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88 results for “Enumeration”
Interagency Ecological Program: Integrated Dataset of Phytoplankton Enumeration Data in the San Francisco Estuary, 1992-2024
Phytoplankton community composition is an important driver of zooplankton productivity and food supply for higher trophic levels in the San Francisco Estuary. Various monitoring surveys throughout the region collect phytoplankton enumeration data dating back to the 1990s. These include surveys from the CA Department of Water Resources (CADWR), CA Department of Fish and Wildlife (CDFW), the US Bureau of Reclamation (USBR), and the US Geological Survey (USGS). These surveys collect data via various sampling and laboratory methods which are not always directly comparable. This integrated dataset includes both enumeration counts and well-documented metadata to allow for informed decision-making in the integration of these data. It also standardizes taxonomic names between groups via a key list. Note that, in this dataset, we make conservative decisions about taxonomic resolution to ensure maximum compatibility between groups. For more detailed metadata and higher taxonomic resolution, refer to individual surveys’ publications or reach out to their primary contact.
On CNF Conversion for SAT and SMT Enumeration: Benchmarks, Results and Plots
<p>Experimental results for the paper:<br><br><a title="Arxiv Link" href="https://arxiv.org/abs/2303.14971" target="_blank" rel="noopener">On CNF encoding for SAT and SMT enumeration</a>, Gabriele Masina, Giuseppe Spallitta and Roberto Sebastiani. ArXiv, 2024.</p> <p>Content:</p> <ul> <li><code>aig-bench.zip</code> <code>iscas85-bench.zip</code> <code>syn-bool-bench.zip</code> contain the inputs and results for the Boolean benchmarks. Each zip contains: <ul> <li><code>data/</code> that contains the input data</li> <li><code>results-<tool>/</code> for each tested tool.</li> </ul> </li> <li><code>syn-lra-bench.zip</code> <code>wmi-bench.zip</code> contain the inputs and results for the Boolean benchmarks. Each zip contains: <ul> <li><code>data/</code> that contains the input data</li> <li><code>results-<tool>/</code> for each tested tool.</li> </ul> </li> <li><code>plot-d4</code>, <code>plot-msat</code>, <code>plot-tabularallsat</code>, <code>plot-tabularallsmt</code> contain the plots for enumeration with different tools. </li> <li><code>plot-msat-sat</code> contains the plot for plain satisfiability using MathSAT.</li> </ul> <p>Results are stored in JSON files, where the field <code>"mode"</code> indicates the CNF transformation used to preprocess the input:</p> <ul> <li><code>LAB</code> for Tseitin CNF</li> <li><code>LABELNEG_POL</code> for Plaisted&Greenbaum CNF, using negative labels for subformulas occurring negatively only.</li> <li><code>NNF_MUTEX_POL</code> for NNF+Plaisted&Greenbaum CNF+mutex clauses, as described in the paper</li> </ul> <p>The source code used to run the experiments is available at <a href="https://doi.org/10.5281/zenodo.14033422" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.14033422</a>.</p> <p> </p>
Data and script: Reduced enumeration effort, but not coarse taxonomic resolution, is sufficient to represent beta diversity patterns of stream benthic diatoms
<p>This is a dataset on benthic diatom communities sampled in 90 riffles (the local communities) within nine near-pristine subtropical streams (each stream represented a metacommunity) in southeast subtropical Brazil. </p> <p>In addition to the dataset, we also provide the R code used to investigate whether reduced enumeration efforts (i.e., subsets of counted valves per sample) and the identification to the genus level are sufficient to recover patterns in the species composition and in beta diversity of benthic diatom metacommunities.</p>
Enumerating Cube Tilings
<p>This dataset contains 899,710,227 pairwise non-isomorphic 5-dimentional cube tilings. Each row in the canex_all.dat file stands for one of the aforementioned tilings. A C program to convert an encoded tiling to coordinates in the 5-d space (tocords.c) is also provided. Each row in the dataset has 33 elements, the last 32 of which stand for the cubes that constitute the tiling (the first element stands for the order of the automorphism group corresponding to the tiling- please refer to the journal paper for details) . The program (tocords.c) takes 32 integers (one may use the last 32 in any row from the dataset) as command line parameters and prints the cubes in coordinate form to the standard output.</p>
RMG-DB-11: Enumerating Reaction Space for Small Molecule Chemistry
<p>This repository presents approximately 750 million atom-mapped reaction SMILES. Reactions are generated by applying templates from the Reaction Mechanism Generator (RMG) database to a subset of the species from GDB11. Thus, we refer to this dataset as RMG-DB-11 i.e., the Reaction Mechanism Generator Database whose species contain up to 11 heavy atoms. All SMILES have been canonicalized by RDKit. All reactions are labeled with their corresponding RMG template.</p> <p>This data serves as a crucial starting point for quantitative predictive chemistry. Many methods that search for transition state structures require atom-mapped SMILES, which this repository provides. This data is also well-suited for unsupervised pre-training of various machine learning models.</p> <p>To parse the data with Python, start with <em>import pandas as pd</em>. Reactions with 1-8 heavy atoms can be parsed using the following code snippet: <em>pd.read_csv(<filepath>)</em>. Reactions with 9 heavy atoms can be parsed using <em>pd.read_pickle(<filepath>, compression='zip')</em>. The file names below include the word "zip" as a helpful hint to use the compression argument. Due to the large number of reactions with 10 and 11 heavy atoms, these are split into smaller chunks. First untar the file using <em>tar -xvf <tar_archive></em> to obtain several zipped pickle files that can each be parsed using the same method as with 9 heavy atoms.</p>
Sediment trap fecal pellets enumerations collected aboard CCE LTER process cruises in the California Current system, 2007, 2008 and 2016.
