Find research datasets worth reusing

openCustomJan 2020View details →

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2011 through 31-Dec-2011

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2011 through 31-Dec-2011. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

openCustomJan 2020View details →

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2015 through 31-Dec-2015

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2015 through 31-Dec-2015. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

openCC (other)Jan 2020View details →

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2016 through 31-Dec-2016

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2016 through 31-Dec-2016. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

openCC (other)Jan 2020View details →

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2017 through 31-Dec-2017

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2017 through 31-Dec-2017. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

openCC (other)May 2021View details →

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2018 through 31-Dec-2018

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2018 through 31-Dec-2018. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

openCC (other)May 2021View details →

Continuous salinity, temperature and depth measurements from moored hydrographic data loggers deployed at GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2019 through 31-Dec-2019

Conductivity, temperature and pressure were measured continuously at Georgia Coastal Ecosystems LTER sampling location GCE2_Hydro (Four Mile Island, Georgia) from 01-Jan-2019 through 31-Dec-2019. Observations were logged at 30 minute intervals by moored Sea-Bird Electronics MicroCAT 37-SM data loggers and downloaded approximately semi-monthly. Salinity, depth and sigma-t (density anomaly) were calculated from the measured parameters using standard UNESCO algorithms. This data set was collected as part of the GCE-LTER Project continuous salinity, temperature and water level monitoring program.

openCC (other)Apr 2022View details →

Precipitation data from four locations within the Tromble Weir experimental watershed, located at the Jornada Basin LTER site, 2010-ongoing

This data package contains one-minute resolution precipitation data from tipping bucket rain gauges (Texas Electronics) at four locations in the Tromble Weir watershed area of the Jornada Basin in southern New Mexico, USA. These rain gauges are used to help quantify the water balance across the small experimental watershed. Values have been used to investigate groundwater recharge, soil infiltration rates, and as forcings for hydrologic models. This is an ongoing dataset that will be updated annually.

openCC (other)Nov 2023View details →

PIE LTER Year 2022 15-minute interval water quality measurements of water column temperature, salinity, oxygen, pH, and depth at four sites in the Plum Island Estuary.

Four YSI EXO2 water quality sondes were deployed from June 2022 to October 2022 at four sites in the Plum Island Estuary. One was at the mouth of the sound at the Ipswich Bay Yacht Club, one in the Rowley River, and two in the Parker River. The sondes measured water column temperature, salinity, oxygen, pH, and depth in 15-minute intervals.

