Find research datasets worth reusing

openOpenJun 2016View details →

Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: GPS coordinates for a 75x75m spatial domain along a permafrost and vegetation gradient.

This dataset includes GPS coordinates and elevation data across a 75x75m spatial domain in the Caribou-Poker Creeks Research Watershed. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.

openOpenMar 2020View details →

Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences X - Research Project Site Information 2014

This data set was collected as a part of Brian Houseman's MS Thesis, Post-fire Variability in Siberian Alder in Interior Alaska: Distribution Patterns, Nitrogen Fixation Rates, and Ecosystem Consequences (December 2017). Data include research site location information. Data were collected on study plots established across two burn scars (2004 Boundary Fire and 1971 Wickersham Dome Fire) within the Yukon-Tanana Uplands ecoregion of interior Alaska.

openOpenMar 2021View details →

Soil Temperature and Moisture Data Collected from Permafrost and Fire Research Sites near Delta Junction, Alaska from 2008-2019

This dataset contains the hourly output from soil moisture and temperature sensors located near Delta Junction, Alaska. These sensors are located in four sites: 1. no permafrost, unburned 2. no permafrost burned 3. permafrost, unburned 4. permafrost (preburn), burned

openCC (other)Feb 2023View details →

Map of ecological sites and ecological states for pastures 1, 4, 14, and 15 on the Chihuahuan Desert Rangeland Research Center, New Mexico

This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.

openCC (other)Jun 2024View details →

North Temperate Lakes-LTER Core Research Lakes Information

Lake information for our eleven core NTL-LTER study lakes. These include seven in the Trout Lake area (Allequash, Big Muskellunge, Crystal Lake, Crystal Bog, Sparkling, Trout Bog and Trout Lake), and four lakes in the Madison area (Mendota, Monona, Wingra, and Fish). Data includes lake identifiers for various water-body databases, geographic location, general lake characteristics, watershed and shoreline land cover descriptions, and long-term averages of select water quality measurements.

openCC (other)Jul 2021View details →

Descriptive data file for information regarding microbial genetic research in the environs of Plum Island Sound watersheds, PIE LTER, Massachusetts.

This is a descriptive, tabular dataset of publications related to microbial or genomic research conducted within PIE. Assession numbers for genetic sequences generated from PIE samples are provided where available, followed by a very brief description of analysis type and study objectives. Sampling locations within PIE, sampling dates, and habitat type (sea water, fresh water, sediment, marsh) are also given. Environmental data are included in some publications and are listed here (if brief) or availability is described. Links to sequence archives are given in Methods.

openCC0Sep 2019View details →

Sevilleta Long Term Ecological Research Program Plant Species List

Sevilleta Long-Term Ecological Research Program has monitored plant species cover, height, abundance (counts), and biomass since 1999. This list represents the plant species found at the Sevilleta, including those species featured in long-term datasets on plant abundance and biomass. Species codes have been updated to the most recent taxonomic designations by the U.S. Department of Agriculture (plants.sc.egov.usda.gov), and are listed by their kartez codes, or character and number symbols.

openCustomJun 2017View details →

Coastal SEES Collaborative Research: Coastal Sustainability: A cross-site comparison of salt marsh persistence in response to sea-level rise and feedbacks from social adaptations

Coastal ecosystems are often valued for decision-making purposes based on monetized market and non-market values of goods and services, and associated economic impacts. Examples include values of fishery landings, price changes for waterfront homes, and tourism revenues. Monetized quantities such as these do not provide a comprehensive characterization of the values provided by these ecosystems. Human reliance on the goods and services provided by ecosystems and the global decline in the health of many of these ecosystems suggests the need for ecosystem valuation to help inform decision-making and conservation policy. However, traditionally employed economic valuation methods are rarely able to capture the full scope of the benefits ecosystems provide, including benefits provided by "cultural" ecosystem services. Qualitative methods such as focus groups can provide insight on these values not available through quantitative methods alone. This research explores public perceptions of salt marsh value through the use of semi-structured focus groups in marsh-adjacent communities in Massachusetts, Virginia, and Georgia. The data include de-identified focus group transcripts from three 90-minute focus groups held in each state. Initial questions were drawn from the same semi-structured question list in each focus group, with exploratory follow-up questions based on participant responses. Results of text analysis suggest that in case study communities, outdoor experiences in salt marshes inspire serenity in Massachusetts, influence shore identities in Virginia, and promote stewardship cultivation in Georgia. Perceived threats to these benefits, such as the threat of residential development, industrial pollution, and increasing flood risk, together constitute the context for various community responses related to marsh protection. Results supplement information from extant economic valuations and show the importance of utilizing diverse methods to elicit information on soci