The collection and enumeration of sinking fecal pellets on CCE LTER Process cruises has been led by Mike Stukel since 2007. Sinking particles are collected in VERTEX-style particle interceptor traps (PIT) with an 8:1 aspect ratio, 70-mm diameter, and a baffle on top comprised of 13 smaller beveled tubes with a similar 8:1 aspect ratio. Tubes are deployed with a formalin-brine for a duration of 2-5 days. After recovery, samples are gently split on a Folsom splitter and typically 3/8 to 1/2 of two separate tubes are utilized for fecal pellet enumeration. After the cruise, samples for fecal pellet enumeration are placed in a settling chamber to allow fecal pellets to settle out. Overlying water is then strained through a 60-um filter to collect any pellets that may have remained in the water. Pellets were then placed on a gridded Petri dish and analyzed using a Zeiss Discovery stereomicroscope. Pellets were separated from other particles and photographed with a dedicated camera. Image processing was then conducted using either Image J or Image Pro to extract area and maximum feret length for each fecal pellet. Pellets were classified by shape and shape-appropriate equations were used to determine the volume of each fecal pellet. Volume was converted to mass using the equations in Stukel et al. (2013). ‘Sample’ refers to which of two samples the fecal pellet was contained within. ‘PelletID’ is the identifier for each fecal pellet in a sample. ‘Conversion Factor’ accounts for the proportion of a sample that was sorted for fecal pellets, as well as the deployment duration and cross-sectional area of the sediment trap. To determine the mass flux of fecal pellets of a certain type: 1) Sum the pellet mass for all fecal pellets of that type in a given sample and 2) multiply by the Conversion factor for that sample. ‘Shape’ is an identifier for the shape of a fecal pellet: 1) ovoid, 2) cylindrical, 3) spherical, 4) tabular, 5) amorphous, 6) ellipsoidal, 7) degraded fecal mater
Reproduction Package (VirtualBox Image) for the POPL 2024 Article `Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations`
<p>This is the artifact for the ACM PACMPL article <i>Enhanced Enumeration Techniques for Syntax-Guided Synthesis of Bit-Vector Manipulations</i>. We provide our artifact as an easy-to-use VirtualBox image, which contains the benchmarks, our tools for bit-vector synthesis, and the scripts for generating the results showcased in the paper.</p>
Questions for Galops 1-7 horse riding theoretical exams and their textual answers with enumerations
<p><strong>Description in English :</strong></p><p>(Description en Français plus bas)</p><p><strong>Questions for Galops 1-7 horse riding theoretical exams and their textual answers with enumerations</strong></p><p>This French dataset was created to develop a system of automatic correction of textual answers with enumeration for horse riding theoretical exams, as part of Marine Potier's Master's thesis, a student in NLP (Natural Language Processing) at the Université de Franche-Comté.</p><p>This dataset contains 122 questions with their respective possible answers.</p><p>These questions and their answers were manually extracted from the official books published by the French Equestrian Federation.</p><p>We used ChatGPT (v.3.5) in order to get variations of the extracted answers, and every new answer was manually verified to make sure it was correct as well.</p><p>This dataset is available in CSV format and structured according to the following fields :</p><p>- <strong>question_id</strong> : The question ID, representing the level (1 to 7) followed by the question number.</p><p>- <strong>type_question</strong> : Either "sans_ordre" or "avec_ordre". Some answers need to be written in a precise order to be considered correct.</p><p>- <strong>question</strong> : The actual question, which was extracted from the official books.</p><p>- <strong>reponse_correcte</strong> : The correct answer, which was extracted from the official books.</p><p>- <strong>variation_1 to variation_5</strong> : The variations of the correct answer which was reformulated with ChatGPT. All were manually verified afterwards.