opencc-zeroNov 2021View details →

Regularized quantum periods for four-dimensional Fano manifolds

The database smooth_fano_4 This is a database of regularized quantum periods for four-dimensional Fano manifolds. The database will be updated as new four-dimensional Fano manifolds are discovered and new regularized quantum periods computed. Each entry in the database is a key-value record with keys and values as described in the paper [CK2021]. If you make use of this data, please cite that paper and the DOI for this data: doi:10.5281/zenodo.5708307 Names The database describes Fano varieties via names, as follows: <table> <caption>Names of Fano manifolds</caption> <thead> <tr> <th scope="col">Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>P1</td> <td>one-dimensional projective space</td> </tr> <tr> <td>P2</td> <td>two-dimensional projective space</td> </tr> <tr> <td>dP(k)</td> <td>the del Pezzo surface of degree k given by the blow-up of P2 in 9-k points</td> </tr> <tr> <td>P3</td> <td>three-dimensional projective space</td> </tr> <tr> <td>Q3</td> <td>a quadric hypersurface in four-dimensional projective space</td> </tr> <tr> <td>B(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 8k</td> </tr> <tr> <td>V(3,k)</td> <td>the three-dimensional Fano manifold of Picard rank 1, Fano index 1, and degree k</td> </tr> <tr> <td>MM(r,k)</td> <td>the k-th entry in the Mori-Mukai list of three-dimensional Fano manifolds of Picard rank r, ordered as in [CCGK2016]  </td> </tr> <tr> <td>P4</td> <td>four-dimensional projective space</td> </tr> <tr> <td>Q4</td> <td>a quadric hypersurface in five-dimensional projective space</td> </tr> <tr> <td>FI(4,k)</td> <td>the four-dimensional Fano manifold of Fano index 3 and degree 81k</td> </tr> <tr> <td>V(4,k)</td> <td>the four-dimensional Fano manifold of Picard rank 1, Fano index 2, and degree 16k</td> </tr> <tr> <td>MW(4,k)</td> <td>the k-th entry in Table 12.7 of [IP1999] of four-dimensional Fano manifolds of Fano index 2 and Picard rank greater than 1</td> </tr> <tr> <td>Obro(4,k)</td> <td>the k-th four-dimensional Fano toric manifold in Obro's classification [O2007]</td> </tr> <tr> <td>Str(k)</td> <td>the k-th Strangeway manifold in [CGKS2020]</td> </tr> <tr> <td>CKP(k)</td> <td>the k-th four-dimensional Fano toric complete intersection in [CKP2015]</td> </tr> <tr> <td>CKK(k)</td> <td>the k-th four-dimensional Fano quiver flag zero locus in Appendix B of [K2019]</td> </tr> </tbody> </table> A name of the form "S1 x S2", where S1 and S2 are names of Fano manifolds X1 and X2, refers to the product manifold X1 x X2. References [CCGK2016] Quantum periods for 3-dimensional Fano manifolds; Tom Coates, Alessio Corti, Sergey Galkin, Alexander M. Kasprzyk; Geometry and Topology 20 (2016), no. 1, 103-256. [CGKS2020] Quantum periods for certain four-dimensional Fano manifolds; Tom Coates, Sergey Galkin, Alexander M. Kasprzyk, Andrew Strangeway; Experimental Math. 29 (2020), no. 2, 183-221. [CK2021] Databases of quantum periods for Fano manifolds; Tom Coates, Alexander M. Kasprzyk; 2021. [CKP2015] Four-dimensional Fano toric complete intersections; Tom Coates, Alexander M. Kasprzyk, Thomas Prince; Proc. Royal Society A 471 (2015), no. 2175, 20140704, 14. [IP1999] Fano varieties; V.A. Iskovskikh, Yu. G. Prokhorov; Encyclopaedia Math. Sci. vol. 47, Springer, Berlin, 1999, 1-247. [K2019] Four-dimensional Fano quiver flag zero loci; Elana Kalashnikov; Proc. Royal Society A 275 (2019), no. 2225, 20180791, 23.  [O2007] An algorithm for the classification of smooth Fano polytopes; Mikkel Obro; arXiv:0704.0049 [math.CO]; 2007.

opencc-by-4.0Feb 2022View details →

Tree ring width chronologies of four Pinaceae species in boreal forests in Yakutia in 2018

Tree cores and discs were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). The samples were dried, sanded, digitized and further processed by identifying the ring layers end exporting the tree ring width for each year. The site chronologies were established by cross-dating all samples to each other, which helped coping with small ring sizes but especially with missing rings, and frost rings. We processed samples of four species, Larix gmelinii (LAGM), Picea obovata (PIOB), Pinus sylvestris (PISY) and Pinus sibirica (PISI). These were recorded at a variety of locations: <ul> <li>LAGM from Lake Khamra sites EN18079, -80, -81, -83 (N59.974919° E112.958985°, N59.977106° E112.961379°, N59.970583° E112.987096°, N59.974714° E113.002874°)</li> <li>PIOB from Lake Khamra sites EN18079, -81, -83 (59.974919° E112.958985°, 59.970583° E112.987096°, 59.974714° E113.002874°)</li> <li>PISI from Lake Khamra site EN18080 (N59.977106° E112.961379°)</li> <li>PISY from different sites between EN18061 (N62.076376° E129.618586°) and EN18077 (N61.892568° E114.288623°)</li> </ul> Data format The data consists of one file in dendrochronological TUCSON format without header for each of the four tree species. Additional information This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817 and an extension to: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Luidmila A; Zakharov, Evgenii S (2020): Individual tree and tall shrub partial above-ground biomass of central Chukotka in 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923784 Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019

opencc-by-4.0Feb 2022View details →

Individual tree aboveground biomass of four Pinaceae species in boreal forests in Yakutia in 2018

