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116 results for “Bottlenecks”
Accompanying material to the Inventory of opportunities and bottlenecks in policy to facilitate the adoption of soil-improving techniques
<p>Inventory of policies at EU and country level for the inventory and analysis of bottlenecks and opportunities in sectoral and environmental policies to facilitate the adoption of Soil-Improving Cropping Systems (SICS).</p>
MCR LTER: Coral Reef: Early life stage bottleneck determines rates of coral recovery following severe disturbance; Data for Speare et al., 2024, Ecology
The data included in this data package were collected on the north shore of Moorea, French Polynesia, from 2011-2018 to evaluate drivers of different recovery rates of corals at two depths (10m and 17m). Data on juvenile coral densities, growth, and mortality, were collected from annual time series photoquadrats. Data from two experiments on coral settlement tiles were used to evaluate how exclusion of fishes influences the density of coral recruits, and the survival of coral recruits at 10 and 17m. These data were used for analyses in the manuscript entitled "Early life stage bottleneck determines rates of coral recovery following severe disturbance". These data are in support of a publication Speare et al. (2024) Ecology. This material uses data collected by the U.S. National Science Foundation's (NSF) Moorea Coral Reef Long Term Ecological Research (MCR LTER) site under Grant No. OCE 2224354 (and earlier awards). Additional financial support to the MCR LTER site was provided through a generous gift from the Gordon and Betty Moore Foundation. Research was completed under permits issued by the French Polynesian Government (Délégation à la Recherche) and the Haut-commissariat de la République en Polynésie Francaise (DTRT) (Protocole d'Accueil 2005-2024).
Supplementary material for "Palaeo-demographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia"
<p>This Supplementary material encompasses the archaeological radiocarbon dates for the Iberian Peninsula analyzed in the article “<em>Palaeo-demographic modelling supports a population bottleneck during the Pleistocene-Holocene transition in Iberia</em>". Specifically:</p> <ul> <li>‘<strong>PALEODEM_dataset.csv</strong>’ provides the dataset in comma-separated format. Contains the compiled raw data of each radiocarbon date used by every analytical procedure.</li> <li>‘<strong>PALEODEM_sites.xlsx</strong>’ is a spreadsheet containing the basic information of each archaeological site analyzed in this work. The columns include ID_site, name of Archaeological site, n<sup>o</sup> of <sup>14</sup>C dates, n<sup>o</sup> of assemblages, Regional Unit of correspondence, and bibliographical Reference.</li> <li>‘<strong>REFERENCES for PALEODEM_sites.docx</strong>’ is a text archive that collects the bibliographical references listed in the above mentioned ‘Reference’ column, that have been the main sources of archaeological information.</li> </ul>
IRIS preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Prprocessed data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of observations performed by IRIS, a NASA small explorer mission developed and operated by LMSAL with mission operations executed at NASA Ames Research Center and major contributions to downlink communications funded by ESA and the Norwegian Space Centre.</p> <p>Multiple Time Series of IRIS level 2 data are available <a href="https://iris.lmsal.com/search/">here</a></p> <p>The selected data was labeled using these definitions:</p> <p>QS: Quiet Sun<br> AR: Active Regions of the Sun<br> FL: Flare</p> <p>A time series is labeled QS when every single time step refer to a quiet sun activity.<br> When a given time series is partially composed of flaring events, the global time series is labeled as FL.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=240 representing a range between 2793.8401Å and 2806.02Å).