</p><p> </p><p><strong>Description en Français :</strong></p><p><strong>Questions des examens théoriques Galops 1 à 7 d'équitation et leurs réponses textuelles contenant des énumérations</strong></p><p>Ce dataset en langue française a été créé pour développer un système de correction automatique de réponses textuelles contenant une énumération pour les examens théorique d'équitation (les Galops 1 à 7). Ce projet s'inscrit dans le cadre de mémoire de recherche de Marine Potier, étudiante en Master en Traitement Automatique des Langues à l'Université de Franche-Comté.</p><p>Ce dataset est constitué de 122 questions avec leurs réponses correctes respectives.</p><p>Ces questions et leurs réponses ont été extraites manuellement des guides fédéraux officiels publiés par la Fédération Française d'Équitation.</p><p>Nous avons utilisé ChatGPT (v.3.5) afin de recueillir des variations des réponses extraites. Chaque nouvelle réponse a été vérifiée manuellement pour s'assurer de leur exactitude.</p><p>Ce dataset est disponible en format CSV, et structuré comme expliqué ci-dessous :</p><p>- <strong>question_id</strong> : L'identifiant de la question, représentant le niveau (1 à 7) suivi du numéro de question.</p><p>- <strong>type_question</strong> : Soit "sans_ordre" ou "avec_ordre". Certaines questions nécessitent d'avoir leurs réponses rédigées suivant un ordre précis pour être considérées comme correctes.</p><p>- <strong>question</strong> : La question, extraite des guides fédéraux.</p><p>- <strong>reponse_correcte</strong> : La réponse correcte, extraite des guides fédéraux.</p><p>- <strong>variation_1 à variation_5</strong> : Les variations de la réponse correcte qui a été reformulée à l'aide de ChatGPT. Toutes ont été vérifiées manuellement par la suite.</p>
CUSP - UBC Workshop: Analytics: Characterization and quantification / enumeration of particles in the environment and in tissue
<p>The CUSP-UBC Workshop was held online on 28th January 2022.</p> <p>69 participants took part from across Europe and British Columbia to share experiences, exchange knowledge, and to discuss challenges and solutions as part of a great collaboration between the two clusters.</p> <p><strong>Acknowledgements:</strong></p> <p><strong>Co-Organisation and Cluster Presentations:</strong></p> <p>Lesley Tobin (CUSP Working Group 6 Communication and Dissemination, PlasticsFatE) <a href="mailto:lesley.tobin@optimat.co.uk"> </a><a href="mailto:lesley.tobin@optimat.co.uk">lesley.tobin@optimat.co.uk</a></p> <p>Mahdi Takaffoli (Coordinator, Cluster for Microplastics, Health and the Environment, The University of British Columbia) <a href="mailto:mahdi.takaffoli@ubc.ca">mahdi.takaffoli@ubc.ca</a></p> <p><strong>Presenters:</strong></p> <p><strong>Florian Meirer </strong>(Associate Professor, Inorganic Chemistry and Catalysis research group at Utrecht University; Polyrisk & Aurora)</p> <p>“Characterizing Nanoplastics with Force Microscopy – An Update”) <a href="mailto:F.Meirer@uu.nl">F.Meirer@uu.nl</a></p> <p><strong>Anna Costa</strong> (Environmental Nanotechnology and Nano-Safety group of CNR-ISTEC; PlasticsFatE) “Strategies for MP/NP simulated samples-laboratory tests” <a href="mailto:anna.costa@istec.cnr.it">anna.costa@istec.cnr.it</a></p> <p><strong>Tao Huan</strong> (Assistant Professor, Chemistry, The University of British Columbia)</p> <p>“Pilot Study of the Impact of Microplastics on Cell Liability and Potential Application of Metabolomics in Understanding the Biological Mechanisms” <a href="mailto:thuan@chem.ubc.ca">thuan@chem.ubc.ca</a></p> <p><strong> Edward Grant</strong> (Professor, UBC Chemistry) “The challenge of representative microplastic analysis” edgrant@chem.ubc.ca</p> <p>Thank you to Michelle Epstein, Doctor of Allergy and Clinical Immunology, MedUni Vienna, for such a useful, stimulating idea, and to everyone who took part despite the unsocial hours!</p> <p> </p>
Enumeration of Williamson Sequences of Even Order