Samples to estimate aboveground tree biomass for four boreal forest species (Larix gmelinii, Picea obovata, Pinus sylvestris, Pinus sibirica) were collected during fieldwork in Yakutia in 2018 by scientists from Alfred Wegener Institute (AWI), Helmholtz Centre for Polar and Marine Research and University of Potsdam, Germany, The Institute for Biological problems of the Cryolithozone, Russian Academy of Sciences, Siberian branch, and The Institute of Natural Sciences, North-Eastern Federal University of Yakutsk, Yakutsk, Russia (Kruse et al., 2019). From each of the visited site, three living trees (a small, a medium-sized and the talles tree) per each site were cut down after estimating the quantity of the different types to be sampled, namely branches, needles, cones, making up the tree. Further, to estimate the stem weight, tree discs were taken. The discs were taken at the base of a tree (0 cm, disc A), breast height (130 cm, disc B) and top/close to the top of a tree (260 cm, disc C). If the tree was small with <1.3 m, its stem is included as woody biomass in the branch sample. To estimate each tree's stem biomass, the stem was assumed to have a cone shape. Dead trees were also sampled, if present. All harvested samples were weighed fresh in the field and subsampled. The dry weight of all subsamples was recorded after oven drying (60 °C, 48 h for needle and branch samples, up to one week for tree stem discs). A detailed protocol for total tree and shrub AGB estimation can be found in Shevtsova, et al. (2020). Data format The data consists of one table for each of the four species. The columns (N=13) contain the follwoing information: 1. TreeDataBaseID -> unique Tree Data Base identifier of the individual 2. Site     -> Sampling site name 3. SampleID -> Field name given to the individual 4. Species    -> Species name 5. Height_cm -> Height of the tree individual in cm 6. Vitality -> Estimate of the vitality state in 6 levels, ++ very good, + good, 0 mediocre, - bad, -- very bad, dead 7. NeedleWeight_g -> Dry weight of needles in g 8. StemWeight_g    -> Dry weight of the stem in g 9. BiomassBranchStatus -> 1 if branches are present and included in the biomass estimate or not 10. TotalWeightNonStem_g -> Dry weight of all parts but the stem, which are needles, branches and cones in g 11. DiameterBasal_cm -> Stem diameter at tree stem base (0 cm above ground) in cm 12. DiameterBreast_cm -> Stem diameter at breast height (130 cm above ground) in cm 13. CrownDiameter_cm -> Mean crown diameter in cm Additional information This data is linked to further information about individual trees and their sites as published in: van Geffen, Femke; Schulte, Luise; Geng, Rongwei; Heim, Birgit; Pestryakova, Luidmila A; Herzschuh, Ulrike; Kruse, Stefan (2021): Tree height and crown diameter during fieldwork expeditions that took place in 2018 in Central Yakutia and Chukotka, Siberia. PANGAEA, https://doi.pangaea.de/10.1594/PANGAEA.932817 Information about the expedition in 2018 in: Kruse, Stefan; Bolshiyanov, Dimitry Yu; Grigoriev, Mikhail N; Morgenstern, Anne; Pestryakova, Ludmila A; Tsibizov, Leonid; Udke, Annegret (2019): Russian-German Cooperation: Expeditions to Siberia in 2018. Berichte zur Polar- und Meeresforschung = Reports on Polar and Marine Research, 734, 257 pp, https://doi.org/10.2312/BzPM_0734_2019 Aboveground estimation protocol and further data in: Shevtsova, Iuliia; Kruse, Stefan; Herzschuh, Ulrike; Brieger, Frederic; Schulte, Luise; Stuenzi, Simone Maria; Pestryakova, Ludmila A; Zakharov, Evgenii S (2020): Total above-ground biomass of 39 vegetation sites of central Chukotka from 2018. PANGAEA, https://doi.org/10.1594/PANGAEA.923719

opencc-by-4.0Jul 2024View details →

Data from Davison et al. (2024) Changes in Danish bird communities over four decades of climate and land-use change

