Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
24
datasets available to search
ShareScore release 0.9.0
Dataset results
24 results for “landslide inventory”
Storm Alex Landslide Inventory
<p>The storm Alex that in 2020 hit the Mediterranean Alps represented and extreme meteorological event triggering devastating floods and landslides in both Italy and France, with severe consequences for people and anthropic settlements. After the Storm Alex, a detailed inventory of rainfall-induced hillslope instability processes was prepared by means of the visual interpretation of VHR satellite imagery in two adjacent mountain catchments of the Liguria Region (northern Italy) impacted by intense rainfall. The inventory map included a total of 302 features classified in debris slide (214), debris slides/debris flow (79) and channelized flow erosion (9). </p>
Inventory of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) in May 2023
<p>The dataset contains 49103 landslides, that were manually mapped by visual inspection of pre- and post-event satellite images in an area of 8981 km2. Such images are acquired by PlanetScope satellites (<a href="https://www.planet.com/">https://www.planet.com/</a>) and are provided under an academic license; 3-m resolution multiband tiles are used.</p> <p>Pre-event imagery refers to the Monthly Global Basemap products provided by Planet, the April 2023 Basemap was used. Post-event images were acquired between 22 May and beginning of June 2023. The cloud-free image closer to the event was used and multi-temporal frames were checked in selected areas (e.g., due to the presence of shadows or unclear images). Images are accessed through the Planet QGIS Plugin.</p> <p>This dataset supersedes version 1, since it represents its update; major changes include:</p> <ul> <li>mapping over a wider area (8981 vs 5764 km2);</li> <li>check on the landslides mapped in version 1 located on flat slopes (lower than 5°); removal of polygons associated with river erosion and not due to gravity movements</li> </ul> <p> </p> <p>NOTES ON VERSION 1</p> <p>landslides were manually mapped at a scale of 1:5.000 by a single operator in a time interval of 5 weeks following the rainfall event; the inventory (version 1.0) was completed on 28 June 2023. Please note that data did not undergo any kind of validation.</p> <p>Data are provided in shapefile format (coordinate system WGS84 UTM 32N) and in kml format.</p> <p>The main dataset is the “Emilia landslides” shp/kml file; the “area” shapefile refers to the investigated area; the “riverbank and agricultural fields” files include polygons that were mapped but refer either to river courses having high discharge in the post-event images, or to color changes probably due to farming activities or the evolution of agricultural fields. The “riverbank and agricultural fields” elements should not refer to slope movements, and usage of these data is not recommended, unless a validation is made.</p>
Semi-automatic and manual shallow landslide inventories of two extreme rainfall events.
<p>This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth.</p><p>With more details, it is possible to find:</p><ul><li>AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy.</li><li>The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016</li><li>The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016</li><li>AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy.</li><li>AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy.</li><li>The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area)</li><li>The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area)</li><li>The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019</li></ul><p>GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. </p><p>The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: </p><p><strong>Script 1. </strong>NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: <a href="https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true">https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true</a></p><p><strong>Script 2.</strong> sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: <a href="https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true">https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true</a></p><p><strong>Script 3. </strong>Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: <a href="https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true">https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true</a></p><p><strong>Script 4.</strong> Multi-temporal-averaged NDVIvar Link to GEE Script: <a href="https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true">https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true</a> for the whole Gavi study area (2019 flood) and <a href="https://code.earthengine.google.com/89e1c0a1361860cd407b7e6ab8bb95de?noload=true">https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true</a> for the entire Arroscia-Tanarello study area</p><p>The full description of the methodology can be found in the paper of Notti et al., 2023</p><p>Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, <a href="https://doi.org/10.5194/nhess-23-2625-2023">https://doi.org/10.5194/nhess-23-2625-2023</a>, 2023</p>
Landslide inventory (1953-1996), Andrews Experimental Forest and Blue River Basin.
Landslide inventory (1953-1996), Andrews Experimental Forest and Blue River Basin. This layer is a combination of data from four landslide inventory efforts that have been conducted in the area beginning in the late 1960's. Data represent landslide occurrences between 1953 and 1996. Data are in the UTM coordinate system; zone 10, NAD27. The data table that describes the characteristics of the landslides inventoried in the following: Individual layers tied to inventories by Ted Dyrness, Fred Swanson, Dan Marion, and Matt Wallenstein were appended and linked to the shape file. This file is associated with the slideinv layer, which is a combination of data from four landslide inventory efforts that have been conducted in the area beginning in the late 1960's. Points have been screen digitized from field maps using streams, roads, contour lines, and harvest units as reference layers.
