Find research datasets worth reusing

opencc-byNov 2020View details →

Exploits: All registered in exploit-db, from January 2019 to October 2020

This dataset contains all exploits registered on the <a href="https://www.exploit-db.com/">exploit-db website</a>, from 02 January 2019 to 06 November 2020. 2,665 exploits were found in this time range, and stored in CSV file. The CSV fields are as follows: <ul> <li>Id: exploit-db identifier.</li> <li>Link Exploit: Link to download exploit code source.</li> <li>App: Link to download the application in case the exploit is an application, and if it isn't an app the field is empty.</li> <li>Date: Publication date on the website.</li> <li>Verification: Exploit verfication by website. </li> <li>Title: Exploit title.</li> <li>Type: Exploit type. Could be Local, Remote and Webapp.</li> <li>Platform: Platform on which the exploit is based on.</li> <li>Author: Name of exploit author.</li> </ul>

opencc-by-4.0Oct 2020View details →

Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil

Fábio Marcelo Breunig¹ (author) ¹ Universidade Federal de Santa Maria, Departamento de Engenharia Florestal, Frederico Westphalen, Rio Grande do Sul, Brasil. E-mail: breunig@ufsm.br Title:   Unmanned Aerial Vehicle (UAV) data acquired over an experimental area of the UFSM campus Frederico Westphalen, at October 20, 2020, Rio Grande do Sul, Brazil Data description:   The data were acquired from an aerial survey conducted with an Unmanned Aerial Vehicle (UAV, also Drone) covering an experimental area of the Federal University of Santa Maria – UFSM in the municipality of Frederico Westphalen, in the Rio Grande do Sul, Brazil (Figure 1). The climate of the region is subtropical (Cfa in the Köppen-Geiger classification) with an average annual temperature of 18 °C and annual precipitation of 1919 mm (<a href="https://www.sciencedirect.com/science/article/pii/S0303243419309481#bib0015">Alvares et al., 2013</a>). The rainfall is well distributed throughout the year.   Figure 1. Location of the site of data acquisition. Based on Google Earth Pro scenes. The KML and KMZ are appended to the files. UAV and camera settings for the acquisition (Specifications Table):   Parameters Specification/value Date (YYYYMMDD): 20201020 Time of day (BRT = -3) 11 h a.m. UAV – Drone - Camera Matrice 100 X3 Fly high (meters above ground) 80 m View angle 90° automatic mode. Sky conditions ( x ) Clear sky (  ) Low cloud coverage (some clouds) (  ) Completely cloudy Wind condition ( x ) no wind (  ) Low speed (  ) High speed wind Approximate data acquisition duration 16 minutes Total of photographs acquired 224 Across track coverage 80% Cross-track coverage 80% Fly planning software Pix4D Capture   For more information contact: Fábio Marcelo Breunig, <a href="mailto:breunig@ufsm.br">breunig@ufsm.br</a> An example of the mosaic is showed (Figure 2), referring to a screen capture of Agisoft Metashape (Agisoft LLC, 11 Degtyarniy per., St. Petersburg, Russia, 191144) and, the workflow adopted.   Figure 2. The capture of an orthomosaic and processing workflow     References to the main project/publications:   Breunig, Fabio Marcelo. CONESAT – Monitoring the CONESUL using remote sensing data. Project. Federal University of Santa Maria, Campus of Frederico Westphalen. Brazil. Available at: <https://www.researchgate.net/project/CONESAT-Monitoring-the-CONESUL-using-remote-sensing-data>.   Breunig, Fabio Marcelo. Integration of multiscale remote sensing data in the precision agriculture and silviculture (in Portuguese: Integração de dados multiescala de sensoriamento remoto na agricultura e silvicultura de precisão). Project. National Council for Scientific and Technological Development (CNPq). Grant 113769/2018-0   Breunig, Fabio Marcelo. Combination of UAV, PlanetScope, Landsat and Sentinel-2 images to precision silviculture and agriculture in a subtropical region (in Portuguese: Combinação de imagens de VANT, PlanetScope, Landsat e Sentinal-2 para a silvicultura e agricultura de precisão em uma região subtropical). Project of the National Council for Scientific and Technological Development (CNPq). Grant 305084/2020-8   Acknowledgments:   This work was supported by the National Council for Scientific and Technological Development (CNPq) (Grants 113769/2018-0, 312081/2013-8, 478085/2013-3 and, 305084/2020-8) and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul  (Grant 23830.388.22048.19092016).   Other considerations   PS. A pdf file is also attached with this description   Declaration of Competing Interest   The author declares that he has no competing interests or personal relationships that have or could be perceived to have influenced the work reported in this report.   References associated:   Breunig, Fábio Marcelo (2017, July 7). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 7, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4327943 Alvares, Clayton Alcarde, José Luiz Stape, Paulo Cesar Sentelhas, José Leonardo De Moraes Gonçalves, and Gerd Sparovek, ‘Köppen’s Climate Classification Map for Brazil’, Meteorologische Zeitschrift, 22 (2013), 711–28 <https://doi.org/10.1127/0941-2948/2013/0507> Breunig, Fábio Marcelo (2019): UAV images acquired over the UFSM campus in Frederico Westphalen, RS, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897548 Breunig, Fábio Marcelo (2019): UAV derived orthomosaic over the “prainha” in the municipality of Iraí, Rio Grande do Sul, Brazil. Universidade Federal de Santa Maria, PANGAEA, https://doi.org/10.1594/PANGAEA.897909 Sestari, Geovane (2019): RPAS orthomosaic over the remnant of rainforest on UFSM/IFFar campus in the municipality of Frederico Westphalen, Rio Grande do Sul, Brazil. PANGAEA, https://doi.org/10.1594/PANGAEA.910114 Breunig, Fábio Marcelo  (2017, July 11). Unmanned Aerial Vehicle (UAV) data acquired over a subtropical forest area of the UFSM campus Frederico Westphalen, at July 11, 2017, Rio Grande do Sul, Brazil. Zenodo. http://doi.org/10.5281/zenodo.4328340

