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Fig. 1 in Giardia duodenalis and Cryptosporidium occurrence in Australian sea lions (Neophoca cinerea) exposed to varied levels of human interaction
Fig. 1. (A) Western Australia sampling locations. Faecal samples were collected from West Australia Sea lion colonies on Beagle and North Fisherman Islands. Coastal settlements and human impacted camping locations within close proximity to Sea lion colonies are indicated. (B) South Australia sampling locations. Australian sea lion faecal samples were collected from South Australia colonies; Blefuscu, Lewis, Liguanea, Lilliput, Olive and West Waldegrave Islands. Coastal towns and camping areas within close proximity to Australian Sea lion colonies are identified. (C) South Australia sampling locations: Kangaroo Island. Three colonies were sampled from Kangaroo Island including Cape Gantheaume, Seal Bay and Seal Slide. Coastal towns and recreational beach camping sites on the island are indicated.
gellum black. Femora all black or forefemur ferruginous in apicoventral half; foretibia brown or ferruginous, midtibia brown ferruginous, hindtibia brown; tarsi varying from brown to ferruginous. ♂.– Unknown. GEOGRAPHIC DISTRIBUTION.– Known only from higher elevations (1020-1130 m above sea level) of Ranomafana National Park, Madagascar. RECORDS (Fig. 29).— All specimens were collected in Ranomafana National Park, Fianarantsoa Province. Holotype: ♀, Belle Vue at Talatakely at 21º15.99'S 47º25.21'E, alt. 1020 m, 14-21 Jan 2002, M. Irwin and R. Harin 'Hala (CAS). Paratypes: Radio tower at forest edge at 21º15.05'S 47º24.43'E, alt. 1130 m, 23 Aug – 7 Sept 2006 and 1-11 Nov 2006, M. Irwin and R. Harin 'Hala (2 ♀, CAS); same data as holotype except 22-28 Nov 2001 and R. Harin 'Hala alone (1 ♀, CAS); Vohiparara at 21º13.57'S 47º22.19'E, alt. 1110 m, 22-28 Nov 2001, R. Harin 'Hala (1 ♀, CAS). FIGURE 29. Collecting localities of Tachytes melanogaster sp. nov. in A Review of the Wasp Genus Tachytes Panzer, 1806 of Madagascar (Hymenoptera: Crabronidae)
gellum black. Femora all black or forefemur ferruginous in apicoventral half; foretibia brown or ferruginous, midtibia brown ferruginous, hindtibia brown; tarsi varying from brown to ferruginous. ♂.– Unknown. GEOGRAPHIC DISTRIBUTION.– Known only from higher elevations (1020-1130 m above sea level) of Ranomafana National Park, Madagascar. RECORDS (Fig. 29).— All specimens were collected in Ranomafana National Park, Fianarantsoa Province. Holotype: ♀, Belle Vue at Talatakely at 21º15.99'S 47º25.21'E, alt. 1020 m, 14-21 Jan 2002, M. Irwin and R. Harin 'Hala (CAS). Paratypes: Radio tower at forest edge at 21º15.05'S 47º24.43'E, alt. 1130 m, 23 Aug – 7 Sept 2006 and 1-11 Nov 2006, M. Irwin and R. Harin 'Hala (2 ♀, CAS); same data as holotype except 22-28 Nov 2001 and R. Harin 'Hala alone (1 ♀, CAS); Vohiparara at 21º13.57'S 47º22.19'E, alt. 1110 m, 22-28 Nov 2001, R. Harin 'Hala (1 ♀, CAS). FIGURE 29. Collecting localities of Tachytes melanogaster sp. nov.