Multi-temporal Landslide Inventory for the Far-Western region of Nepal
<p>The Multi-Temporal Landslide Inventory for the Far-Western region of Nepal datasets comprises 26350 different landslide events digitize in form of polygons from Google Earth satellite imagery interpretation. In Google earth has been used for interpretation 93 different sources for 79 different time slices between 2002 and 2018. The maximum scale of interpretation used is 1:1000, meanwhile the scale of digitalization was constant between 1:800 and 1:2000, resulting in a final visualization scale of 1:1000. All landslides in the inventory have been classified between deep-seated and shallow types (attribute field "Depth") by visual interpretation which have been later corroborated with calculations of the elevation differences within the surface of rupture area of the landslides</p> <p>The dataset comprises 4 different shapefiles:</p> <ul> <li><strong>"LandslideInventory_FarWesternNepal_Pol.shp"</strong>: Shapefile with 26350 Polygon features that bound completely the “zone of depletion” and partially the “zone of accumulation” of each identified landslide. Including completely the surface of rupture and more or less partially the depositional zone of the landslides. Landslide</li> <li><strong>"LandslideInventory_FarWesternNepal_Points.shp"</strong>: Shapefile with 25639 Point features that approximately correspond with the center of the surface of rupture area, the point location within each landslide has ben extracted automatically with GIS tools using ALOS PALSAR (12.5 m) DEM. </li> <li><strong>"LandslideInventory_FarWesternNepal_Points_Dated1992_2018.shp"</strong>: Shapefile with 8778 Point features for landslides in the inventory that have been dated within the period 1992-2018 (attribute field "Year". The dating of the landslides has been perform automatically by an own new toolbox in ArcGIS that compare annual Landsat (4-5, 7 and 8), to find sudden vegetation changes within the areas of the digitized landsldies. The tool has an accuracy of 83% to detect annual dates of activation or reactivations of the inventoried landslides. </li> <li><strong>"LandslideInventory_FarWesternNepal_AOI.shp"</strong>: Shapefile with the Polygon boundary of the landslide inventory Area of Interpretation.</li> </ul> <p>All shapefiles are in a UTM projected coordinate system UTM44N (WGS84).</p> <p> </p> <p>This research was funded by the UK Natural Environment Research Council (NERC) and Department for International Development (DFID) as project NE/P000452/1 (LandslideEVO) under the Science for Humanitarian Emergencies and Resilience (SHEAR) program.</p> <p> </p>
Rainfall-Induced Landslide Inventory of the 2014 Itaóca Event, Ribeira Valley, Brazil
<p>Heavy precipitation on January 12, 2014, triggered numerous shallow landslides in Itaóca, Ribeira Valley, Brazil. The inventory was manually created using a RapidEye satellite image (5 m spatial resolution) acquired on January 30, 2014, 18 days after the event. Specific criteria were applied to identify shallow landslides through image interpretation. These criteria included the absence of vegetation, proximity to the drainage network, altimetric variation, planar rupture surfaces, slope position, as well as the shape and size of the features. The dataset is provided in shapefile format and contains 1,723 polygons representing the areas affected by shallow landslides.</p> <p> </p>
Rapid mapping inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023
<p>The dataset contains the spatial explicit inventories of landslides triggered by heavy rainfall in the Emilia-Romagna region (Italy) during May 2023. Hybrid pixel-based and object-based image analysis approach was used to generate the rapid mapping products. The fully automated supervised procedure relies on change detection analysis, based on quantitative variation of vegetation cover in pre-event and post-event remote sensing imagery. Two separate inventories were generated using:<br>- Copernicus Sentinel-2 MSI satellite imagery<br>- PlanetScope SuperDove satellite imagery</p> <p>The dataset is a spatial representation vector polygons representing potential landslides, distributed in GeoPackage format, complemented with confidence measure. It contains modified Copernicus Sentinel data, available at no cost from Copernicus Open Access Hub. Use of PlanetScope images is under license: ©Planet Labs PBC, CC BY-NC-SA 2.0.</p>