Data for Research Assessment in the Transition to Open Science. 2019 EUA Open Science and Access Survey Results

This database refers to the data collected by the European University Association (EUA) for its Open Science and Access Survey 2019, which gathered responses from universities and higher education institutions across Europe. The full report published by the association is available at <a href="https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html">https://eua.eu/resources/publications/888:research-assessment-in-the-transition-to-open-science.html</a>. The data included in this database refers only to those universities and higher education institutions that accepted their data to be available in open access (n=174). All information that could lead to the identification of individual universities and higher education institutions was removed from the database (cf. cells highlighted in red). The following files are available: <ul> <li>2019 EUA Open Science and Access Survey</li> <li>Database in the following formats: .xlsx (Microsoft Excel)</li> <li>Survey Codebook: includes information on all the variables and their coding.</li> </ul>

Dataset: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey.

Dataset for: Koschollek C, Kuehne A, Müllerschön J, Amoah S, Batemona-Abeke H, Dela Bursi T, Mayamba P, Thorlie A, Mputu Tshibadi C, Wangare Greiner V, Bremer V, Santos-Hövener C: Knowledge, information needs and behavior regarding HIV and sexually transmitted infections among migrants from sub-Saharan Africa living in Germany: Results of a participatory health research survey. This dataset has been described in a PLoS One paper and contains all data necessary to replicate the results presented within this paper (10.1371/journal.pone.0227178). Please cite both the paper as well as the DOI of this dataset if you make use of the data.

A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled: Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study Iliana Chollett1, D. Ross Robertson2 1 Sea Cottage, Louisburgh, Co. Mayo, Ireland 2 Smithsonian Tropical Research Institute, Balboa, Panamá