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :</p> <p>Type: .npz<br> Size: 11.89GB</p> <p>*** Key: 'data_TR_QS'<br> ndarray data of length 2467<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR_AR'<br> ndarray data of length 1042<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR_FL'<br> ndarray data of length 1055<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_QS'<br> ndarray data of length 325<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_AR'<br> ndarray data of length 1042<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL_FL'<br> ndarray data of length 714<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_QS'<br> ndarray data of length 1428<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_AR'<br> ndarray data of length 792<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TE_FL'<br> ndarray data of length 356<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_TR'<br> ndarray data of length 4564<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'data_VAL'<br> ndarray data of length 2081<br> containing np.ndarray of shapes ['various', 240]</p> <p><br> *** Key: 'data_TE'<br> ndarray data of length 2576<br> containing np.ndarray of shapes ['various', 240]</p> <p> </p> <p>*** Key: 'position_TR_QS'<br> ndarray data of length 2467<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR_AR'<br> ndarray data of length 1042<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR_FL'<br> ndarray data of length 1055<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_QS'<br> ndarray data of length 325<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_AR'<br> ndarray data of length 1042<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL_FL'<br> ndarray data of length 714<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_QS'<br> ndarray data of length 1428<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_AR'<br> ndarray data of length 792<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE_FL'<br> ndarray data of length 356<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TR'<br> ndarray data of length 4564<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_VAL'<br> ndarray data of length 2081<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p> <p> </p> <p>*** Key: 'position_TE'<br> ndarray data of length 2576<br> containing ndarray data of length 4<br> containing mix of types {'ndarray', 'int', 'str'}</p>
No severe genetic bottleneck in a rapidly range-expanding bumblebee pollinator
<p>Genetic bottlenecking can limit the success of populations colonising new ranges. However, successful colonisations can occur despite bottlenecking, a phenomenon known as the genetic paradox of invasion. Eusocial Hymenoptera such as bumblebees (<em>Bombus</em> spp.) should be particularly vulnerable to genetic bottlenecking, since homozygosity at the sex-determining locus leads to costly diploid male production. The Tree Bumblebee (<em>B. hypnorum</em>) has rapidly colonised the UK since 2001 and has been highlighted as exemplifying the genetic paradox of invasion. Using microsatellite genotyping, combined with the first genetic estimates of diploid male production in UK B. hypnorum, we tested two alternative genetic hypotheses ('bottleneck' and 'gene flow' hypotheses) for B. hypnorum's colonisation of the UK. We found that the UK population has not undergone a recent severe genetic bottleneck and exhibits levels of genetic diversity falling between those of widespread and range-restricted<em> Bombus</em> species. Diploid males occurred in 15.4% of reared colonies, leading to an estimate of 21.5 alleles at the sex-determining locus. Overall, the findings show that this population is not bottlenecked, instead suggesting that it is experiencing continued gene flow from the continental European source population with only moderate loss of genetic diversity, and does not exemplify the genetic paradox of invasion.</p>
Fig. 2 in Selectivity of fish ladders: a bottleneck in Neotropical fish movement
Fig. 2. Catch per unit effort of fish (CPUE; individuals per 100 casts) along the fish ladder at Lajeado Dam (Numbers in parentheses = total number of species; gray light portions of columns indicate the proportion of Rhaphiodon vulpinus; arrow indicates upward movement).
Fig. 4 in Selectivity of fish ladders: a bottleneck in Neotropical fish movement
Fig. 4. Monthly mean water levels downriver (line) and the number of hours with elevations less than 175.1 m (columns) (a), the number and abundance of fish species in samples downstream from the dam (b and d) and from the fish ladder (c and e) of Lajeado Dam.
Fig. 3 in Selectivity of fish ladders: a bottleneck in Neotropical fish movement
Fig. 3. Monthly occurrence (a) and catch per unit effort (CPUE; individuals per 100 casts) (b) of the four most abundant species in the fish ladder at Lajeado Dam (arrow indicates upward movement).