Virtual tide gauges for predicting relative sea level rise supporting data
<p>Data and results from the publication</p> <p> Hawkins R., Husson L., Choblet G., Bodin T. and Pfeffer J.,<br> "Virtual tide gauges for predicting relative sea level rise",<br> JGR: Solid Earth,<br> 2019 (submitted)<br> </p> <p>Software available from </p> <p>https://github.com/rhyshawkins/TransTessellate2D/</p> <p> </p>
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m. in The Ceratopogonidae (Insecta: Diptera) fauna of the Central Black Sea Region in Turkey
Figure. The Central Black Sea Region of Turkey and sampling sites. Sampling sites: 1. Amasya: Centrum, Firingiler, 40°41′15.9″N, 35°54′45.9″E, 378 m; 2. Amasya: Göynücek, Kışlabeyi Village, 40°23′25.2″N, 35°33′43.1″E, 542 m; 3. Amasya: Gümüşhacıköy, Keçi Village, 40°49′07.5″N, 35°15′35.4″E, 777 m; 4. Amasya: Merzifon, Yakacık Village, 40°53′48.6″N, 35°25′43.9″E, 877 m; 5. Amasya: Suluova, Centrum, 40°49′24.3″N, 35°37′18.0″E, 473 m; 6. Amasya: Suluova, Çayüstü Village, 40°48′43.4″N, 35°38′24.4″E, 495 m; 7. Amasya: Taşova, 40°44′55.5″N, 36°17′49.6″E, 242 m; 8. Amasya: Taşova, Güngörmüş Village, 40°43′41.8″N, 36°17′06.3″E, 279 m; 9. Çorum: Centrum, Güney Village, 40°37′47.6″N, 35°05′58.5″E, 1170 m; 10. Çorum: Laçin, Gökgözler Village, 40°48′48.6″N, 34°50′38.6″E, 434 m; 11. Çorum: Mecitözü, Centrum, 40°31′41.1″N, 35°18′22.3″E, 767 m; 12. Çorum: Mecitözü, Hıdırlı Village, 40°29′19.6″N, 35°15′10.9″E, 918 m; 13. Çorum: Ortaköy, Senemoğlu Village, 40°19′24.0″N, 35°21′37.2″E, 533 m; 14. Çorum: Uğurludağ, Eskiçeltek Village, 40°33′46.6″N, 34°27′00.0″E, 519 m; 15. Ordu: Akkuş, Gökçebayır, 40°43′06.0″ N, 37°01′33.5″E, 920 m; 16. Ordu: Fatsa, Ayazlı, 41°00′32.9″N, 37°27′06.9″E, 130 m; 17. Ordu: Gölköy, 40°40′18.9″N, 37°36′43.4″E, 850 m; 18. Ordu: İkizce, 41°06′07.9″N, 37°07′45.3″E, 50 m; 19. Ordu: Korgan, Terzili Village, 40°42′06.6″N, 37°17′39.2″E, 1246 m; 20. Ordu: Korgan, Yenipınar Village, 40°47′58.0″N, 37°21′31.6″E, 584 m; 21. Ordu: Mesudiye, Centrum, 40°27′42.7″N, 37°46′23.0″E, 1100 m; 22. Ordu: Perşembe, Yumrutaş Village, 41°06′07.7″ N, 37°45′38.3″E, 231 m; 23. Ordu: Ünye, Cevizdere Village, 41°06′26.4″ N, 37°20′10.2″E, sea level; 24. Samsun, Terme, Centrum, 41°12′22.4″N, 36°56′14.8″E, sea level; 25. Samsun:Ayvacık, Yenice Village, 41°03′05.5″N, 36°39′17.4″E, 70 m; 26. Samsun: Bafra, Karaköy, 41°31′26.1″N, 36°00′52.5″E, 21 m; 27. Samsun: Centrum, Ataköy, 41°15′22.9″N, 36°17′26.8″E, 150 m; 28. Samsun: Centrum, entrance of Yeşiltepe (Çorak Village), 41°14′29.6″N, 36°16′52.8″E, 32 m; 29. Samsun: Havza, entrance of Mürsel Village, 40°59′26.5″N, 35°43′20.9″E, 642 m; 