opencc-zeroOct 2019View details →

Data from "Removal of grazers alters the response of tundra soil carbon to warming and enhanced nitrogen availability", Ecological Monograps in October 2019

Here we present the data used in the manuscript "Removal of grazers alters the response of tundra soil carbon to warming and enhanced nitrogen availability", Ecological Monograps, Early view in October 2019 by H. Ylänne, E. Kaarlejärvi, M. Väisänen, M. K. Männistö, S. H. K. Ahonen, J. Olofsson & S. Stark. In this paper we studied, how five years of experimental warming and increased soil nitrogen availability interact with both long- and short-term differences in grazing intensity in shaping ecosystem carbon stocks and the processes underlying the changes. We used an over 50-year-old reindeer fence that separates a lightly grazed shrub-dominated tundra from a heavily grazed graminoid-dominated tundra, where the different grazing histories on the two sides of the fences have created different ecosystem states. In addition to the long-term grazing difference, we also established short-term grazer exclosures on the heavily grazed side of the fence to account for the effect of a sudden grazing cessation. This file includes data of ecosystem carbon stocks, soil properties, and fungal and bacterial copy numbers. It also provides data on development of the vegetation through the course of the experiment (2010-2014) and presents the activities of six extracellular enzymes measured on three occasions in 2013.

opencc-by-4.0Jun 2021View details →

TweetsCOV19 - Geolocation Data (Part 1, October 2019 - April 2020)

We provide a tab-separated values (tsv) of the TweetsCOV19 geolocation dataset. Each line contains tweet ID, latitude, longitude, country, state, county, city information separated by tab character ("\t").

opencc-zeroDec 2014View details →

Data from: "Diagnostic SNPs for inferring population structure in American mink (Neovison vison) identified through RAD sequencing" in Genomic Resources Notes accepted 1 October 2014 to 30 November 2014

The article documents the public availability of RAD sequencing data and generated SNPs for the American mink (Neovison vison). 224,095 polymorphic loci were identified from 14 mink from which primers were designed for a subset of 380 SNPs. The panel was tested on 211 mink. Fisher's F-statistics (Fis, FIT and FST) as well as observed (HO), expected (HE) and unbiased expected (uHE) heterozygosity was calculated for the SNPs and 194 SNPs was validated as being useful for population genetic studies.