PB preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed PB data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=325 representing 325 traffic detectors ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">C</a><a href="https://pems.dot.ca.gov/">alTrans</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 114.23MB<br> *** Key: 'data_TR_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE_PB'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 3<br> containing np.ndarray of shapes [12160, 325]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 3<br> containing np.ndarray of shapes [868, 325]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 3<br> containing np.ndarray of shapes [4343, 325]</p> <p>*** Key: 'position_TR_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_PB'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 3<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
AL preprocessed data used in paper "Multi variables time series information bottleneck"
<p>Preprocessed AL data used in paper "Multi variables time series information bottleneck" with the <a href="https://github.com/DenisUllmann/IB-MTS">GitHub</a> code</p> <p>This dataset is created from a public available dataset of solar power data collected in Alabama by <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>.</p> <p>The npz file is a numpy (np) compressed data and can be loaded using np.load with allow_pickle=True<br> Loaded data is then a python dict described bellow.</p> <p>Each sample 'data' is a np.ndarray with 2 dimensions: time (various length) and wavelength (length=137 representing 137 solar plants ordered like in <a href="https://www.nrel.gov/grid/solar-power-data.html">NREL</a>).</p> <p>Each sample is given a 'position' which is a list of length 4:<br> position[1] is a string that gives the name of the event<br> position[4] is a boolean vector that gives the time positionsof the corresponding sample in the original sequence of public IRIS level2 data</p> <p>Data file info :<br> Type: .npz<br> Size: 34.48MB<br> *** Key: 'data_TR_AL'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL_AL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE_AL'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TR'<br> ndarray data of length 161<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_VAL'<br> ndarray data of length 11<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'data_TE'<br> ndarray data of length 57<br> containing np.ndarray of shapes ['various', 137]</p> <p>*** Key: 'position_TR_AL'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL_AL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE_AL'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TR'<br> ndarray data of length 161<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_VAL'<br> ndarray data of length 11<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p> <p>*** Key: 'position_TE'<br> ndarray data of length 57<br> containing ndarray data of length 4<br> containing mix of types {'str', 'ndarray', 'int'}</p>
Population demographic history and evolutionary rescue: Influence of a bottleneck event
<p class="p1">Rapid environmental change presents a significant challenge to the persistence of natural populations. Rapid adaptation that increases population growth, enabling populations that declined following severe environmental change to grow and avoid extinction, is called evolutionary rescue. Numerous studies have shown that evolutionary rescue can indeed prevent extinction. Here, we extend those results by considering the demographic history of populations. To evaluate how demographic history influences evolutionary rescue, we created 80 populations of red flour beetle, <em>Tribolium castaneum</em>, with three classes of demographic history: diverse populations that did not experience a bottleneck, and populations that experienced either an intermediate or a strong bottleneck. We subjected these populations to a new and challenging environment for six discrete generations and tracked extinction and population size. Populations that did not experience a bottleneck in their demographic history avoided extinction entirely, while more than 20% of populations that experienced an intermediate or strong bottleneck went extinct. Similarly, among the extant populations at the end of the experiment, adaptation increased the growth rate in the novel environment the most for populations that had not experienced a bottleneck in their history. Taken together, these results highlight the importance of considering the demographic history of populations to make useful and effective conservation decisions and management strategies for populations experiencing environmental change that pushes them toward extinction.</p>
No severe genetic bottleneck in a rapidly range-expanding bumblebee pollinator
Open the record for dataset details and reuse information.
Population demographic history and evolutionary rescue: Influence of a bottleneck event
Open the record for dataset details and reuse information.
Caught in a bottleneck: habitat loss for woolly mammoths in central North America and the ice-free corridor during the last deglaciation
<p>The dataset is to describe the habitat structure and bioenergetic characteristics of woolly mammoths (<i><span>Mammuthus primigenius</span></i>) in North America during the last deglaciation between 15 and 10 ka. The habitat structure includes fractional woody cover (FWC) and net primary productivity (NPP) for 20 plant functional types (PFTs). NPP is based on the dynamic vegetation model LPJ-GUESS (LPJG). FWC is based on LPJ-GUESS and fossil pollen records in the Neotoma Paleoecology Database. The bioenergetic characteristics of woolly mammoths are the results of Niche Mapper, including metabolic rate, forage consumption, freshwater consumption, and other bioenergetic traits of individual woolly mammoths. These data were conducted at a temporal resolution of 100-year means of climate simulations every 1,000 years, from 15 to 10 ka. Simulations were run for all of North America except Alaska (10° – 80°N, 140° – 45°W) at a spatial resolution of 0.5°x0.5° grid latitude and longitude.</p>
Simulation data used in "An attempt to distinguish physical and socio-psychological influences on pedestrian bottleneck"
<p>The dataset contains all simulated trajectory data used in the main text analysis of "Self-organisation phenomena in pedestrian bottleneck flow under varying corridor width". The data was simulated using JuPedSim. The source code used can be downloaded from: https://github.com/JonasRzez/jpsnewnoise.git</p>
Data from: Bottlenecks and inbreeding depression in autotetraploids
Inbreeding depression is dependent on the ploidy of populations and can inhibit the evolution of selfing. While polyploids should generally harbor less inbreeding depression than diploids at equilibrium, it has been unclear whether this pattern holds in non-equilibrium conditions following bottlenecks. We use stochastic individual based simulations to determine the effects of population bottlenecks on inbreeding depression in diploids and autotetraploids, in addition to cases where neo-autotetraploids form from the union of unreduced gametes. With a ploidy-independent dominance function based on enzyme kinetics, inbreeding depression is generally lower in autotetraploids for fully and partially recessive mutations. Due to the sampling of more chromosomes during reproduction, bottlenecks generally reduce inbreeding depression to a lesser extent in autotetraploids. All else being equal, population bottlenecks may have ploidy dependent effects for another reason – in some cases mating between close relatives temporarily increases inbreeding depression in autotetraploids by increasing the frequency of the heterozygous genotype harboring the most harmful mutations. When neo-autotetraploids are formed by few individuals, inbreeding depression is dramatically reduced, given extensive masking of harmful mutations following whole genome duplication. This effect persists as nascent tetraploids reach mutation-selection-drift balance, providing a transient period of permissive conditions favoring the evolution of selfing.