A set of six databases used in a study of the biogeography of Greater Caribbean reef fishes entitled:   Comparing biodiversity databases: Greater Caribbean reef-fishes as a case study                                    Iliana Chollett, D. Ross Robertson     Database Authors: D Ross Robertson and Ernesto Peña, Smithsonian Tropical Research Institute, Panamá   This set of six databases contains georeferenced location records from six sources as described below.These six sources provided georeferenced records of occurrence of fishes found in the Greater Caribbean study area (6-330 N, 57-1000 W). Each occurrence record consists of a species name and associated latitude and longitude. Databases included in the comparisons made here are from five major online aggregators. Since their content overlaps to some extent, and OBIS, iDigBio and FishNet collaborate with GBIF, their data might be expected to produce similar biogeographic patterns. STRI includes a curated compendium of data from those five aggregators, enriched with data from many additional sources.   Only reef-associated fish species were included in the present analysis. These mostly represent demersal species known to occur on hard bottoms (coral, rock and oyster substrata), but also include species living on rubble, sand and vegetated bottoms within and around the immediate fringes of reefs, and pelagic species regularly found on reefs. All exotic and non-resident species and species other than reef-associated fishes were excluded from all databases prior to comparisons. Non-residents were defined as otherwise widespread species only rarely seen in the study area. Shore-fishes, including what are generally regarded as reef fishes, include those found in the waters of continental and insular shelves, i.e. between 0-200m. Reef-fish assemblages dominated by shallow-water taxa extend down to that depth in the study area (Baldwin et al. 2018). We used the shelf edge as a breakpoint and excluded records in areas deeper than 200m, identifying those areas using the General Bathymetric Chart of the Oceans (Kapoor, 1981; GEBCO Compilation Group, 2019).   Before the analyses, for all databases, duplicate records were deleted. Subsequently, records in the Pacific or on land were deleted. We used the Global Self-consistent, Hierarchical, High-resolution Geography Database (Wessel & Smith, 1996) to identify these areas. The spatial distribution of species-records in each database is shown in Figure 1 of the publication.   Global Biodiversity Information Facility (GBIF, https://www.gbif.org/): GBIF is an international network and research infrastructure aimed at providing open access to data about all types of life on earth. GBIF works through participant nodes using common standards and open-source tools that enable them to share information. Data from among the 49,000+ datasets hosted by GBIF that were used here range from those on museum specimens collected since the 18th century, to published scientific checklists, to curated  local checklists produced by trained science sources such as the Atlantic and Gulf Rapid Assessment Program (https://www.agrra.org/),to geotagged smartphone photos (that act as vouchers allowing verification) shared by amateur and scientific naturalists through iNaturalist (https://www.inaturalist.org/), to unvouchered, unverified and unverifiable observation records from untrained divers such as those contributing to DiveBoard (http://www.diveboard.com). GBIF data are standardized in Darwin Core format. GBIF data were obtained from a polygon of the region of study and subject to taxonomic review and selection after downloading. GBIF data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the GBIF portal, https://www.gbif.org/, on or about 2019-05-19).   Ocean Biogeographic Information System (OBIS,  <a href="https://obis.org/">https://obis.org/</a>): OBIS is a global open-access data and information clearing-house on marine biodiversity (OBIS, 2019) that was adopted as a project of the Intergovernmental Oceanographic Data and Information Exchange of the Intergovernmental Commission of UNESCO . Its range of sources is similar to that of GBIF. OBIS hosts data from organizations or programs that join it as one of 13 “nodes”, and harvest the data from the IPT (Integrated Publishing Toolkit), where providers publish their data. The IPT is developed and maintained by the GBIF, and OBIS is a major contributor of marine data to GBIF. Data are standardized in Darwin Core format. OBIS data were obtained for the region of study by downloading data on each family, then retaining only data inside the study area, which were then subject to taxonomic review and selection (accessed through the OBIS portal, https://obis.org/, on or about 2019-05-19).   Integrated Digitized Biocollections (iDigBio, https://portal.idigbio.org/portal/search): iDigBio is sponsored by the a US National Science Foundation and run by the University of Florida that provides digital data from public, non-federal, US collections. Data are standardized in a Darwin Core format, and provided “as is”. IDigBio joined the GBIF network in 2017. IDigBio records were downloaded from a polygon of the region of study and subject to taxonomic review and selection (accessed through the iDigBio portal, https://portal.idigbio.org/portal/search, on or about 2019-05-19).   FishNet2 (http://www.fishnet2.net/): FishNet2 is a collaborative effort that aggregates data on fish collections around the world to share and distribute data on specimen holdings from ~75 museums, universities and other institutions. FishNet2 distributes data in Darwin Core, and data are provided “as is”. FishNet2 is part of the network VerNet, which has contributed to GBIF since 2013 and became part of IDigBio in 2016. While FishNet2 has made substantial efforts to georeference location-record data it hosts, many hosted records still lack georeferencing. FishNet2 data were obtained from a polygon of the study area and subject to taxonomic review after downloading (accessed through the Fishnet2 Portal, www.fishnet2.org, 2019-05-19).   