opencc-zeroDec 2013View details →

Data from: "Genome-wide microsatellite marker development from next-generation sequencing of two non-model bat species impacted by wind turbine mortality: Lasiurus borealis and L. cinereus (Vespertilionidae)" in Genomic Resources Notes accepted 1 October 2013 to 30 November 2013

Tree-roosting bats in the genus Lasiurus are widespread, migratory species that have not been well characterized for population genetic diversity and structure due to a lack of genetic resources. Generating genetic resources in Lasiurus is made pressing by the need for conservation genetic assessments of demographic trends in this genus, which comprise a large percentage of bat mortalities at wind turbine sites across North America. We report on marker development from whole-genome Illumina sequencing of the red bat (Lasirus borealis) and the hoary bat (L. cinereus). We generated paired-end libraries for a single individual of each species, sequenced on the Illumina HiSeq platform. We mapped a total of 46.6 million reads to the Myotis lucifigus reference genome, and used bioinformatics searches to identify tends of thousands of simple sequence repeats (SSRs) distributed across the bat genome. We selected 48 candidate microsatellite loci to develop cross-species primer sequences for Lasiurus, assembled these into multiplex combinations, and tested for amplification and polymorphism levels in a sample of 23 individuals from each of L. borealis and L. cinereus. In total, we identified 42 highly polymorphic loci that could be robustly amplified and scored, the majority of which (39) were also combinable into highly multiplexed assays of 4-8 loci each. The combination of new genomic sequence assemblies, a large set of highly polymorphic microsatellite loci, and the ability to efficiently multiplex represents a significant contribution to the genetic resources available for population and comparative genetic studies of bats.

opencc-zeroDec 2013View details →

Data from: "Complete mitochondrial and partial nuclear genomes for the jack species Caranx ignobilis (Forsskål, 1775) and C. melampygus (Cuvier, 1833) (Perciformes:Carangidae) from the High Hawaiian Islands" in Genomic Resources Notes accepted 1 October 2013 – 30 November 2013

Complete mitochondrial and partial nuclear genomes for the jack species Caranx ignobilis (Forsskål, 1775) and C. melampygus (Cuvier, 1833) (Perciformes:Carangidae) from the High Hawaiian Islands are presented along with annotation and characterization of intragenomic single nucleotide polymorphism (SNPs) and indel variation.

FIGURE 6 A–F in Ptyctimous mites (Acari, Oribatida) from the Joint Russian-Vietnamese Biological Expedition (October 2013 – April 2014)

FIGURE 6 A–F. Apoplophora minuscula Niedbała, 2013: A, prodorsum, dorsal view; B, prodorsum, lateral view; C, opisthosoma, lateral view; D, ventral side of opisthosoma. Arphthicarus parasentus Niedbała, 2000: E, anterior part of notogaster, lateral view. Atropacarus (Hoplophorella) stilifer (Hammer, 1961): F, anterior part of notogaster, lateral view.

FIGURE 4 A–H in Ptyctimous mites (Acari, Oribatida) from the Joint Russian-Vietnamese Biological Expedition (October 2013 – April 2014)

FIGURE 4 A–H. Hoplophthiracarus clavatus Niedbała sp. nov.: A, prodorsum, dorsal view; B, prodorsum, lateral view; C, opisthosoma, lateral view; D, mentum of subcapitulum; E, left genitoaggenital plate: F, left anoadanal plate; G, trochanter and femur of leg I; H, tibia of leg IV.

FIGURE 3 A–D in Ptyctimous mites (Acari, Oribatida) from the Joint Russian-Vietnamese Biological Expedition (October 2013 – April 2014)

FIGURE 3 A–D. Acrotritia paragranulata Niedbała sp. nov.: A, prodorsum, dorsal view; B, prodorsum, lateral view; C, opisthosoma, lateral view; D, left genitoaggenital plate.