Patagonian Andes landslides inventory
<p>We present the dataset developed in the research "Patagonian Andes landslides inventory: The deep learning's way to their automatic detection", submitted to the journal Remote Sensing (<a href="https://doi.org/10.3390/rs14184622">https://doi.org/10.3390/rs14184622</a>).</p> <p>“Landslide_database” folder: contains two ESRI shapefile-type vector files, the first (Ground_Truth_database), corresponds to the manually outlined landslides for training the deep learning model. The file contains the Sentinel 2 tile number, area, and perimeter. The second (Ground_Truth_database_centroid) corresponds to the centroid of the outlined landslides, in addition to the previously mentioned fields, it contains the X and Y coordinates.</p> <p>“Model results” folder: contains an ESRI shapefile vector file (Pred_T18GYS), corresponding to the landslides detected and segmented by the deep learning algorithm in the Sentinel-2 test tile.</p> <p>“Model validation” folder: contains multiple ESRI shapefile vector files used during model validation. Study area (Study_Area), roads (Roads) and populated areas (Localities). Contains the predicted landslides in the study area (Predict_T18GYS_SA), the randomly selected predicted landslides (Predict_T18GYS_Random) and their geometries (Predict_T18GYS_Geometry). The folder also contains manually delineated landslides (Groud_Truth_T18GYS) constrained to the extent of the assessed mosaic and delineated landslides that spatially match the predicted landslides (Ground_Truth_Random). Finally, true positives (TP_T18GYS), false positives (FP_T18GYS), and false negatives (FN_T18GYS) are provided separately.</p>
Shallow landslide inventory for 2000-2019 (eastern DRC, Rwanda, Burundi)
<p>This shapefile contains 7944 shallow landslide instances for the North Tanganyika-Kivu Rift Region. These landslides were identified in © Google Earth. This inventory was also used in the paper "Interactions between deforestation, landscape rejuvenation, and shallow landslides in the North Tanganyika–Kivu rift region, Africa" published in Earth Surface Dynamics (https://doi.org/10.5194/esurf-9-445-2021). When using this dataset, you can refer to this work. This research was funded by the Belgian Science Policy Office (BELSPO) through the PAStECA project (BR/165/A3/PASTECA) entitled `Historical Aerial Photographs and Archives to Assess Environmental Changes in Central Africa' (http://pasteca.africamuseum.be/).</p>
Inventories of landslides triggered by the 2019 Cotabato - Davao del Sur (Philippines) seismic sequence
<p>These files are related to the paper “Environmental effects following a seismic sequence: the 2019 Cotabato - Davao del Sur (Philippines) earthquakes” by Ferrario M. F., Perez J., Livio F., Rimando J., Michetti A. M., currently under review.</p> <p>Files include a multi-temporal inventory of landslides triggered during a seismic sequence. In October – December 2019, the Cotabato and Davao del Sur Provinces (Philippine) were hit by four Mw > 6.0 earthquakes. The sequence started with a Mw 6.4 on 16 October (EQ1), then three earthquakes with Mw of 6.6, 6.5 and 6.8 occurred on 29 October (EQ2), 31 October (EQ3) and 15 December 2019 (EQ4).</p> <p>Landslides were manually mapped on 3-m resolution PlanetScope images. Shapefiles are in WGS84 UTM Zone 51n and include:</p> <ul> <li>Area study: shapefile of the study area (inventories 2 and 3)</li> <li>Area study_October 2019: shapefile of the study area for inventory 1</li> <li>Landslides_ott2019: polygonal inventory of landslides mapped on images acquired on 24 and 28/10/2019, that is after EQ1</li> <li>Landslides_nov2019: polygonal inventory of landslides mapped on images acquired on 15 and 16/11/2019, that is after EQ3</li> <li>Landslides_dic2019: polygonal inventory of landslides mapped on images acquired on 05/02/2020, that is after EQ4</li> </ul>
Multi-temporal landslide inventory for southern Sikkim State, India