Data from: Genetic diagnosis of a rare myrmecochorous species, Plagiorhegma dubium (Berberidaceae): historical genetic bottlenecks and strong spatial structures among populations
Distribution of genetic variation over time and space is relevant to demographic histories, and tightly linked to ecological disturbances as well as evolutionary potential of an organism. Therefore, understanding the pattern of genetic diversity is a primary step in conservation and management projects for rare and threatened plant species. We used 8 microsatellite markers to examine the level of genetic diversity, spatial structure and demographic history of Plagiorhegma dubium, a rare myrmecochorous herb, populations sampled across northeast Asia and Siberia. We found low within-population genetic variation associated with historical bottlenecks. Although pairwise FST values were not much higher than the ones found in similar life form species, STRUCTURE and PCoA revealed a clear broad-scale spatial pattern of genetic structure. Bayesian clustering (best K=6) and PCoA identified three populations that are distinctive from neighboring populations in the Korean peninsula, which suggests potential units for conservation and management plans in Korea. MIGRATE-N and BAYESASS showed that both contemporary (0.003-0.045) and historical migration rates (2e-5-4.6e-4) were low. Our findings provide a good example, where genetic considerations should be integrated for conservation and management plans of rare and threatened species.
Data from: Genomic signatures of population bottleneck and recovery in Northwest Atlantic pinnipeds
Population increases over the past several decades provide natural settings in which to study the evolutionary processes that occur during bottleneck, growth, and spatial expansion. We used parallel natural experiments of historical decline and subsequent recovery in two sympatric pinniped species in the Northwest Atlantic, the gray seal (Halichoerus grypus atlantica) and harbor seal (Phoca vitulina vitulina), to study the impact of recent demographic change in genomic diversity. Using restriction site‐associated DNA sequencing, we assessed genomic diversity at over 8,700 polymorphic gray seal loci and 3,700 polymorphic harbor seal loci in samples from multiple cohorts collected throughout recovery over the past half‐century. Despite significant differences in the degree of genetic diversity assessed in the two species, we found signatures of historical bottlenecks in the contemporary genomes of both gray and harbor seals. We evaluated temporal trends in diversity across cohorts, as well as compared samples from sites at both the center and edge of a recent gray seal range expansion, but found no significant change in genomewide diversity following recovery. We did, however, find that the variance and degree of allele frequency change measured over the past several decades were significantly different from neutral expectations of drift under population growth. These two cases of well‐described demographic history provide opportunities for critical evaluation of current approaches to simulating and understanding the genetic effects of historical demographic change in natural populations.
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 1)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from Switchboard. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair information was the ground truth from Switchboard. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
Pairwise Learning using Unsupervised Bottleneck Features for Zero-Resource Speech Challenge 2017 (System 3)
<p>The system is for track1 alone. We trained an antoencoder using unsupervised bottleneck features with word-pair information from unsupervised term detection (UTD) on all corpora of five languages. The unsupervised bottleneck features was extracted from an extractor of multi-task learning deep neural networks (MTL-DNN). The word-pair was found by UTD. The UTD process was built on ZRTools. The final features are obtained from the third layer in our pairwise trained autoencoder.</p>
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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.