FishBase (<a href="http://www.fishbase.org/">http://www.fishbase.org</a>): FishBase is a global biodiversity information system supervise by a consortium of nine non-USA international institutions, and hosts data on fin fishes and elasmobranchs  (Froese & Pauly, 2009). Information presented in FishBase is extracted from the scientific literature, reports and museum or aggregator (GBIF) databases, and standardized by a team of specialists. Data from Fishbase were downloaded for the following ecosystems: Caribbean Sea, Gulf of Mexico, Southeast U.S. Continental Shelf, Atlantic Ocean, Sargasso Sea and Bermuda, and subject to taxonomic review and selection after downloading (2019-05-19).   Smithsonian Tropical Research Institute (STRI; <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>): The STRI database was compiled by DRR and Ernesto Peña at STRI’s Naos Marine Laboratory, and represents about 15 years accumulation of curated data (see below) from the following sources:  data downloaded at roughly two year intervals from the five aggregators; data from online databases of various museums that supply aggregators (data directly downloaded from a museum sometimes differs from that available in an aggregator from the same museum), including the Swedish Museum of Natural History, the American Museum of Natural History, the Natural History Museum of Denmark, the Gulf Coast Research Laboratory, the Colombian Museum of Natural Marine History, the United States National Museum, and the United States Geological Survey; data from national aggregators of Colombia (Sistema de Información Sobre Biodiversidad de Colombia (https://sibcolombia.net/), and  Sistema de Información Ambiental Marina de Colombia, https://siam.invemar.org.co/), Mexico (La Comisión Nacional para el Conocimiento y Uso de la Biodiversidad, CONABIO;   http://www.conabio.gob.mx/informacion/gis/), and Costa Rica (Museo de Zoologia de la Universidad de Costa Rica, http://museo.biologia.ucr.ac.cr/); verified (by DRR) underwater photographs of fishes taken at known locations; peer reviewed publications containing location information (species descriptions; taxonomic revisions of species, genera and families; regional and local checklists); fisheries reports; digital tagging data for species such as elasmobranchs; diving surveys and collections of local faunas by DRR (e.g. Robertson et al. 2019). In addition selected data from two sources that collect species lists at sites scattered throughout the Greater Caribbean are incorporated: from the Atlantic and Gulf Rapid Reef Assessment program (AGRRA, https://www.agrra.org/: Kramer & Lang, 2003) and from trained citizen scientists who contribute data on fishes to the Reef Environmental Education Foundation’s database (REEF: Pattengill-Semmens & Semmens, 2003). The bibliographic module (https://biogeodb.stri.si.edu/caribbean/en/library) of Robertson & VanTassel (2019) contains ~1700 publications linked to species names, among them the publications from which location data were extracted.   Data from the aggregators is presented “as is” and the aggregators themselves do not do data curation. Duplicates (and occasionally triplicates and quaduplicates) of the same museum record often are included from multiple sources (e.g. the original museum source, derivative checklists, an aggregator), sometimes with slightly different georeferenced coordinates. Data available in one year may subsequently disappear from an aggregator, and different data may be available for the same species under different names (e.g. the old and new names when a species is reassigned to another genus). Errors, sometimes large errors (Robertson, 2008), are common in aggregator data, from museums as well as other sources, and longstanding errors can seem to take on a perpetual existence. For example the damselfish Abudefduf saxatilis is a common and widespread inhabitant of tropical reefs on both sides of the Atlantic, but does not naturally occur outside that ocean. Despite the fact that its taxonomic status and range were resolved ~30 y ago (e.g. see Allen, 1991) museum data presented by the all five aggregators that contributed to the multi-source database used in this study currently (December 10, 2019) show large numbers of records of this species throughout the entire tropical Indo-Pacific, as well as across its native range in the Atlantic. Since many of the databases accumulating on aggregators are derivative (lists derived from records and from other derivative lists) it will become increasingly difficult to eliminate such errors as corrections to data in primary sources do not automatically propagate through the chain of usage by different databases. Due to increasing limitations on resources for taxonomic work, museums themselves have difficulty dealing with errors in specimen identity and location, and old specimens become unidentifiable, specimens never get returned when loaned out, or simply vanish, and entire collections can get destroyed by hurricanes or fires, or get dumped when museums close or experience a major change in mission. Georeferenced location data on fish distributions in the neotropics (and presumably most other areas) hosted by aggregators, particularly GBIF and OBIS, which take data from a broad range of source types, might best be described as messy, and the significant potential for errors in location records and an inability to verify records always needs to be taken into account when incorporating data from aggregators, primary museum sources, and analog sources.   Data considered for inclusion in the STRI database were screened as follows to exclude questionable records.  