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zenodo40/100

Supplement to "Discriminating non-stationary flood hazard effects via probabilistic estimation of sparse residuals from rescued and rated stage–discharge data"

This supplement contains the data and script associated with “Sparse hydrometric data rescue for exploratory analyses of conveyance-driven flood hazard trends and controls,” which has been submitted to a journal for consideration. This supplement is deposited on Zenodo.   Code, Data, and Attribute Descriptions   Uploaded are five directories (indicated in bold font, with their contents detailed below) containing input data and various outputs of the analyses performed for our case study on the Pulangi River at Lumayong (Philippines). Please consider this description equivalent to an omnibus README file for the deposited files. Note that we collected and rescued the hydrometric data herein from the archives of the Water Projects Division (WPD) of the Philippine Department of Public Works and Highways (DPWH).   ●      Pln_R_code contains (1) Pln_RC.R, the R script file, and (2) 240601_Pln_RC.RData, which stores objects generated from the script based on the last execution (on 1 June 2024). The script consists of admittedly too many lines of code (e.g., for data wrangling, formal analyses, and producing figures used in the manuscript) that should have been split into multiple .R files. Apologies. Please be guided by the outline and the comments and kindly reach out lest issues with the code arise.   ●      Pln_GH_PDFs contains the scanned gaugekeeper’s reports of gauge heights (“stages”).    ○      The name of each file varies. For example, Pln-GH-1100.pdf contains daily stage readings for all months in the year 2011. But, if the last two digits are not zeroes, as in Pln-GH-1204.pdf, they refer to the month of that year; in this case, for example, the PDF file contains stage data for the month of April in the year 2012. ○      Each scanned sheet contains sub-daily (with readings at “AM,” “NOON,” and “PM”) and mean daily stages for a given month. ○      Also indicated are the gaugekeeper’s remarks on the daily weather (e.g., “fair,” “cloudy”). Under inclement weather, the gaugekeeper would note the duration of rainfall and its intensity and might, at times, record extra stage readings.   ●      Pln_DM_PDFs contains the following PDF files and a sub-directory: ○      Pln-DM-<XXXXXX>.pdf contains the scanned log for each direct stage—discharge measurement, otherwise known as “gaugings.” Each file is named according to the date of gauging (YYMMDD). For example, the log for the gauging performed on 24 February 2011 can be found in the file Pln-DM-110224.pdf. Data from these gaugings are summarized in Pln-DM-filtered.csv in the Pln_In_CSVs directory. ■      The readability of each file varies based on the original quality of the original paper-format data. ■      The first page in each file contains, on the left side, a summary of the gauging data and metadata (e.g., date of measurement, number of gauging verticals or “sections” used, method of crossing or measuring the cross-section), and on the right side, the velocity—area readings at each gauging vertical. ■      The second page in each file contains the plotted cross-section of the channel at the time of measurement. ○      Pln-RC.pdf contains various other paper-format data and some annotations relevant to the historical rating at the station. ■      P. 1: The hydrographic engineer’s comment (dated 19 January 2012) on the evaluation of the discharge data, specifically detailing the periods of validity of the rating curves developed for different sub-periods of monitoring. ■      PP. 2—3: A summary table of all the gaugings performed from 1983 to 2010 whose logs were no longer retrievable (and hence not included as a DM-PDF file in this directory). ■      PP. 4—5: Plots of the official stage—discharge rating curves developed by DPWH hydrographers. ■      PP. 6—8: Rating tables used to convert daily mean stages to deterministic discharge estimates. Two of these rating tables were digitized and can be found in the Pln_RatingTables sub-directory in Pln_In_CSVs. ■      PP. 9—22: Daily stage (m) and its corresponding daily discharge (L/s) for the 2004—2010 sub-period. The stages were digitized and included in Pln-H_arch.csv in the Pln_In_CSVs directory. ○      Pln_DM_unused contains the PDF files of logs (including data and metadata) corresponding to the gaugings that were excluded from our analysis following our filtering step for gauging location consistency.   ●      Pln_In_CSVs contains the following CSV files and two sub-directories; these files were used as inputs to the R script for formal analyses: ○      Pln-H_arch.csv contains the mean daily stage values [“H_bar_arch”] (m) for every day in the 2004—2020 sub-period [“Date”] (YYYY-MM-DD). The stages in this file are already corrected for gross errors. ○      Pln-DM-filtered.csv contains the following information on the gaugings performed on the Pulangi at Lumayong (1983—2020): (i) date [“Date”] (YYYY-MM-DD); (ii) stage [“H”] (m); (iii) discharge in L/s [“Q_lps”] and m3/s [“Q_cms”]; (iv) wetted area [“A_sqm”] (m2); (v) mean flow velocity [“Vel_mps”] (m/s); (vi) channel width [“W_m”] (m); (vii) mean flow depth [“D_ave_m”] (m); (viii) location of the measurement cross-section with respect to the staff gauge, with negative values meaning downstream of the gauge and positive values meaning upstream of the gauge [“XS_loc_wrt_gage”]; (ix) maximum flow depth [“Max_Depth_m”] (m); and (x) minimum streambed elevation [“MINSBE”] (m). Note that this file includes only gaugings that passed our filtering step for measurement location consistency. ○      Pln-DM_oview.csv contains information on the temporal coverage (bounded by “Start_date” and “End_date”) of the available hydrometric data from the station archives. ○      Pln-H_sample_GrossE_corr.csv contains a sample sub-period (2017-06-15 through 2017-11-30) and the corresponding values of stages (m), uncorrected [“H_uncorrected”] and corrected for gross errors [“H_corrected”]. This CSV file was used as an input to the R script to produce one of the figures in the manuscript.  ○      Pln-XS-csv.csv contains data on the gauging transects: gauging ID [“GaugingID”]; date [“Date”] (YYYY-MM-DD); lateral distance from a fixed initial point [“Lat_distance”] (m); width of the gauging vertical [“Width_vert..m.”] (m); depth relative to the water surface [“Depth..m.”] (m); stage at the gauging vertical [“H_m_vert..m.”] (m); and elevation with respect to a fixed arbitrary datum at the station [“Elev..m.”] (m). ○      Pln_Rating_Tables contains two rating tables, Pln-DM - RatingTable_A.csv and Pln-DM - RatingTable_B.csv, prepared and used by DPWH hydrographers for converting stage values to deterministic discharge estimates for the Pulangi River at Lumayong for the 1980s—early 2000s sub-period. Each rating table contains columns for stage [“H”], discharge in L/s [“Q_lps”], and discharge in m3/s [“Q_cms”]. ○      Pln_Q_rated contains two CSV files: Pulangi.csv has the columns “YEAR”, “DAY”, and every month of the year [“JAN” through “DEC”] for the 1983—2003 sub-period, with the values under each month column indicating the deterministic discharge estimates in L/s; Pulangi_trunc.csv contains similarly formatted data, but for the 2009—2010 sub-period.   ●      Pln_Out_CSVs contains two CSV files, the primary outputs of the hydrometric data rescue effort. ○      Pln_Q_recon.csv contains the reconstructed discharge time series (1983—2020) and its associated uncertainties at the 95% credibility interval. It has the following columns: date [“Date”] (YYYY-MM-DD); daily stage [“H”] (m); lower bound of the discharge estimate [“Q_lwr”] (m3/s); median discharge estimate [“Q_med”] (m3/s); and the upper bound of the discharge estimate [“Q_upr”] (m3/s). Pln_H_recon.csv contains the reconstructed and quality-controlled stage time series (1983—2020). It has the following columns: date [“Date”] (YYYY-MM-DD); mean daily stage that has been corrected for gross errors, but not yet filtered through other quality checks [“H_bar_arch”] (m); quality check for low outliers [“Low_Outlier”] (TRUE/FALSE); quality check for flatliners [“Flatliner”] (TRUE/FALSE); difference between the stage values on day i and day i-1 [“Daily_Step”] (m); quality check for large steps [“Large_Step”] (TRUE/FALSE); and the corrected and quality-controlled mean daily stage values [“H”] (m).