30. Samsun: Kavak, İdrisli Village, 41°05′45.5″N, 35°59′36.0″E, 706 m; 31. Samsun: Ladik, Tatlıcak Village, 40°55′29.6″N, 35°58′13.1″E, 870 m; 32. Samsun: Ladik, the vicinity of Lake Ladik, 40°54′06.0″N, 35°59′49.9″E, 870 m; 33. Samsun: Ondokuz Mayıs, Yörükler, 41°31′14.8″N, 36°07′23.6″E, sea level; 34. Samsun: Tekkeköy, Kerpiçli Village, 41°09′26.9″N, 36°32′04.4″E, 152 m; 35. Samsun: Vezirköprü, Pazarcı Village, 41°04′18.5″ N, 35°30′23.2″E, 690 m; 36. Tokat: Almus, Centrum, 40°22′35.5″N, 36°54′42.5″E, 803 m; 37. Tokat: Artova, Centrum, 40°06′42.1″N, 36°18′14.3″E, 1170 m; 38. Tokat: Centrum, vicinity of Tokat Airport, 40°18′23.5″N, 36°20′12.0″E, 556 m; 39. Tokat: Erbaa, Dereçiftliği, 40°33′22.3″ N, 36°37′22.4″E, 384 m; 40. Tokat: Niksar, Şahinli Village, 40°35′09.2″N, 36°53′59.5″E, 270 m; 41. Tokat: Reşadiye, Centrum, 40°23′02.9″N, 37°20′06.3″E, 511 m; 42. Tokat: Turhal, 40°20′21.1″N, 36°08′41.2″E, 507 m; 43. Tokat: Turhal, Arzupınar Village, 40°19′43.7″N, 36°10′52.3″E, 608 m.
Figure 10. A in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 10. A thin biofilm that has practically grown into the surface of crumbling sandstone in the abrasive section of an open pseudolittoral (a). Fragment of colonial settlment by Halamphora borealis (b). Scale bars: a — 5 cm, b — 10 µm. Photos by Philipp Sapozhnikov, Olga Kalinina.
Figure 9 in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 9. Fragment of cheesy ("moss") biofilm (a) on flat blocks of sandstone, in the middle pseudolittoral zone. Mixed colonial settlements of Halamphora coffeaeformis and H. hybrida (b, c) growing in the form of "clouds" (flakes) on Enteromorpha filaments. Designations: h — cells of various species of Halamphora, ep — cell of Entomoneis paludosa. Puddles of the upper pseudolittoral, April 2023. Scale bar: a – 5 cm, b – 100 µm, c – 25 µm. Photos by Philipp Sapozhnikov.
Figure 3 in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 3. Map of microepiliton sampling points in various coastal locations in the city of Aktau: a - map of the Caspian Sea with a highlighted area of the coast of the Mangystau region, b - section of the coast of the Mangystau region with a highlighted area of the city of Aktau, c - coast in the area of the city of Aktau and its immediate suburbs, d - locations of sampling in October 2022, e - locations of sampling in April 2023.
Figure 1. A in Epilithic biofilms of the Eastern Caspian (Aktau region, Kazakhstan) under conditions of falling sea level
Figure 1. A view of the impact of the wind waves on the newly dry bottom at the shoreline in the center of Aktau on 20 October 2022. Photo by Andrey Kostianoy.