Data from two databases hosted by OBIS and GBIF were excluded entirely due to lack of reliability: BioGoMx (https://www.gulfbase.org/project/biodiversity-gulf-mexico-biogomx-database) and Diveboard (http://www.diveboard.com).  The only REEF data used were from “expert” REEF recorders on readily identifiable species that are unlikely to be confused with similar species (e.g. data for some genera of sparids, gerreids, labrisomids and gobies that include various sympatric species with very similar appearances, were not used).  After data from aggregators and museum sources were combined into a single database duplicate records were filtered out by rounding all records to three decimal places and eliminating duplicates, a process that inevitably deleted some valid records as well as duplicates. The sizes of the databases and abundance of such duplicates precluded individual manual exclusion. Finally, all location data for each species were revised by DRR by examining the distribution of its georeferenced coordinates overlayed on a digital map of the current known distribution range of that species (for such range information see Carpenter & De Angelis, 2002; Ebert et al., 2013; Last et al., 2016; Robertson & Van Tassell, 2019; IUCN Redlist species accounts for most species considered here: https://www.iucnredlist.org/search). Such revision took into account any recent modifications to taxonomy and distributions due to new data and new publications, or as a result of discussions between DRR and experts in the taxonomy of particular species or genera. Source information of many individual questionable records provided by aggregators with the hosted data was inspected to try and assess their validity. Records thought likely to be erroneous were deleted. Those included inexplicable records lacking adequate documentation located well outside the known distribution range, and records in unlikely habitats (e.g. on land for marine species; in deep water for shallow-water species). This revision process reduced the number of records by about 30%.   Data from the five individual aggregator databases that are used in the comparisons described here were all downloaded from their online portals during May, 2019. However, data from those five aggregators that were incorporated in the STRI database were downloaded in March 2017, with data from other sources described above added to the STRI database intermittently between then and May 2019, when the entire dataset was curated as described above. Hence the five individual aggregator databases analyzed in this study undoubtedly contain data not included in the version of the STRI database used in the present analyses.   Acknowledgements   Data acquisition and construction of the STRI database was supported by funds from STRI, the Smithsonian Marine Science Network, the Smithsonian Publications Fund, the Smithsonian’s Deep Reef Observation Project, the National Geographic Society, the IUCN Red List program, the Harte Research Institute, and CONABIO. We thank REEF and AGRRA for supplying species-location records, various people for taxonomic and location-record information used to construct that database (principal among them C Baldwin, S Brandl, K Conway B Frable, T Menut, T Munroe, R Robins, L Tornabene, J Van Tassell and B Victor), and hundreds of citizen-scientist submarine photographers whose images (see <a href="https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists">https://biogeodb.stri.si.edu/caribbean/en/contributors/citizen_scientists</a>) acted as vouchers for location records.   References   Allen, G.R. (1991) Damselfishes of the World. Mergus, Melle, 271 p. Baldwin, C.C., Tornabene, L. & Robertson, D.R. (2018) Below the mesophotic. Scientific Reports, 8, 4920. Carpenter, K.E. (Ed) (2002) The living marine resources of the Western Central Atlantic. Vols 1-3, FAO, Rome, 2127 p. Ebert, D.A., Fowler, S., Compagno, L. (2013) Sharks of the World: a fully illustrated guide. Wild Nature Press, Plymouth. 528 p. GEBCO Compilation Group (2019) GEBCO 2019 Grid (doi:10.5285/836f016a-33be-6ddc-e053-6c86abc0788e). Kapoor, D.C. (1981) General bathymetric chart of the oceans (GEBCO). Marine Geodesy, 5, 73–80. Kramer, P.R. & Lang, J.C. (2003) Appendix one: The Atlantic and Gulf Rapid Reef Assessment (AGRRA) Protocols: Former Version 2. 2. Atoll Research Bulletin, 496, 611–624. Last, P. R., White, W.A., de Carvalho, M.R., Séret, B., Stehmann, F.W., & Naylor, J.P. (2016). Rays of the World. CSIRO, Clayton. 790 p. Pattengill-Semmens, C.V. & Semmens, B.X. (2003) Conservation and management applications of the reef volunteer fish monitoring program. Coastal Monitoring through Partnerships: Proceedings of the Fifth Symposium on the Environmental Monitoring and Assessment Program (EMAP) Pensacola Beach, FL, U.S.A., April 24–27, 2001 (ed. by B.D. Melzian), V. Engle), M. McAlister), S. Sandhu), and L.K. Eads), pp. 43–50. Springer Netherlands, Dordrecht. Robertson, D. R. (2008) Global biogeographic databases on marine fishes: caveat emptor. Diversity and Distributions, 14, 891-892 Robertson, D.R,, Dominguez-Dominguez, O., Lopez Arollo, Y.M., Moreno Mendoza. R., Simoes, N. (2019) Reef-associated fishes from the offshore reefs of western Campeche Bank, Mexico, with a discussion of mangroves and seagrass beds as nursery habitats. Zookeys 843: 71-115. <a href="https://doi.org/10.3897/zookeys.843.33873">https://doi.org/10.3897/zookeys.843.33873</a> Robertson, D.R & Van Tassell, J. (2019) Shorefishes of the Greater Caribbean: online information system. Version 2.0. Smithsonian Tropical Research Institute, Balboa, Panamá. <a href="https://biogeodb.stri.si.edu/caribbean/en/pages">https://biogeodb.stri.si.edu/caribbean/en/pages</a>. Wessel, P. & Smith, W.H.F. (1996) A global, self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research: Solid Earth, 101, 8741–8743.