Environmental and biodiversity data associated with the article: Davison, C. W., Rahbek, C., & Morueta-Holme, N. (2024) Changes in Danish bird communities over four decades of climate and land-use change. Oikos. https://doi.org/10.1111/oik.10697 Data on local bird species richness, functional diversity, temporal and spatial turnover (beta diversity), abundance, and biomass at volunteer led survey routes across Denmark. Matched habitat data (from volunteers) and historical climate data (E-OBS). Bird observations are a subset of the Common Bird Monitoring programme (DOF – BirdLife Denmark) that include routes surveyed in the summer season, spanning ≥10 years, and with full GPS coordinates. This excel document contains all of the derived (and anomysied) data used in the final analyses and includes metadata describing the variables. Climate and trait data were obtained from open-access databases (see references). Metadata is included in the excel file. <ul> <li>Danish Common Bird Monitoring programme – Eskildsen, D. P., Vikstrøm, T., & Jørgensen, M. F. (2021). Overvågning af de almindelige fuglearter i Danmark 1975-2020. Dansk Ornitologisk Forening.</li> <li>E-OBS European gridded climate data – Haylock, M. R., Hofstra, N., Klein Tank, A. M. G., Klok, E. J., Jones, P. D., & New, M. (2008). A European daily high-resolution gridded data set of surface temperature and precipitation for 1950-2006. Journal of Geophysical Research Atmospheres, 113(20). https://doi.org/10.1029/2008JD010201</li> <li>AVONET bird traits data – Tobias, J. A., Sheard, C., Pigot, A. L., Devenish, A. J. M., Yang, J., Neate-Clegg, M. H. C., Alioravainen, N., Weeks, T. L., Barber, R. A., Walkden, P. A., MacGregor, H. E. A., Jones, S. E. I., Vincent, C., Phillips, A. G., Marples, N. M., Montaño-Centellas, F., Leandro-Silva, V., Claramunt, S., Darski, B., … Schleunning, M. (2022). AVONET: morphological, ecological and geographical data for all birds. Ecology Letters, 25(3), 581–597. https://doi.org/10.1111/ele.13898</li> </ul>

opencc-by-nc-sa-4.0Sep 2024View details →

Household surveys in four informal settlements in Abidjan (Côte d'Ivoire) and Nairobi (Kenya)

Description: Latest release of data (anonymized) collected in informal settlements in Côte d'Ivoire and Kenya during my PhD thesis, with the respective metadata (questionnaire files). Please note that some data (geolocation, specific age of participant, and health facilities used) have been ommitted due to personal data protection concerns. Includes: Data (CSV), questonnaires (XLS), and Jupyter notebooks summarizing the data (using Python). Ethical clearance: We obtained ethical clearance in Switzerland from EPFL’s HREC (decision n° 068-2020), in Kenya from KEMRI (KEMRI/RES/7/3/1) and the National Commission for Science, Technology & Innovation (NACOSTI/P/21/10921), and in Côte d’Ivoire from the National Health and Life Sciences Ethics Committee (Comité National d’Éthique des Sciences de la Vie et de la Santé, ref. n° 005-22/MSHPCMU/CNESVS-km). Citation: Pessoa Colombo V. Relating health benefits of water, sanitation, and hygiene services with the context of urban informal settlements: lessons from Côte d'Ivoire and Kenya. PhD thesis. EPFL: Lausanne. 2023. https://doi.org/10.5075/epfl-thesis-10143