opencc-by-4.0Jun 2024View details →

dryad40/100

Comparing first street foundation and PRIMo flood hazard data across the Los Angeles metropolitan region

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. The data in this repository supports an assessment of the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S.. For the analysis, FSF data was compared to PRIMo-Drain, a flood hazard model that resolves drainage infrastructure and fine resolution drainage dynamics. In the linked journal manuscript, using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ~1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.

opencc-zeroJul 2024View details →

zenodo40/100

FIGURE 3 in Heterobranch Sea Slugs from Hazard Canyon Reef, San Luis Obispo County, California

FIGURE 3. Seasonal variation at Hazard Canyon Reef in (A) number of species of Heterobranchia found per month, and (B) total number of individuals of Heterobranchia found per observer per hour. Values shown are means ± 1 SE of monthly data averaged by season across all years sampled (n = 8, 21, 3, 15 for winter, spring, summer, fall, respectively)..

opencc-by-4.0Feb 2023View details →

zenodo40/100

Dataset of tweets, used to detect hazardous events at the Baths of Diocletian site in Rome

This dataset is composed by 276865 tweets, extracted from the Twitter stream, in the period from May 2018 to May 2019. Each element is characterized by the ID, that permits to retrieve the tweet from the stream, the text content, the GPS information (if it is provided or not), the localization, and the time. The last attribute of the elements of the dataset is the label associated to the tweet after the process of event detection. If the tweet has been identified as containing useful information of an hazardous event at the Baths of Diocletian site in Rome, the last attribute (Generated  Useful Info at BOD) is "yes", otherwise its value is "no".

opencc-by-4.0Jun 2019View details →

zenodo40/100

Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.

The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in: Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S, Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G (2019) Advancing Coastal Risk Reduction Science and Implementation by Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556. doi: 10.3389/fmars.2019.00556 The readme.txt file contains information about data layers.

opencc-by-4.0Sep 2019View details →

zenodo40/100

Novel benzotriazole-nanomaterials for maritime applications: comparative anti-corrosion performance, environmental behavior, and hazard

<div>The present dataset contains chemical, characterization, and ecotoxicological data upon exposure of benzotriazole (BTA) in the soluble form and nanoforms (Mg-Al LDH-BTA and Zn-Al LDH-BTA). Chemical analyses data include the quantification of relevant anions (nitrites, nitrates, phosphates and chlorides, determined through High-Performance Liquid Chromatography) and target metals (Al, Mg and Zn) determined through Inductively Coupled Plasma Optical Emission Spectrometry). Characterization data include DLS (size) and zeta potential analysis. Ecotoxicological data includes acute and chronic endpoints determined in a wide-range of marine species.</div> <div> </div> <div> </div> <div> </div>

opencc-by-4.0Jul 2024View details →

zenodo40/100

Kinematic and Paleoseismic Investigation of an Upper-Plate Fault on Chirikof Island: A Potential Tsunami-Seismic Hazard Source within the Alaska Subduction Zone

Open the record for dataset details and reuse information.

opencc-by-4.0Aug 2024View details →

zenodo40/100

Supplementary data for ZeroPM Deliverable 2.1 - List of viable alternatives for uses of PFAS and PM substances with hazard characterization

This dataset contains the data collected for all the studies which are mentioned in the ZeroPM deliverable D2.1 titled "List of viable alternatives for uses of PFAS and PM substances with hazard characterization".

opencc-by-4.0Aug 2024View details →

zenodo40/100

Complex Lava Tube Networks Developed Within the 1792-93 Lava Flow Field on Mount Etna (Italy): Insights for hazard assessment Supporting Informations: Maps and sections of the lava tubes

<div> <div> <div> This supporting information for the above paper submitted to Frontiers in Earth Science - Volcanology, comprises Table 1, as well as the maps and sections of the 8 lava tubes analyzed in this paper, that are located within the 1792-93 lava flow field at Etna volcano. The methods used for the new surveys of the lava tubes are also explained. </div> </div> </div>

opencc-by-4.0Jun 2024View details →

zenodo40/100

Sample data for "Classification Modeling for Hazardous Rip Current Prediction" Notebook

This sample dataset is used in the notebook "Classification Modeling for Hazardous Rip Current Prediction" to demonstrate the application of using machine learning to identify hazardous rip current.  The notebook is available in the NOAA Center for Artificial Intelligence GitHub Learning Journey repository (https://github.com/noaa-ncai/learning-journey). The full dataset is available via NOAA.

opencc-by-4.0Aug 2024View details →

zenodo40/100

Loess and anthropogenic activities enable moderate-sized earthquakes to induce anomalous hazards

Raw data of "Loess and anthropogenic activities enable moderate-sized earthquakes to induce anomalous hazards"

opencc-by-4.0Oct 2024View details →

zenodo40/100

Wind and Precipitation Extremes in Great Britain (1979-2019) to apply the methodology for Spatiotemporal Identification of Compound Hazards