openmit-licenseJan 2020View details →

CVE-2019-18222: research data and tooling

This dataset and software tool are for reproducing the research results related to CVE-2019-18222. Description <ul> <li><code>enum</code> contains the key enumeration tool.</li> <li><code>kt_candidates</code> contains the JSON for blinded nonce candidates, indexed by trial number. JSON fields:</li> </ul> <ol> <li><code>kt_candidates</code>: list of nonce candidates.</li> </ol> <ul> <li><code>sig_data</code> contains the JSON for ECDSA signatures, index by trial number. JSON fields:</li> </ul> <ol> <li><code>p</code>: the prime the curve is defined over. (P-256 here.)</li> <li><code>Gx</code>, <code>Gy</code>: Generator coordinates.</li> <li><code>d</code>: Ground truth ECDSA long term key.</li> <li><code>Px</code>, <code>Py</code>: Public key coordinates.</li> <li><code>h</code>: SHA-256 digest to sign, encoded to the finite field.</li> <li><code>k</code>: Ground truth ECDSA nonce.</li> <li><code>r</code>, <code>s</code>: ECDSA signature.</li> </ol> Build <pre><code>cd enum make clean make</code></pre> Run Start with <code>enum</code> as the working directory. <pre><code>cd enum</code></pre> Pull out a <code>kt</code> candidate, in this example index 847. <pre><code>$ jq '.kt_candidates' ../kt_candidates/kt_candidates_847.json [ "0x48ad7217d10f6c7b1a3db836d38aa3972999115f38a6b3d176fc660941aa5c882d2528ec1fc27da7610e7ee3d7dd84367c380259e0386224c2c46aa2a5eb2a0" ]</code></pre> Factor that candidate. <pre><code>$ time sage -c "print ecm.factor(0x48ad7217d10f6c7b1a3db836d38aa3972999115f38a6b3d176fc660941aa5c882d2528ec1fc27da7610e7ee3d7dd84367c380259e0386224c2c46aa2a5eb2a0)" [2, 2, 2, 2, 2, 3, 353, 193243, 1540830719, 9263081209, 103633959617085683, 151389566295160172521, 283135469779419532841, 572987990320782777757565685333349772719941819448953457732874126833] real 0m5.837s user 0m5.648s sys 0m0.214s</code></pre> Now pull out the <code>r</code> component of the ECDSA signature for that index, and convert it from hex to base 10. <pre><code>$ jq '.r' ../sig_data/sig_data_847.json "0x30e2ce20a8140177a31a66763d85f431acc9790dd050ffc22ed5d454cdfbbb67" $ python -c "print 0x30e2ce20a8140177a31a66763d85f431acc9790dd050ffc22ed5d454cdfbbb67" 22111746808803128586382711090186612204136854333384650261207856620766542674791</code></pre> Now run the <code>enum</code> tool to recover the nonce. <pre><code>$ ./enum Usage: ./enum <jobs_num> <jobs_id> <target_base_10> space delimited flat list of factors in base ten</code></pre> The <code><jobs_num></code> and <code><jobs_id></code> arguments are to ease parallel execution; read the source code. But for a single core, pass them as <code>1 0</code>. <pre><code>$ ./enum 1 0 22111746808803128586382711090186612204136854333384650261207856620766542674791 2 2 2 2 2 3 353 193243 1540830719 9263081209 103633959617085683 151389566295160172521 2831354697794195 32841 572987990320782777757565685333349772719941819448953457732874126833 INFO:target:30E2CE20A8140177A31A66763D85F431ACC9790DD050FFC22ED5D454CDFBBB67 INFO:found:31A52C4960857E6D2F7AD82BAC7D55CE6CC9AD13B959F069002B6A949EA6A048 INFO:tests:7879</code></pre> where <code>221..791</code> is the base-10 <code>r</code> component of the ECDSA signature, and <code>2 2 .. 572..833</code> is the full list of blinded nonce factors. In the output: <ul> <li><code>INFO:target:<hex></code> is the hex form of base-10 target input (ECDSA <code>r</code> component).</li> <li><code>INFO:found:<hex></code> is the hex form of the recovered ECDSA nonce.</li> <li><code>INFO:tests:<num></code> is the number of tested nonce candidates (scalar multiplications).</li> </ul> We can see this successfully recovered the nonce (hence long term ECDSA private key) correctly: <pre><code>$ jq '.k' ../sig_data/sig_data_847.json "0x31a52c4960857e6d2f7ad82bac7d55ce6cc9ad13b959f069002b6a949ea6a048"</code></pre>