FIGURE 2 A–D in Ptyctimous mites (Acari, Oribatida) from the Joint Russian-Vietnamese Biological Expedition (October 2013 – April 2014)

FIGURE 2 A–D. Euphthiracarus (Parapocsia) medius Niedbała sp. nov.: A, prodorsum, dorsal view; B, prodorsum, lateral view; C, opisthosoma, lateral view; D, left genitoaggenital and anoadanal plates.

FIGURE 1 A–F in Ptyctimous mites (Acari, Oribatida) from the Joint Russian-Vietnamese Biological Expedition (October 2013 – April 2014)

FIGURE 1 A–F. Euphthiracarus (Euphthiracarus) quasitakahashii Niedbała sp. nov.: A, prodorsum, dorsal view; B, prodorsum, lateral view; C, opisthosoma, lateral view; D, mentum of subcapitulum; E, left genitoaggenital plate; F, anoadanal region.

opencc-by-4.0Dec 2023View details →

Atmospheric Surface Flux Station #50 measurements (level 2 Processed), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023

Processed (Level 2) measurements and derived variables from the Atmospheric Surface Flux Station #50 (ASFS-50) deployed at the Avery Picnic site (38°58.3455' N, 106°59.8113' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from October 2021 through June 2023. The ASFS measured variables of the surface energy budget, momentum flux, near-surface meteorology, and soil properties. Measurements of high-resolution 3-dimensional winds were observed at a nominal height of 4.6 m (depending on snow depth). Measurements of upwelling broadband radiation and meteorology observed from a nominal height of 2.9 m. The measurements are included in three netCDF files per day. The "sledmet" files are comprised of 1-min averages of measured and derived variables, including near-surface meteorology, surface skin temperature, snow depth, radiative fluxes, and conductive fluxes. The "sledseb" files are 10-min averages of the same variables as in the 1-min files and also include calculations of turbulent sensible and latent heat fluxes, momentum flux, and associated diagnostics, surface stress, and Monin-Obukhov parameters using both eddy covariance and bulk methodologies, all valid for the 10-min intervals. Both of these file types also contain a "_qc" variable paired with each measurement variable, or family of variables, that is a temporally-matched quality control code: 0 = good data, 1 = caution (data may be suspect), 2 = bad data, and -1 = missing (no data was collected). The "10hz" files include 3-dimensional winds and gas densities of water vapor and carbon dioxide that are quality-controlled, aggregated to a regular 10-Hz temporal grid, and (for winds) rotated into the earth coordinate frame. A detailed documentation of the measurement conditions, the processing steps taken to construct this data set, and other caveats and uncertainties will be provided in an accompanying published data manuscript. Note on update: v2_1 update provides double rotation ("dbl") turbulent fluxes in addition to planar fit ("pf"), as well as corrects a rotation problem with the v2 data that primarily affects the momentum fluxes. v2_1 only includes updates for the sledseb file set: for sledmet and sledwind10hz, continue to refer to v2 data set.

opencc-by-4.0Sep 2024View details →

Sea ice bulk density estimates during the MOSAiC freezing season from October 2019 to April 2020