opencc-by-4.0Jan 2023View details →

Data from: Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide

Summary This dataset accompanies the publication "Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide" published in Zoo Biology. It contains anonymised data from 540 captive flamingo populations, and includes the four species: Phoeniconaias minor, Phoenicopterus chilensis, Phoenicopterus roseus and Phoenicopterus ruber. Data were sourced from the Zoological Information Management System (ZIMS), operated by Species360 (https://www.species360.org/). ZIMS is the largest real-time database of comprehensive and standardized information spanning more than 1,200 zoological collections globally, and provides the number of institutions currently managing each flamingo species and both their current and historic population sizes. These data were used to investigate the relationship between reproductive success and both flock size, and structure, on a global scale. This dataset also contains climatic data provided by WorldClim, which were used to assess the influence of climatic variables on captive flamingo reproductive success globally. The WorldClim database averages 19 different climatic variables derived from monthly temperature and rainfall values at a 1 km spatial resolution for the period 1970-2000. Using geographic coordinates (latitude and longitude) we calculated several climatic metrics for each institution.    Description of the Dataset One file is provided for each species (P. minor, P. chilensis, P. roseus and P. ruber) as a csv file. Each file contains the following 15 columns: <ul> <li>Institution Code: An anonymous code used to identify individual zoological institutions.       </li> <li>Country: The country where the institution is located.</li> <li>Year: Current year (t).</li> <li>Flock Size: Flock size in year t.</li> <li>Males: The number of males in the flock in year t.           </li> <li>Females: The number of females in the flock in year t.</li> <li>Unsexed: The number of unsexed individuals in the flock in year t.</li> <li>Proportion of Females: The proportion of the flock made up of female individuals in year t.        </li> <li>Proportion of Unsexed: The proportion of the flock made up of unsexed individuals in year t.</li> <li>Hatches: Number of birds hatched in year t.</li> <li>Proportion of Additions: The proportion of the flock in year t made up of additions from year t-1 (not including new birds hatched into the flock).</li> <li>MAP: Mean annual precipitation (mm).</li> <li>MAT: Mean annual temperature (°C).</li> <li>MAP Var: Mean annual variation in precipitation (MAP coefficient of variation).</li> <li>MAT Var: Mean annual variation in temperature (MAT standard deviation).</li> </ul> Note: Mean Annual Temperature (MAT) is provided by WorldClim as °C multiplied by 10, and similarly mean annual variation in temperature as MAT standard deviation multiplied by 100. In the corresponding publication, both were divided (by 10 and 100 respectively) prior to modelling to avoid confusion in the units used.   Acknowledgements We acknowledge and thank all Species360 member institutions for their continued support and data input. The research which data refers to was funded by the Irish Research Council Laureate Awards 2017/2018 IRCLA/2017/60 to Y.M.B. Additionally, S.Q.S. received funding from the International Max Planck Research School for Organismal Biology. The Species360 Conservation Science Alliance would like to thank their sponsors: the World Association of Zoos and Aquariums, Wildlife Reserves of Singapore, and Copenhagen Zoo.    Disclaimer Despite our best efforts at screening the data for errors and inconsistencies, some information could be erroneous. Similarly, data contained within ZIMS are based on submitted records from individual institutions, and are not subject to editorial verification, potentially permitting errors or failure to update species holdings etc. Despite this, ZIMS represents the only global database of zoo collection composition records, and as a result, is used by the IUCN, Convention on International Trade in Endangered Species (CITES), the Wildlife Trade Monitoring Network (TRAFFIC), United States Fish and Wildlife Service (USFWS) and Department for Environment, Food and Rural Affairs (DEFRA).    Credit If you use this dataset, please cite the corresponding publication: Mooney, A., Teare, J. A., Staerk, J.,Smeele, S. Q., Rose, P., Edell, R. H., King, C. E., Conrad, L., & Buckley, Y. M. (2023). Flock size and structure influence reproductive success in four species of flamingo in 540 captive populations worldwide. Zoo Biology, 1–14. <a href="https://doi.org/10.1002/zoo.21753">https://doi.org/10.1002/zoo.21753</a>

opencc-by-4.0Feb 2023View details →

Beyond the Four-Level Model: Dark and Hot States in Quantum Dots Degrade Photonic Entanglement