GNSS tomography data for assimilation into the Weather Research and Forecasting model

The data set contains GNSS troposphere tomography estimations of 3D wet refractivity fields for a part of Central Europe (mostly Germany and Czech Republic), for the period of 29 May–14 June 2013 when heavy-precipitation events were observed. The refractivity fields were estimated using two different GNSS tomography models: ATom software package (https://github.com/GregorMoeller/ATom) developed at TU Wien, and the TOMO2 model (Rohm and Bosy, 2011; Rohm et al., 2014; Trzcina and Rohm, 2019) developed at the Wrocław University of Environmental and Life Sciences. Further description of the GNSS tomography processing can be found in the paper by Hanna et al. (2019).

Data from the ATLASM5 Research Station from 13-07 to 27-08 and 19-09 to 20-11, 2017

PI_NAME = Abdelwahid MELLOUKI INSTITUTE = CNRS-ICARE EMAIL=mellouki@cnrs-orleans.fr ADDRESS=1C Av. de la Recherche Scientifique, CS 50060, 45071 Orl¨¦ans cedex 2 TITLE = Data from the ATLASM5 Research Station from 13-07 to 27-08 and 19-09 to 20-11, 2017 DATA_CATEGORY=Field_ATLAS Project=MARSU TYPE_OF_DATA= FIELD MEASUREMENT STATUS_OF_FILE=FINAL VERSION=1.0 PLATFORM=ATLAS NAME_OF_PLATFORM=MARSU DESCRIPTION=https://marsu-h2020.org/ Time Range=since 2017-07-14 00:00:00 to 2017-08-14 UTC             and  2017-09-10 00:00:00 to 2017-11-20 UTC

opencc-by-4.0Feb 2020View details →

Payment Methods -research

Consumer behavior, payment methods, payment technologies, applications, nfc, rfid, POI, Point of interaction

opencc-by-4.0May 2018View details →

Curated Research on Network Traffic Analysis

With the NTA Database we aim to collect relevant information about the research in network traffic analysis conducted during the last years. To this end, we have curated related papers from journals and conferences and stored the extracted data in JSON files.

opencc-by-4.0Nov 2019View details →

Legal Aspects of Research Data

A video introduction to the legal aspects of research data management.  Find out more about research data and open data training from: <a href="https://discipline-workshops.com/">https://discipline-workshops.com/</a>

opencc-by-4.0Feb 2020View details →

Data from Open Research Data: SNSF monitoring report 2017-2018

This file collection is part of Open Research Data: SNSF monitoring report 2017-2018 (doi 10.5281/zenodo.3618123). This report gives a first overview on the research data management practices adopted by researchers since the introduction of the SNSF Open Research Data Policy in 2017.  Please cite this data collection as: Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Data from Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618209) Further information is given in the corresponding monitoring report Milzow, Katrin; von Arx, Martin; Sommer, Cornélia; Cahenzli, Julia and Perini, Lionel (February 2020). Open Research Data: SNSF monitoring report 2017-2018. Zenodo (10.5281/zenodo.3618123)   Contact Swiss National Science Foundation (SNSF) Open Research Data group E-mail: ord@snf.ch

opencc-by-4.0Feb 2021View details →

Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregives and researchers

Nine animation videos used in the questionnaire described in the articles "Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome" (<a href="https://doi.org/10.1177%2F1545968321989331">https://doi.org/10.1177/1545968321989331</a>) and "Brain-Computer Interfaces for communication: preferences of individuals with locked-in syndrome, caregivers and researchers" (<a href="https://doi.org/10.1080/17483107.2021.1958932">https://doi.org/10.1080/17483107.2021.1958932</a>). Video animations were designed and produced by Merel Horsmeier.