<p>The <em>Multi-temporal landslide inventory for southern Sikkim State, India</em>, is based on two data-sources (mapped extent given in <strong>Shapefile A</strong>): Google Earth images (<strong>Shapefiles B–C</strong>) and stereoscopic Cartosat-1 satellite images (<strong>Shapefiles D–F</strong>). The landslide inventories were collected for the purpose of mapping landslide domains (regions with similar physical and environmental characteristics that specifically drive landslide style) and the data was used to give a general idea of landslides occurring in the region rather than a detailed overview. The landslide inventories are given as shapefiles with two sources of data described separately, after which a summary of all shapefiles is given.</p> <p>Google Earth landslides are mapped using images from 2002 to 2019 with a mapped extent of approximately 3000 km<sup>2</sup> and was ground-truthed during a 12-day field visit from 23 February to 6 March 2019. The resultant landslide inventory contains 440 landslides with three main landslide types identified: translational slides, debris flows, and rockfalls. Translational slides include debris slides, rock slides, and unclassified translational slides. In the landslide inventory, debris flows and rockfalls are mapped as points representing their source area and translational slides are mapped as polygons representing both the source and depositional area. A complete description of the landslide types and mapping is given in Heijenk (2022, Chapter 3, section 3.4.2) The final landslide inventory (refer to how they would access it here, so a reference, or shapefile) includes the following:</p> <ul> <li><strong>Year</strong>, the year of the first image that the landslide appears in is taken,</li> <li><strong>Geology</strong>, the geological unit that the landslide occurs in is taken from Mottram <em>et al.</em> (2004),</li> <li><strong>Area</strong>, for translational slides the area is given,</li> <li><strong>Topographic data</strong> (elevation, aspect, slope, and curvature), which is taken from ASTER GDEM (Version 3.0, 2018, 30 m horizontal resolution, 30 m vertical resolution).</li> </ul> <p>The Cartosat landslide inventory contains 44 features mapped from one pair of stereoscopic Cartosat-1 images (National Remote Sensing Centre, Cartosat-1 ID 197823411, https://www.nrsc.gov.in/, 2.5 m x 2.5 m) captured on 30 September 2011 with extents of 851 km<sup>2</sup> and 957 km<sup>2</sup>. Three main landslide types have been mapped: deep-seated landslides, multi-temporal landslide areas, and rockfall areas. For deep-seated landslides, the scarp is mapped separately from the depositional area. A complete description of the methodology is given in Heijenk (2022, Chapter 3, section 3.4.3).</p> <p>The following shapefiles are included in this dataset:</p> <ol> <li><strong>Google_Earth_mapped_extent_21Dec2021.shp</strong>: Shapefile with a polygon that denotes the mapped extent of southern Sikkim State.</li> <li><strong>Google_Earth_landslides_polygon_21Dec2021.shp</strong>: Shapefile with 255 polygon features, where the polygon includes the source and depositional area of translational landslides.</li> <li><strong>Google_Earth_landslides_point_21Dec2021.shp</strong>: Shapefile with 185 point features that denote the source area of both debris flows and rockfalls.</li> <li><strong>Cartosat_197823411_extents.shp</strong>: Shapefile with 2 polygon features that denote the extent of the Cartosat-1 image pair captured on 30 September 2011.</li> <li><strong>Cartosat_landslides_21Dec2021.shp</strong>: Shapefile with 67 polygon features that describe 44 landslide features. Some landslide features have been mapped with separate polygons for the scarp and the depositional area.</li> <li><strong>Cartosat_197823411clouds.shp</strong>: Shapefile with 5 polygon features that show an estimated area of the clouds that block landslide mapping in the 30 September 2011 Cartosat-1 image pair.</li> </ol> <p>All shapefiles are in an WGS 84 EPSG:3857 projection.</p> <p>This research was funded by the UK Natural Environment Research Council (NERC, Grant # NE/R012148/1) and the British Geological Survey (BGS, BUFI).</p> <p>References: </p> <p>Heijenk, R.A. (2022). Landslide Variables, Inventories, and Domains in Data-Poor Regions: A Case Study in East Sikkim, India. [PhD thesis]. King’s College London.</p>
Landslide rainfall-induced Inventory at Japan case study (2017-2020)
<p>This dataset is used in the article, which title is "Combination of optical images and SAR images for detecting landslide scars, using a classification and regression tree", published in the International Journal of Remote Sensing (https://doi.org/10.1080/01431161.2023.2224096).</p><p>The dataset includes historical landslide inventories conducted by the Geospatial Information Authority of Japan (GSI) and digitized itself.</p>