The data used in this study is extracted from ERA5. ERA5 is a climate reanalysis product which was released in 2019 by ECMWF and benefits from the latest improvements in the field (Hersbach et al., 2020). ERA5 data (ECMWF, 2020) is available 1979 to present (we use up to September 2019), with a spatial resolution of 0.25deg x 0.25deg and an hourly temporal resolution. The data resolves the atmosphere using 137 levels from the surface up to a height of 80 km (ECMWF, 2020). ERA5 data are generated with a short forecast of 18 h twice a day (06:00 and 18:00 UTC) and assimilated with observed data (ECMWF, 2020). more information about ERA5 can be found <a href="https://confluence.ecmwf.int/display/CKB/ERA5%3A+data+documentation">here</a>. The two following variables are extracted from the product: <ul> <li> Extreme precipitation (p): accumulated liquid and frozen water, comprising rain and snow, that falls to the Earth’s in one hour (mm). This value is averaged over a grid cell. </li> <li> Extreme wind (w): hourly maximum wind gust at a height of 10 m above the surface of the Earth (m s-1). The WMO (2021) defines a wind gust as the maximum of the wind averaged over 3 s intervals. As this duration is shorter than a model time step, this value is deduced from other parameters such as surface stress, surface friction, wind shear and stability. This value is averaged over a grid cell. </li> </ul> Importation of the raw data Input data is divided into 4 files for each variables representing 4 periods: <ol> <li>1979-1986</li> <li>1987-1997</li> <li>1998-2008</li> <li>2009-2019</li> </ol> <pre>library(ncdf4) filer=c(paste0(getwd(),"/data/in/raindat_7986.nc"), paste0(getwd(),"/data/in/raindat_8797.nc"), paste0(getwd(),"/data/in/raindat_9808.nc"), paste0(getwd(),"/data/in/raindat_0919.nc")) filew=c(paste0(getwd(),"/data/in/windat_7986.nc"), paste0(getwd(),"/data/in/windat_8797.nc"), paste0(getwd(),"/data/in/windat_9808.nc"), paste0(getwd(),"/data/in/windat_0919.nc")) Startdate=as.POSIXct("1979-01-01 10:00:00") Enddate=as.POSIXct("1986-12-31 23:00:00") # ncr = nc_open(filer) # ncw = nc_open(filew)</pre> Intermediary data Intermediary data are stored in the “data/interdat” folder which contains the following files in Rdata format: <pre><code>## [1] "allraininclusters1.Rdata" "allraininclusters2.Rdata" ## [3] "allraininclusters3.Rdata" "allraininclusters4.Rdata" ## [5] "extremEventsWind.Rdata" "interclustRain.Rdata" ## [7] "interclustWind.Rdata" "metaclustRain.Rdata" ## [9] "metaclustWind.Rdata" "Rain_99_AllP.Rdata" ## [11] "rainP1.Rdata" "rainP2.Rdata" ## [13] "rainP3.Rdata" "rainP4.Rdata" ## [15] "rawclustRain.Rdata" "rawclustWind.Rdata" ## [17] "timeP1.Rdata" "timeP2.Rdata" ## [19] "timeP3.Rdata" "timeP4.Rdata" ## [21] "windP1.Rdata" "windP2.Rdata" ## [23] "windP3.Rdata" "windP4.Rdata" ## [25] "Wnd_99_AllP.Rdata" </code></pre> <ul> <li> allraininclustersX: [data.frame] files are used to assess more accurately the accumulated precipitation during events by collecting precipitations from timesteps in which precipitation is above and below the threshold for every grid cell and the whole duration of the cluster. </li> <li> 99_allp: [matrix] value of extreme precipitation and extreme wind gust threshold over the whole domain (one value per grid cell) </li> <li> interclust: [list] files contain a list of data from wind and precipitation clusters divided in the 4 periods aggregated over space and clusters (1 value per grid cell per cluster). These files are uses to create the files “RainEv_ldat” and “Windev_ldat”. </li> <li> metaclust: [list] files contain a list of metadata from wind and precipitation clusters divided in the 4 periods . These files are uses to create the files “RainEv_meta” and “Windev_meta”. </li> <li> rainPX: [matrix] files contain a 3D matrix of dimension longlattime containing precipitation data for the period X. </li> <li> rawclust: [list] files contain a list of data.frame from wind and precipitation clusters divided in the 4 periods. These files are uses to create the files “RainEv_hdat” and “Windev_hdat”. </li> <li> timePX: [vector] contain vectors of time for the 4 periods. </li> <li> windPX: [matrix] files contain a 3D matrix of dimension longlattime containing wind gust data for the period X. </li> </ul> Output data Output data contains metadata and raw data of single and compound hazard clusters are stored in the “data/out” folder which contains the following files in Rdata format: <pre><code>## [1] "compoundclusters.csv" "CompoundRW_79-19.v3x.Rdata" ## [3] "extremEvents_Rain.Rdata" "extremEvents_Wind.Rdata" ## [5] "Rain_stfprint.Rdata" "rainclusters.csv" ## [7] "RainEv_hdat_1979-2019.Rdata" "RainEv_ldat_1979-2019.Rdata" ## [9] "Rainev_ldatp_1979-2019.Rdata" "RainEv_meta_1979-2019.Rdata" ## [11] "RainEv_metap_1979-2019.Rdata" "Wind_stfprint.Rdata" ## [13] "windcluster.csv" "WindEv_hdat_1979-2019.Rdata" ## [15] "WindEv_ldat_1979-2019.Rdata" "WindEv_meta_1979-2019.Rdata" </code></pre> <ul> <li> CompoundRW: [data.frame] contains metadata for the compound hazard clusters identified </li> <li> _hdat: [data.frame] hourly data of precipitation and wind gust clusters. </li> <li> _ldat: [data.frame] aggregated data over space and clusters (1 value per grid cell per cluster) for wind gust and precipitation clusters.Rain_ldatp contains aggregated values including non-extreme timesteps. Created from allraininclustersX. </li> <li> _meta:[data.frame] metadata for wind gust and precipitation clusters </li> <li> stfprint: [data.frame] files containing duration*footprint of each hazard clusters during all clusters </li> <li> sptdf: [data.frame] data.frame containing spatial, temporal, cluster and intensity information </li> </ul> Codes assiciated to the method are availaible here: https://github.com/Alowis/SI-CH