Vanishing glaciers: a cause of sea-level rise and a threat to water supply
<p><span>The video discusses the contribution of glaciers to sea-level rise and their importance for humans. Due to their proximity to 0°C temperature, glaciers respond much faster to global warming than ice sheets, making their mass loss a significant contributor to sea-level rise during the 20th century and beyond. To determine the health state of glaciers and their contribution to sea-level rise, glaciologists calculate their mass budget, which has been largely negative for several decades now, indicating that glaciers are losing mass year after year, causing them to retreat. The video emphasizes the need for immediate reductions of greenhouse gas emissions to preserve these crucial and vulnerable water resources and natural heritage.</span></p>
MITgcm simulations of sea level response to freshwater injected at the surface and at depth in southern high latitudes: Model output and analysis code
<p>Model output (netcdf) and python code (included in both py and ipynb formats) to create the figures in Eisenman et al. (2024).</p> <div> <p>See https://eisenman-group.github.io for further details.</p> </div>
Ocean Basin and Lake Polygons for Sea Level Grids
<p>This collection of files contains geographically registered polygons defining the shape of ocean basins and large lakes across the globe, designed for use in gridding and analysis of satellite-based observations of sea level.<span> </span>In particular, this data set was produced as part of the NASA-SSH (<a href="https://podaac.jpl.nasa.gov/NASA-SSH">https://podaac.jpl.nasa.gov/NASA-SSH</a>) effort, whose aim is to deliver continuously updated, climate-quality, global observations of sea level derived from radar altimeter observations.<span> </span></p> <p> </p> <p>The polygons were derived using products from free vector and raster map data Natural Earth (<a href="https://naturalearthdata.com">https://naturalearthdata.com</a>).<span> </span>Ocean basins were derived from version 5.1.0 of the 1:10m Marine Areas:</p> <p><a href="https://www.naturalearthdata.com/downloads/10m-physical-vectors/">https://www.naturalearthdata.com/downloads/10m-physical-vectors/</a></p> <p>and Lakes were derived from version 5.0.0 of the 1:50m Lakes and Rivers polygons:</p> <p><a href="https://www.naturalearthdata.com/downloads/50m-physical-vectors/">https://www.naturalearthdata.com/downloads/50m-physical-vectors/</a></p> <p> </p> <p>For the lakes, only the largest 26 lakes and inland seas were retained, as most smaller lakes are not typically sampled by traditional nadir altimeters.<span> </span>For the marine basins, some polygons were joined or sometimes split in order to simplify grouping altimeter data by regions where it is expected to be geographically correlated.<span> </span>For example, southern sections were split from the South Pacific and South Atlantic Oceans to simplify separation of these basins across the South American Peninsula.<span> </span></p> <p> </p> <p>In addition to the polygons themselves, a table listing connections between polygons is also provided.<span> </span>This allows users to select observations in regions that are likely to be correlated over time scales of days to weeks or longer. Each polygon carries a unique numerical identifier (the Arctic Ocean is 1, the Southern Ocean is 2, etc…).<span> </span>For each identifier, the connection table lists all of the other identifiers that polygon is connected to.<span> </span>This is used in the NASA-SSH gridding process to down-select data used to estimate sea level at a specific location.</p> <p> </p> <p>Both the ocean and lake polygons themselves, and the connection table can be easily visualized in Google Earth (or other geographic mapping software) using the KMZ file provided.<span> </span>The connection table provides sets of polygons connected to each individual feature.<span> </span>For example, the Arctic Ocean polygon is connected to the Beaufort Sea, the Greenland Sea, the Barents Sea, etc.<span> </span>These can be easily visualized by turning on subsets of features in the Basin Connections folder within the KMZ file.</p> <p> </p> <p>Files contained in this dataset include:</p> <p>basin_files.tar.gz – a tar gzip file that contains a .dbf, .prj, .shx, and .shp Shape file that can be loaded into a geographic mapping program such as QGIS or Google Earth.<span> </span>This contains the polygon definitions, including their names.</p> <p>basin_name_table.txt – an ascii text file containing a list of all the basin ID numbers and simplified version of the basin names, separated by a colon “:”.