Inventory of landslides triggered by the 2015 Mw 6.0 Sabah earthquake (Malaysia)
<p>These files are related to the paper “Landslides triggered by the 2015 Mw 6.0 Sabah (Malaysia) earthquake: inventory and ESI-07 intensity assignment” by Ferrario M.F., submitted to NHESS</p> <ul> <li>Shapefile of the mapped landslides and study area</li> <li>Spreadsheet with data on inventories of earthquake-triggered landslides</li> </ul>
Inventory of coal mining caused landslides and fissures in the center Chinese Loess Plateau
<p>A new inventory of coal mining caused landslides and fissures in the center of the Chinese Loess Plateau was generated based on four high-resolution DOMs conducted by UAV surveys and field investigations from 2020 to 2023. The field named "area" in the attribute tables of shapefiles named "2020_Landslides", "2021_Landslides", "2022_Landslides", and "2023_Landslides" indicates the area of the landslides (square meter) , and the "Time" indicates the occurred year of landslides. The field named "Length" in the attribute tables of shapefiles named "2020_Fissures", "2021_Fissures", "2022_Fissures", and "2023_Fissures" indicates the length of the fissures (meter), and the "Time" indicates the occurred year of fissures. The inventory of landslides and fissures in 2020 published by Yang et al., (2022) was referenced when we conducted visual interpretation.</p>
RER2023: the landslide inventory dataset of the May 2023 Emilia-Romagna event - Version 1
<p><span>The dataset consists of 80,997 polygons that document the landslides occurred in Emilia-Romagna, Italy, in May 2023. Landslides were manually mapped by experts using high-resolution aerial images (0.2 m resolution, RGB and near-infrared) taken shortly after the event. This landslide inventory has been designated as reference map by the Emilia-Romagna Region and the Po River Authority for the “Special Plan for interventions against situations of hydrogeological instability” (June 2024). It also serves to assist the Commission for Reconstruction during the recovery phase.</span></p> <p><span>The dataset is provided in ESRI (Environmental Systems Research Institute) shapefile format and includes several attributes: polygon ID (IDC), manually classified landslide type (ClassMan), geological unit at the polygon's centroid (Lito), Green Leaf Index (GLI), percentage of deposit over Non-Forested Slopes (NFS), and reclassified landslide type after applying a harmonization algorithm (ClassNew).</span></p> <p><span>Landslide classification within the ClassMan and ClassNew fields includes:</span></p> <p><span>DS1=debris slide with high mobility</span></p> <p><span>DS2=debris slide with low mobility</span></p> <p><span>DF1=debris flow with long runout</span></p> <p><span>DF2=debris flow with limited runout</span></p> <p><span>RS1=fully-developed rock-block slide</span></p> <p><span>RS2=incipient rock-bloc kslide</span></p> <p><span>ES=earth slide</span></p> <p><span>EF=earth flow.</span></p> <p><span>For a detailed explanation of the dataset, its attributes, and the harmonization methodology, please refer to the accompanying paper.</span></p>
Landslide inventories for Nepal: Database 2010 - 2021
Open the record for dataset details and reuse information.
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
<p>An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China</p>
An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China
<p>An Inventory of Large-Scale Landslides in Baoji City, Shaanxi Province, China.</p>
Multitemporal inventory of landslides in the epicentral region of the 2017 Jiuzhaigou earthquake
<p>Multitemporal inventory of landslides in the epicentral region of the 2017 Jiuzhaigou earthquake</p>
Landslide inventory - Rolante River Basin, Brazil
<p>Landslide inventory for the extreme event that occurred on 05 January 2017, in the Rolante River Basin, Brazil.</p> <p>This is an update of the landslide inventory generated by Quevedo, R.P., Oliveira, G.G., and Guasselli, L.A. described in the article available at http://doi.org/10.11137/2020_2_128_138.</p> <p>This update is a part of Quevedo's PhD thesis available at https://www.researchgate.net/publication/375958360_Do_Land_Use_and_Land_Cover_and_Spatial_Heterogeneity_influence_on_landslide_occurrence_An_analysis_of_susceptibility</p>
ScienceDex guides
Understand access before you commit
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.