Dataset for "Beyond the four-level model: Dark and hot states in quantum dots degrade photonic entanglement" Nano Lett. 2023, 23, 4, 1409–1415 Publication Date: February 6, 2023 <a href="https://doi.org/10.1021/acs.nanolett.2c04734">https://doi.org/10.1021/acs.nanolett.2c04734</a> A description of the dataset is found in the readme.md file (markdown markup language). Data reuse Please cite B.U. Lehner et al., Nano Lett. 2023, 23, 4, 1409–1415 (2023) in publications that reuse this data and if possible inform the corresponding authors.

opencc-by-4.0Jan 2019View details →

Planet Four Data Catalog

This is the data catalog for the paper: <a href="https://www.sciencedirect.com/science/article/abs/pii/S0019103518301039">Planet Four: Probing springtime winds on Mars by mapping the southern polar CO2 jet deposits</a> The catalog can be automatically retrieved from here using the Python package <a href="https://pypi.org/project/p4tools/">p4tools</a>

openCC0Mar 2023View details →

Submersed aquatic vegetation community composition in the Sacramento-San Joaquin Delta integrated across four surveys

Submersed aquatic vegetation (SAV) has become widespread in the Sacramento-San Joaquin Delta (Delta), and the diverse SAV assemblage is dominated by non-native species. SAV negatively impacts this estuarine ecosystem by impeding flows needed for water delivery and flood control, degrading habitat needed by native species, increasing breeding habitat for disease-vectoring mosquitoes, harboring non-native predatory fish, and hindering water recreation. The goal of this published integrated dataset is to facilitate study of these impacts. This data set includes four surveys conducted in the region during 2008-2021. Two of these are short-term special studies that have been completed, and two are ongoing long-term annual surveys. The nearshore survey of SAV and largemouth bass was conducted by the University of California-Davis (UC-Davis) at sites across the Delta during 2008-2010. The Aquatic Weed Control Action was completed by the Department of Water Resources as part of the Delta Smelt Resiliency Strategy and included monthly surveys of four sites during 2017-2018. The ongoing survey of Franks Tract is conducted annually by the SePRO corporation and the Division of Boating and Waterways, and available data are from 2014-2021. The ongoing annual survey conducted by the UC-Davis Center for Spatial Technologies and Remote Sensing covers many areas of the Delta and spans 2007-2008 and 2014-2021. Additional data from these ongoing surveys and data from other surveys will be added in subsequent versions of this data set.

openCC (other)Apr 2020View details →

Gloeotrichia echinulata density at four nearshore sites in Lake Sunapee, NH, USA from 2005-2016

Surface densities of Gloeotrichia echinulata, a filamentous colonial cyanobacterium, were collected at four nearshore sites in Lake Sunapee, NH, USA from 2005-2016. Lake Sunapee is a large (1667 hectare surface area), oligotrophic, north temperate lake used for drinking water and recreation with a primarily forested watershed and a moderately-developed shoreline. Samples were collected approximately weekly at Herrick Cove South and Newbury mid-July to September in 2005 and at Herrick Cove South late June to mid-September in 2006. Data collection at Herrick Cove South, Newbury, South of the Fells, and Sunapee Harbor occurred from mid-June to mid-September in 2007-2008 and from May to October in 2009-2016.

openCC (other)Jan 2020View details →

Vegetation indices calculated from canopy reflectance spectra at four sites along Imnavait Creek, AK during the 2008-2010 growing seasons.

A spectrophotometer was used to scan the canopy vegetation at four sites along Imnavait Creek in the Kuparuk Watershed near Toolik Lake LTER, Alaska. The resulting reflectance spectra were used to calculate average vegetation indices for each site and collection day.