opencc-by-4.0Jun 2021View details →

zenodo40/100

Data Repository: 2022 Hawai'i Cesspool Hazard Assessment & Prioritization Tool

Data Repository, Codebase, inputs and Results for the Hawaii Cesspool Prioritization Tool.  A project conducted by University of Hawaii Sea Grant and Water Resources Research Center, Data updated October 2022.  Please see also:  https://github.com/cshuler/Act132_Cesspool_Prioritization and  https://health.hawaii.gov/wastewater/files/2022/11/prioritizationtoolreport.pdf

opencc-byNov 2022View details →

zenodo40/100

Fig.1 in Distribution Of Ixodes Ricinus (Arachnida, Ixodidae) In Ukraine In The Context Of Tick Hazard, And Factors Favoring Its Persistence In Conditions Of Fast-Going Environmental Change

Fig.1. The hiatus in the range that has been noted on the territory of Ukraine: 1 — European area of distribution; 2 — Crimean area of distribution; 3 — Caucasian-Western Asian area of distribution; 4 — zone of disjunction (after fig. 1 Akimov & Nebogatkin, 1996 with changes).

opencc-by-4.0Dec 2022View details →

zenodo40/100

Fig. 2 in Distribution Of Ixodes Ricinus (Arachnida, Ixodidae) In Ukraine In The Context Of Tick Hazard, And Factors Favoring Its Persistence In Conditions Of Fast-Going Environmental Change

Fig. 2. Distribution of I. ricinus on the territory of Ukraine, from the point of view of tick-borne danger.

opencc-by-4.0Dec 2022View details →

zenodo40/100

Estimating durations of disruptions to freight transport due to climate-related hazards

A table of hazard intensity thresholds and notes on how to estimate the duration of disruption to freight transport (road and rail) due to climate-related hazards (extreme heat, flooding and tropical cyclone winds).

opencc-by-4.0Mar 2023View details →

zenodo40/100

Streamflow drought hazard indicators for monitoring drought hazard for human water supply and river ecosystems at the global scale (WaterGAP 2.2d, WFDEI-GPCC)