<h2>MOSAiC: the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition.</h2> Assuming hydrostatic equilibrium, we integrated and adjusted sea ice thickness and snow depth data from an IMB array (comprising 15 buoys), high-resolution along-track freeboard data from airborne laser scanning (ALS) and the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) measurements, as well as snow bulk density data from snow pits, to estimate the ice bulk density (IBD) within the MOSAiC DN (defined as the area within 50 km of Polarstern) during the MOSAiC freezing season from late October 2019 to late April 2020 (Regional buoy sites). Additionally, we also provided core-based IBD data from the MOSAiC Main Coring Sites (MCS), derived using the weighing method (MCS-FYI and MCS-SYI). Due to the challenges in reconciling spatially non-overlapping observations, we implemented two alternative approaches—Regional Case 1 and Regional Case 2—to further upscale the hydrostatic equilibrium-based IBD retrievals within the MOSAiC DN.   <h2>Variable Details:</h2> Note that all density results are in kilograms per cubic metre. <blockquote> [Time]: covering the period from 5 October 2019 to 30 April 2020 in the format yyyyMMdd. [Longitude] & [Latitude]: characterizing the daily position for the MOSAiC Central Observatory (CO). </blockquote>   <blockquote> [Reg_buoy_DN]: Estimated mean sea ice bulk density at the buoy siteswithin a radius of ~50 km from CO (DN scale). [Reg_buoy_DN_Unc1]: Uncertainty of [Reg_buoy_DN] resulting from the input parameters, calculated using the Gaussian error propagation method. [Reg_buoy_DN_Unc2]: Uncertainty of [Reg_buoy_DN] resulting from the introduced empirical adjustment coefficient, characterised using the mean absolute difference of multiple freeboard estimates. </blockquote>   <blockquote> [Reg_buoy_Lsite]: Estimated mean sea ice bulk density at the buoy sites within a radius of ~25 km from CO (Lsite scale). [Reg_buoy_Lsite_Unc1]: Uncertainty of [Reg_buoy_Lsite] resulting from the input parameters, calculated using the Gaussian error propagation method. [Reg_buoy_Lsite_Unc2]: Uncertainty of [Reg_buoy_Lsite] resulting from the introduced empirical adjustment coefficient, characterized using the mean absolute difference of multiple freeboard estimates. </blockquote>   <blockquote> [Reg_buoy_DL]: Mean value of [Reg_buoy_DN] and [Reg_buoy_Lsite], representing regional buoy estimates of IBD within the MOSAiC DN. [Reg_buoy_DL_Unc1]: Uncertainty of [Reg_buoy_DL] resulting from the input parameters, calculated using the Gaussian error propagation method. [Reg_buoy_DL_Unc2]: Uncertainty of [Reg_buoy_DL] resulting from the introduced empirical adjustment coefficient, characterized using the mean absolute difference of multiple freeboard estimates. </blockquote>   <blockquote> [Reg_case1_DN]: Mean IBD value for typical ice conditions at the DN scale, calculated using the integration of multi-source observations. [Reg_case2_DN]: Mean IBD value for typical ice conditions at the DN scale, calculated using initial values from large-scale observations and trend extrapolation. </blockquote>   <blockquote> [Loc_MCS_FYI]: Local-scale core-based bulk density derived from the main first-year coring sites (MCS-FYI) during MOSAiC. [Loc_MCS_SYI]: Local-scale core-based bulk density derived from the main second-year coring sites (MCS-SYI) during MOSAiC. The density of these ice cores was measured in a freezing laboratory at an ambient temperature of –15 °C using the hydrostatic weighing method, achieving a relatively low uncertainty of only 0.2% (Pustogvar and Kulyakhtin, 2016). Pustogvar, A. and Kulyakhtin, A.: Sea ice density measurements. Methods and uncertainties, Cold Regions Science and Technology, 131, 46-52, https://doi.org/10.1016/j.coldregions.2016.09.001, 2016. </blockquote>   <h2>Relevant datasets:</h2> Sea ice mass balance buoys (IMBs): sea ice thickness and snow depth <blockquote> SIMBA buoy measurements are available from PANGAEA: https://doi.org/10.1594/PANGAEA.938244. SIMB buoy measurements are available from the Arctic Data Center: https://doi.org/10.18739/A20Z70Z01. Lei, R., Cheng, B., Hoppmann, M., and Zuo, G.: Snow depth and sea ice thickness derived from the measurements of SIMBA buoys deployed in the Arctic Ocean during the Legs 1a, 1, and 3 of the MOSAiC campaign in 2019-2020, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.938244, 2021. Perovich, D., Raphael, I., Moore, R., Clemens-Sewall, D., Polashenski, C., and Planck, C.: Measurements of ice mass balance and temperature from autonomous Seasonal Ice Mass Balance buoys in the Arctic Ocean, 2019-2020, Arctic Data Center [data set], https://doi.org/10.18739/A20Z70Z01, 2022. </blockquote>   Airborne laser scanning (ALS): sea ice total freeboard (sea ice freeboard + snow depth) <blockquote> Airborne laser scanning measurements during MOSAiC are available from PANGAEA: https://doi.org/10.1594/PANGAEA.950896. Hutter, N., Hendricks, S., Jutila, A., Birnbaum, G., von Albedyll, L., Ricker, R., and Haas, C.: Gridded segments of sea-ice or snow surface elevation and freeboard from helicopter-borne laser scanner during the MOSAiC expedition, version 1. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.950339, 2023. </blockquote>   Ice, Cloud, and land Elevation Satellite-2 (ICESat-2): sea ice total freeboard (sea ice freeboard + snow depth) <blockquote> ICESat-2 ATL10 total freeboard data (version 6, latest version) are available from NSIDC: https://doi.org/10.5067/ATLAS/ATL10.006. Kwok, R., Petty, A., Cunningham, G., Markus, T., Hancock, D., Ivanoff, A., Wimert, J., Bagnardi, M., and Kurtz, N.: ATLAS/ICESat-2 L3A Sea Ice Freeboard, Version 6, National Snow and Ice Data Center, Boulder, Colorado, USA [data set], https://doi.org/10.5067/ATLAS/ATL10.006, 2023.&ensp; </blockquote>   Snow pits: snow density <blockquote> Snow pit data collected during the MOSAiC expedition are available from PANGAEA: https://doi.org/10.1594/PANGAEA.940214. Macfarlane, A. R., Schneebeli, M., Dadic, R., Wagner, D. N., Arndt, S., Clemens-Sewall, D., Hämmerle, S., Hannula, H.-R., Jaggi, M., Kolabutin, N., Krampe, D., Lehning, M., Matero, I., Nicolaus, M., Oggier, M., Pirazzini, R., Polashenski, C., Raphael, I., Regnery, J., Shimanchuck, E., Smith, M. M., and Tavri, A.: Snowpit snow density cutter profiles measured during the MOSAiC expedition, PANGAEA [data set], https://doi.org/10.1594/PANGAEA.940214, 2022. </blockquote>   Transects: sea ice thickness and snow depth <blockquote> Transect data collected during MOSAiC are available from PANGAEA: https://doi.org/10.1594/PANGAEA.937781. Itkin, P., Webster, M., Hendricks, S., Oggier, M., Jaggi, M., Ricker, R., Arndt, S., Divine, D. V., von Albedyll, L., Raphael, I., Rohde, J., and Liston, G. E.: Magnaprobe snow and melt pond depth measurements from the 2019-2020 MOSAiC expedition. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.937781, 2021. </blockquote>   Ice cores: sea ice density <blockquote> Ice core data collected from the MOSAiC Main Coring Sites are available from PANGAEA: https://doi.org/10.1594/PANGAEA.956732 (MCS-FYI) and https://doi.org/10.1594/PANGAEA.959830 (MCS-SYI). Oggier, M., Salganik, E., Whitmore, L., Fong, A. A., Hoppe, C. J. M., Rember, R., Høyland, K. V., Divine, D. V., Gradinger, R., Fons, S. W., Abrahamsson, K., Aguilar-Islas, A. M., Angelopoulos, M., Arndt, S., Balmonte, J. P., Bozzato, D., Bowman, J. S., Castellani, G., Chamberlain, E., Creamean, J., D'Angelo, A., Damm, E., Dumitrascu, A., Eggers, S. L., Gardner, J., Grosfeld, L., Haapala, J., Immerz, A., Kolabutin, N., Lange, B. A., Lei, R., Marsay, C. M., Maus, S., Müller, O., Olsen, L. M., Nuibom, A., Ren, J., Rinke, A., Sheikin, I., Shimanchuk, E., Snoeijs-Leijonmalm, P., Spahic, S., Stefels, J., Torres-Valdés, S., Torstensson, A., Ulfsbo, A., Verdugo, J., Vortkamp, M., Wang, L., Webster, M., Wischnewski, L., and Granskog, M. A.: First-year sea-ice salinity, temperature, density, oxygen and hydrogen isotope composition from the main coring site (MCS-FYI) during MOSAiC legs 1 to 4 in 2019/2020. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.956732, 2023. Oggier, M., Salganik, E., Whitmore, L., Fong, A. A., Hoppe, C. J. M., Rember, R., Høyland, K. V., Gradinger, R., Divine, D. V., Fons, S. W., Abrahamsson, K., Aguilar-Islas, A. M., Angelopoulos, M., Arndt, S., Balmonte, J. P., Bozzato, D., Bowman, J. S., Castellani, G., Chamberlain, E., Creamean, J., D'Angelo, A., Damm, E., Dumitrascu, A., Eggers, L., Gardner, J., Grosfeld, L., Haapala, J., Immerz, A., Kolabutin, N., Lange, B. A., Lei, R., Marsay, C. M., Maus, S., Olsen, L. M., Müller, O., Nuibom, A., Ren, J., Rinke, A., Sheikin, I., Shimanchuk, E., Snoeijs-Leijonmalm, P., Spahic, S., Stefels, J., Torres-Valdés, S., Torstensson, A., Ulfsbo, A., Verdugo, J., Vortkamp, M., Wang, L., Webster, M., Wischnewski, L., and Granskog, M. A.: Second-year sea-ice salinity, temperature, density, oxygen and hydrogen isotope composition from the main coring site (MCS-SYI) during MOSAiC legs 1 to 4 in 2019/2020. PANGAEA [data set], https://doi.org/10.1594/PANGAEA.959830, 2023. </blockquote>

