carbon price. The statistical states in our data are validated against similar estimates in the literature both at a grid cell level and at a regional level. </p> <p>This is a data record which corresponds to the paper "Spatially explicit re-harmonized terrestrial carbon densities for calibrating Integrated Multisectoral Models" (Narayan et al. 2023, under review)</p> <p>We have now also added a tabular version of the dataset aggregated to GTAP's AEZ definitions as opposed to GCAM's GLUs</p> <p> </p>
Field-Acquired DBH Measurements of Trees in Durango, Mexico, using Terrestrial Laser Scanning (TLS) and Tree Caliper
<p><strong>Introduction<br></strong>The data generated in this study provide a valuable resource for developing and testing algorithms focused on circumference fitting and diameter estimation of tree stems. By combining field-measured diameters and high-resolution terrestrial laser scanning (TLS) data, the dataset includes precise point clouds representing individual trees, with diameter at breast height (DBH) measured at 1.30 meters above ground level. This standardized height ensures consistency in diameter measurements, a crucial metric in forestry and ecological studies. The dataset supports advancements in remote sensing applications, offering researchers an opportunity to refine methods for accurately determining DBH and enhancing the reliability of biomass and carbon estimation models.<strong><br><br>Study Area<br></strong>The research was conducted in Mexico within a permanent forest research plot, established based on the methodology outlined by Corral-Rivas et al. The plot covers a quadrangular area of 625 m² and is situated in the Sierra Madre Occidental region, specifically within the "La Victoria" management unit in the municipality of Pueblo Nuevo, Durango, Mexico. This area experiences a temperate climate, with average annual temperatures between 20 and 22 °C and rainfall ranging from 800 to 1200 mm per year. The dominant vegetation type is a coniferous forest, primarily consisting of <em>Pinus cooperi</em>, with an estimated tree density of 960 trees per hectare.<strong><br><br>Field Data</strong><br>In this study, we focused on 50 trees with diameters exceeding 10 cm, a commonly used threshold in forest inventory protocols for estimating biomass and carbon, chosen to enhance accuracy and consistency. According to Hoover and Smith, excluding smaller trees generally has a negligible effect on biomass estimates across most forest types, which supports the practicality of this 10 cm cutoff. This threshold also helps eliminate saplings, which can add variability due to their inconsistent growth patterns, and aligns with standard inventory practices. We conducted a conventional inventory to measure DBH for each tree, using a Häglof caliper and taking two measurements at perpendicular angles to achieve a reliable average (<strong>data_field.xlsx</strong>).<br><br><strong>Data Capture and Acquisition via Terrestrial Laser Scanning</strong><br>The terrestrial laser scanning data were collected using a FARO Focus M70 scanner, capable of measuring distances up to 70 meters with an accuracy of ±3 mm. The scanner was set to an "exterior" profile with a resolution of 1/4 and quality level of 4x, achieving an average point density of 234,679 points per square meter and a resolution of 10,310 x 4,268 points. Before starting the scans, ten targets were strategically positioned on-site to assist with alignment during post-processing. Four scans were then performed, with a 180-degree vertical angle and a 360-degree horizontal angle, capturing a complete view of the surrounding environment. The scans were merged using FARO Scene software to ensure accurate alignment. All details regarding the scanner and tree positions can be found in Appendix A.<br><br></p> <p><strong>Extraction of 2D Planes from Individual Tree Stem Point Clouds</strong><br>The point cloud was initially loaded into CloudCompare, where selected trees were manually segmented, and ground points were removed to minimize slope effects. Each tree was represented as a series of n points in three-dimensional space: <br><br>(x_1, y_1, z_1), (x_2, y_2, z_2), ..., (x_i, y_i, z_i), ..., (x_n, y_n, z_n)</p> <p>For each tree stem, a specific cylindrical section was isolated by selecting points with z-coordinates between 1.25 m and 1.35 m, filtering out points outside this range. This method enabled the extraction of a segment corresponding to the diameter at breast height (DBH).</p> <p>Following this step, the z-coordinates within the selected cylindrical sections were excluded, and duplicate points were removed, resulting in a two-dimensional dataset. This reduction in dimensionality produced a new collection of n points:</p> <p>(x_1,y_1),(x_2,y_2),…,(x_i,y_i),…,(x_n,y_n).<br><br>The information about the planes and their representation can be found in <strong>tls_tree_discs.zip</strong> and <strong>tls_tree_imgs.zip</strong>.</p>
Data from: Terrestrial reproduction as an adaptation to steep terrain in African toads
How evolutionary novelties evolve is a major question in evolutionary biology. It is widely accepted that changes in environmental conditions shift the position of selective optima, and advancements in phylogenetic comparative approaches allow the rigorous testing of such correlated transitions. A longstanding question in vertebrate biology has been the evolution of terrestrial life histories in amphibians and here, by investigating African bufonids, we test whether terrestrial modes of reproduction have evolved as adaptations to particular abiotic habitat parameters. We reconstruct and date the most complete species-level molecular phylogeny and estimate ancestral states for reproductive modes. By correlating continuous habitat measurements from remote sensing data and locality records with life-history transitions, we discover that terrestrial modes of reproduction, including viviparity evolved multiple times in this group, most often directly from fully aquatic modes. Terrestrial modes of reproduction are strongly correlated with steep terrain and low availability of accumulated water sources. Evolutionary transitions to terrestrial modes of reproduction occurred synchronously with or after transitions in habitat, and we, therefore, interpret terrestrial breeding as an adaptation to these abiotic conditions, rather than an exaptation that facilitated the colonization of montane habitats.
Data from: Reproductive workers show queen-like gene expression in an intermediately eusocial insect, the buff-tailed bumble bee Bombus terrestris
Bumble bees represent a taxon with an intermediate level of eusociality within Hymenoptera. The clear division of reproduction between a single founding queen and the largely sterile workers is characteristic for highly eusocial species, whereas the morphological similarity between the bumble bee queen and the workers is typical for more primitively eusocial hymenopterans. Also, unlike other highly eusocial hymenopterans, division of labour among worker subcastes is plastic and not predetermined by morphology or age. We conducted a differential expression analysis based on RNA-seq data from 11 combinations of developmental stage and caste to investigate how a single genome can produce the distinct castes of queens, workers and males in the buff-tailed bumble bee Bombus terrestris. Based on expression patterns, we found males to be the most distinct of all adult castes (2411 transcripts differentially expressed compared to nonreproductive workers). However, only relatively few transcripts were differentially expressed between males and workers during development (larvae: 71 and pupae: 162). This indicates the need for more distinct expression patterns to control behaviour and physiology in adults compared to those required to create different morphologies. Among female castes, reproductive workers and their nonreproductive sisters displayed differential expression in over ten times more transcripts compared to the differential expression found between reproductive workers and their mother queen. This suggests a strong shift towards a more queenlike behaviour and physiology when a worker becomes fertile. This contrasts with eusocial species where reproductive workers are more similar to nonreproductive workers than the queen.
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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.