1) Streamflow drought hazard indicators (SDHIs) as computed by WaterGAP 2.2d (climate data WFDEI-GPCC) for the whole globe except Antarctica, spatial resolution: 0.5°, monthly data for the reference period 1986-2015: Indicators of drought magnitude: SSI1, SSI12, EP1, RDQI1 Indicators of drought severity: CDQI1-Q50, CDQI1-Q50_f, CDQI1-Q80, CDQI1-Q80_f, CDQI1-Q80-HS, CDQI6-Q80, CDQI6-Q80_f, CDQI1-WUs, CDQI1-WUs-EFR, CDQI1-WUs-EFR_f, CEP1(20%), CEP1(20%)_f, CRDQI1(-50%), CRDQI1(-50%)_f 2) WaterGAP grid cell IDs ("arcid") with longitude and latitude: (WaterGAP_ArcID_lon_lat.txt) 3) Streamflow observations at 220 GRDC gauging stations and list of the 220 GRDC station numbers with related WaterGAP grid cell ID ("arcid"): Observed_monthly_streamflow_km3month_220_calstations_1986_2015.txt GRDC_number_WaterGAP_ArcID_220_stations.txt 4) SDHIs for four GRDC gauging stations: time_series_danube_river.txt, time_series_angara_river.txt, time_series_white_river.txt, time_series_orange_river.txt 5) WaterGAP output: Mean monthly surface water abstractions in km3 per month: Mean_monthly_WUs_km3_per_month_WFDEI_GPCC_ant_22d_1986_2015.txt

opencc-by-4.0Apr 2023View details →

zenodo40/100

The HELPOS Fault Database: a new contribution to seismic hazard assessment in Greece

In seismically-active regions such as Greece, the mapping of active faults is a key step to assess seismic hazards and evaluate deterministic ground motion scenarios for infrastructure works, pipeline designs and other constructions of critical importance. Here, we present a comprehensive database of active onshore and offshore faults in Greece based on existing studies and GIS geospatial mapping using geological, geophysical, seismological and geomorphological criteria. The design and population of the database follows the NOAFaults concept <a href="http://doi.org/10.5281/zenodo.3483136">http://doi.org/10.5281/zenodo.3483136</a> and development in ARCGIS environment. The HELPOS database includes over 550 faults with simplified (linear) traces and lengths between 8 – 108 km (onshore part) together with their corresponding 2D rupture planes. Additional information includes parametric data such as maximum expected magnitude, slip rate, length, width, strike, dip angle, last seismic event, rupture depth (to top-fault) and fault kinematics. A particular aim of the HELPOS Fault database has been an update of the seismic sources model for the seismic hazard assessment of Greece considering shallow earthquakes, which involves modeling surface fault traces in terms of seismic sources at depth. The fault database is a major contribution to HELPOS with applications among others in volcano-tectonic settings, urban planning, paleoseismology, landscape processes, and in the study of active tectonics, deformation and interactions between overriding plate (Aegean) faults and the Hellenic subduction. In this version of the database (v1.8) we include the onshore fault traces and rupture planes and the offshore fault traces. We acknowledge funding by project "HELPOS - Hellenic Plate Observing System” (MIS 5002697) which was funded by the Operational Programme “Competitiveness, Entrepreneurship and Innovation” (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).

opencc-by-4.0May 2023View details →

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Slip deficit rate realizations for 2023 New Zealand National Seismic Hazard Model geodetic inversions

This data set contains inversion results presented in Johnson et al. (2023) and also Johnson et al. (2022). All calculations involving slip deficit rates in those papers were conducted using the results in the files provided in this data set.

opencc-by-4.0Jul 2023View details →

zenodo40/100

Geospatial Analysis of Road Conditions and Hazardous Factors in Communities on Continuous vs. Sporadic Permafrost in Greenland

Road conditions and hazardous factors were surveyed in two permafrost-affected communities of West Greenland, Ilulissat (underlain by continuous ice-rich permafrost) and Sisimiut (underlain by sporadic permafrost). Pavement damages, repairs, embankment structural elements, artificial drainage systems, water accumulations and preferential snow ploughing deposits were notably mapped and georeferenced in a geographic information system to form high-resolution spatial databases. In total, respectively 66 and 76 \% of the paved road networks of Ilulissat and Sisimiut were surveyed. Manual in-situ mapping took place in September 2020 and September 2021 in Ilulissat, while Global Navigation Satellite System (GNSS) equipment was used to map road conditions in Sisimiut in September 2020. The severity of the pavement damages was assessed according to the ASTM D 6433–07, Standard Practice for Roads and Parking Lots Pavement Condition Index Surveys, by ASTM International (2008). The drainage conditions were characterized following the recommendations in Cold Regions Pavement Engineering, by Doré, G. and Zubeck, H. K. (2009). This dataset comprises the geospatial layers of the road damage and hazard inventories created for the settlements of Ilulissat and Sisimiut. Each settlement’s inventory is provided in a ZIP-folder, containing the geospatial layers as geopackages and sorted following a thematic structure. Further information about each layer and its attributes can be found in the metadata PDF document.

opencc-by-4.0Sep 2023View details →

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record