<p>These files contain an enumeration of all inequivalent Williamson sequences of even orders n < 65 as defined in the paper "A SAT+CAS Method for Enumerating Williamson Matrices of Even Order" by Bright, Kotsireas, and Ganesh. This paper has been accepted to appear at AAAI-18, the thirty-second AAAI conference on artificial intelligence.</p> <p>Every line in each file contains exactly one Williamson sequence, with spaces separating the members A, B, C, and D of the Williamson sequences. The sequence entries are encoded using the characters + for 1 and - for −1.</p>
Enumeration of Williamson sequences divisible by 2 or 3
<p>These files contain an enumeration of all inequivalent Williamson sequences of orders divisible by 2 or 3 and up to 70 as defined in the paper "<a href="https://cs.uwaterloo.ca/~cbright/reports/jsc-willsat.pdf">Applying Computer Algebra Systems with SAT Solvers to the Williamson Conjecture</a>" by <a href="https://cs.uwaterloo.ca/~cbright/">Bright</a>, <a href="http://web.wlu.ca/science/physcomp/ikotsireas/">Kotsireas</a>, and <a href="https://ece.uwaterloo.ca/~vganesh/">Ganesh</a>.</p> <p>Every line in each file contains exactly one Williamson sequence, with spaces separating the members A, B, C, and D of the Williamson sequences. The sequence entries are encoded using the characters + for 1 and - for −1.</p> <p>A <a href="https://uwaterloo.ca/mathcheck/download/williamson-sequences">table with the sequence counts</a> in each file is also available.</p>
SSH Username Enumeration Attack Detection Dataset
<p>The dataset is collected from a closed-environment network using network monitoring tools installed in the data collection point. The dataset generation was achieved through the use of common vulnerabilities and exposures (CVE) with the identification number CVE-2018-15473 retrieved from the public exploits database and pcap file of normal traffic obtained from public training repository. A total of 36,273 instances were collected with two classes <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>”. We chose the terms <em>“username enumeration attack”</em> and “<em>non-username enumeration</em>” instead of the traditional <em>“attack”</em> and <em>“normal”</em> label notations since <em>“</em>normal<em>”</em> traffic data could contain attacks other than username enumeration attack.</p> <p>The username enumeration attack corresponds to the attack traffic while non-username enumeration traffic corresponds to the normal traffic. This traffic reflects different services including emails, DNS, HTTP, web, few to mention. Several data preprocessing techniques were carried out including categorical encoding. Both label encoding and one hot encoding techniques were used to transform categorical feature values into numerical feature values. Hence, two types of datasets were generated. </p>
Supplementary Files - SMILES strings for DOSEDO enumerated library
<p>Supplementary Files for Nature Communications publication titled "Diversity-oriented synthesis encoded by deoxyoligonucleotides". Two files contain the Iodo-library and Bromo-library SMILES strings.</p>
McMurdo Dry Valleys Bacterial Enumeration
An important part of the McMurdo Long Term Ecological Research (LTER) project is monitoring spatial and temporal patterns, and processes that control bacterial production in perennial ice covered lakes. This data set quantifies bacteria concentrations at specific depths in Dry Valley lakes.
Enumeration of Circulant Best Matrices
<p>These files contain an enumeration of all inequivalent defining rows of circulant best matrices up to order 57. The row entries are encoded using the characters '+' for 1 and '-' for −1.</p> <p>A <a href="https://uwaterloo.ca/mathcheck/download/best-matrices">table with the sequence counts</a> in each file is also available.</p>
Fig. 2A in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 2A. Asian Common Toad Duttaphrynus melanostictus.
Fig. 1 in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 1. Map of the study area showing terrestrial and aquatic habitats.
Fig. 2O in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 2O. Checkered Keelback Xenochrophis piscator.
Fig. 2K in Enumeration of Herpetofaunal assemblage of Surajpur Wetland, National Capital Region (India)
Fig. 2K. Bengal Monitor Varanus bengalensis.
Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem
<div><strong>Description. </strong>This is the data used in the experiments presented in the following paper:<br> <div> <ul> <li> <div> <div>N. Arınık, R. Figueiredo, and V. Labatut, “Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem,” <em>Journal of Global Optimization</em>, vol. 86, pp. 355–391, 2023. DOI: <a href="http://doi.org/10.1007/s10898-023-01270-3">10.1007/s10898-023-01270-3</a> ⟨<a href="https://hal.science/hal-03935831">hal-03935831</a>⟩</div> </div> </li> </ul> <p><strong>Source code. </strong>The related source code is available on GitHub: </p> <ul> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/%3C/i%3E%3Ci%3E%3Ci%3ECompNet%3C/i%3E/Sosocc%3C/i%3E">https://github.com/CompNet/Sosocc</a></li> <li><a href="https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/%3Ci%3Ehttps://github.com/CompNet/EnumCC%3C/i%3E">https://github.com/CompNet/EnumCC</a></li> </ul> <div><strong>Citation. </strong>If you use these data, please cite the above reference:</div> <div><br><code>@Article{Arinik2021,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Efficient Enumeration of the Optimal Solutions to the Correlation Clustering problem},</code><br><code> journal = {Journal of Global Optimization},</code><br><code> year = {2023},</code><br><code> volume = {86},</code><br><code> pages = {355-391},</code><br><code> doi = {10.1007/s10898-023-01270-3},</code><br><code>}</code></div> <div> </div> <div><strong>Funding. </strong>This research benefited from the support of Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</div> <div> </div> <div><strong>Further details. </strong>We describe below the structure of the zip file `article_materials.zip`:</div> <div> <ul> <li>Experiments for Dataset 1 <ul> <li>delay_exec_time: all the results and plots regarding the difference of execution times between EnumCC(3) and OneTreeCC() (i.e., EnumCC(3) minus OneTreeCC()), represented on the log-scaled y-axis of the plots. When such difference takes a negative value, this means our proposed method EnumCC(3) runs faster than OneTreeCC().</li> <li>EnumCC_nb-jumps: all the results regarding the number of jumps related to EnumCC(3), i.e. n<sub><em>jump</em></sub>(EnumCC(3))</li> <li>exec_time: all the results regarding the execution times of EnumCC(3) and OneTreeCC().</li> <li>nb-sols: all the results regarding the number of optimals solutions based on EnumCC(3). Note that we show the results of OneTreeCC() only for those with n=50, since both methods run out of the time limit of 12h for several networks with n=50.</li> </ul> </li> <ul> <li>networks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. For complete unweighted signed networks, this model relies on only three parameters: n (number of vertices), l<sub>0</sub> (initial number of modules) and <em>q<sub>m</sub></em> (proportion of misplaced edges, i.e. edges meant to be frustrated by construction). Moreover, we make the assumption that the proportion of misplaced edges is the same inside and between the modules. When it comes to incomplete unweighted signed networks, we introduce two more parameters, which are the density d of the graph and the proportion q neg of the negative edges. The last parameter q<sub>neg</sub> allows to control the ratio of positive to negative edges. For complete unweighted signed networks with d = 1, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40, 45, 50} and <em>q<sub>m</sub></em> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6}. In these networks, the value of <em>q<sub>neg</sub> </em>with the considered parameters is approximately equal to 0.7. For incomplete unweighted signed networks with d <em>∈</em> {0.25, 0.50}, we generate 20 replications for parameter values l<sub>0</sub> = 3, n <em>∈</em> {32, 36, 40}, q<sub>m</sub> <em>∈</em> {0.1, 0.2, 0.3, 0.4, 0.5, 0.6} and <em>q<sub>neg</sub> </em><em>∈</em> {0.3, 0.5, 0.7}. In total, we produce 600 and 1,080 instances for complete and incomplete networks, respectively, which makes a total of 1,680 instances.</li> <li>partitions: folder containing the partitioning results of two methods: EnumCC(3) vs. OneTreeCC(). Note that the results of OneTreeCC() are not shown for space considerations, except for those with n=50.</li> <li>Results</li> </ul> <li>Experiments for Dataset 2</li> <ul> <li>benchmark netwoks: We generate these complete and incomplete networks through our random signed network generator, which is publicly available online. The optimal solution for a generated network is known by construction. For a given n, d and l<sub>0</sub>, we first create a perfectly structurally balanced (i.e., internally positive and externally negative) signed network with a built-in module structure. The underlying module structure constitutes the optimal partition. Then, in order to take into account different positive to negative ratio values for internal and external edges we generate several signed networks by perturbing the initial signed network without affecting its underlying optimal partition, thanks to its definition of stability range. We generate signed networks with parameter values n <em>∈</em> {30, 40, 50, 60, 70, 90}, d <em>∈</em> {0.25, 1.00} and l<sub>0</sub> <em>∈</em> {2, 4, 6}. In total, we produce 214 and 184 instances for complete and incomplete networks, respectively, which makes a total of 398 instances.</li> <li>benchmark partitions: folder containing the partitioning results of two methods: CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where <em>r<sub>max</sub></em> ∈ {3,4}.</li> <li>results: two `csv` files containing benchmark results between CoNS(<em>r<sub>max</sub></em>) with vs. without MVMO pruning, where r<sub>max</sub> ∈ {3,4}.</li> </ul> </ul> </div> </div> </div>
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.