opencc-by-4.0Dec 2023View details →

Atmospheric Surface Flux Station #50 measurements (level 1 Raw), Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH), October 2021-June 2023

Raw (Level 1) measurements from the Atmospheric Surface Flux Station #50 (ASFS-50) deployed at the Avery Picnic site (38°58.3455' N, 106°59.8113' W) during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign near Gothic, Colorado, from October 2021 through June 2023. The ASFS measured variables comprising the surface energy budget, surface momentum flux, near-surface meteorology, and soil properties.  These measurements are included in three netCDF files per day. The "slow" files are for 1-minute averages of the measured variables, including near-surface meteorology, surface height change (due to accumulating/ablating snow), and upwelling and downwelling shortwave and longwave radiative fluxes. The "fast" files are for data at 20 Hz resolution including 3-dimensional wind, temperature, and gas concentrations of water vapor and carbon dioxide. These data are raw measurements with technical corrections applied but no quality assurance. A detailed documentation of the measurement will be provided in a forthcoming publication. For scientific purposes, we recommend using the Level 2 data files when they are available, as these will include full quality control as well as higher-order derived products.

opencc-by-4.0Nov 2021View details →

COVID-19 Seroprevalence in Ontario; March 27 - October 30, 2020

Results of population-based age stratified seroepidemiological investigation in Ontario, Canada

opencc-by-4.0Nov 2021View details →

Continous observations of chlorophyll fluorescence and photosynthesis at XTS site (October 2020- June 2021)

This is the dataset of continuous observations of chlorophyll fluorescence and photosynthesis at the Xiaotangshan (XTS, Beijing) site. The dominant species during the observation period was winter wheat, and the data was provided at an interval of half an hour.

opencc-by-4.0Dec 2021View details →

PM2.5 composition and VOCs species measured in Tianjin in July-October, 2018

This spreadsheet include the concentrations of water-soluble PM2.5 chemical species (sheet 1) and VOCs species (sheet 2) measured at NKUAQS site in July-October 2018 in Tianjin, China. The dataset was used to investigate changes in these species during tropical cyclone days with respect to normal days. Results were analyzed and presented in the paper entitled "Co-Occurrence of Surface O3, PM2.5 Pollution, and Tropical Cyclones in China" (<a href="https://doi.org/10.1029/2021JD036310">https://doi.org/10.1029/2021JD036310</a>).

opennotspecifiedDec 2021View details →

FIGURE 3. October 2004 in Dr Claude Massin (25/08/1948-04/09/2021), in Memoriam

FIGURE 3. October 2004. Dr Claude Massin coming back from a successful dive in the waters of Grande Comore, the Comores; Dr Yves Samyn on the front right side of the boat served as his dive buddy (see also Samyn et al. 2005; 2006 a, b). (courtesy of Dr Didier VandenSpiegel).