<span> </span>A few basins were created for this dataset and do not have common geographic names.<span> </span>These are given names based on their ID number for example “Feature ID: 240”.<span> </span></p> <p>basin_connection_table.txt – an ascii text file containing a list of all the basin ID numbers that are geographically connected to a given basin ID.<span> </span>The basin ID number in question is listed first on each row, and the connected ID numbers follow a colon “:”, in a comma separated list.</p> <p>NASA-SSH Basins.kmz – This KMZ file contains all of the polygon definitions, along with the set of polygons for each basin that shows which basins it is connected to.<span> </span>If loaded into Google Earth, it will create a folder in the Google Earth “Places” panel called “NASA-SSH Basins”.<span> </span>Below this, two subfolders will be created, one will be called “All Basin Polygons” and will contain all of the basins polygons colored red.<span> </span>Clicking on any one of the polygons will show the Basin ID number and name of this polygon.<span> </span>The second subfolder is called “Basin Connections” and contains a list of subfolders, one for each Basin ID.<span> </span>These can be turned on 1 at a time and will show a given basin polygon and all of the polygons it is connected to.</p> <p> </p> <p>If you use these data please cite:</p> <p>Willis, J.K., J. Sanchez, R. Santos, and S. Fournier,<span> </span>Ocean Basin and Lake Polygons for Sea Level Grids, at DOI:10.5281/zenodo.13910542.</p>
STEHME files & HOLSEA spreadsheet for "Creel et al. 2022: Postglacial Relative Sea Level Change in Norway" and Balascio et al. 2023: "Refining Holocene sea-level dynamics for the Lofoten and Vesterålen archipelagos, northern Norway: Implications for prehistoric human-environment interactions"
<p><strong>Data Files for Creel et al. 2022: "Postglacial Relative Sea Level Change in Norway" and Balascio et al. 2023: "</strong><strong>Refining Holocene sea-level dynamics for the Lofoten and Vesterålen archipelagos, northern Norway: Implications for prehistoric human-environment interactions"</strong></p> <p>This repository contains the following files:</p> <p>1. Netcdf and csv files for the mean (stehme_mean.nc, stehme_mean_ts.csv) and standard deviation (stehme_std.nc, stehme_std_ts.csv) of the spatiotemporal empirical hierarchical model ensemble (STEHME) produced for Creel et al. 2022. The 'ts' suffix denotes time series for each unique lat/lon site. The netcdf files contain spatial maps at 100 yr resolution.</p> <p>2. mmc1.xlsx, the HOLSEA format Norway data compilation produced for Creel et al. 2022.</p> <p>3. Netcdf and csv files for the mean (stehme_mean_230721.nc, stehme_mean_ts_230721.csv) and standard deviation (stehme_std_230721.nc, stehme_std_ts_230721.csv) of the spatiotemporal empirical hierarchical model ensemble (STEHME) produced for Balascio et al. 2023. The 'ts' suffix denotes time series for each unique lat/lon site. The netcdf files contain spatial maps at 100 yr resolution. </p>
Direct comparison of the tsunami-generated magnetic field with sea level change for the 2009 Samoa and 2010 Chile tsunamis
<p>This is the dataset for the paper of</p> <p>“<strong>Direct comparison of the tsunami-generated magnetic field with sea level change for the 2009 Samoa and 2010 Chile tsunamis</strong>”</p> <p>in Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>This dataset includes three zips:</p> <p><strong>1. Processed Observation Tsunami Data</strong></p> <p>-- In this zip, there have the observation tsunami magnetic field and sea level change data of 2009 Samoa and 2010 Chile earthquakes which extracted from the data of the TIARES experiment (Suetsugu et al., 2012).</p> <p><strong>2. Simulated Tsunami Data</strong></p> <p>-- This is the simulated tsunami sea level change and magnetic field of 2009 Samoa and 2010 Chile earthquakes. The tsunami sea level was simulated by JAGURSv5.2 (Baba et al., 2017) and the tsunami magnetic field was simulated by TMTGEMv1.1 (Minami et al., 2017).</p> <p><strong>3. Converted Tsunami Sea Level Change</strong></p> <p>-- The converted sea level changes were calculated by the 2-D analytical solution of tsunami magnetic field (Minami et al., 2021) using the filtered tsunami magnetic vertical component Bz.</p>
Data from article "Sea-level rise in Venice: historic and future trends (review article)"
<p>Data from the article Zanchettin D., et al.: Sea-level rise in Venice: historic and future trends (review article), Nat. Hazards Earth Syst. Sci., 21, 1–35, 2021, https://doi.org/10.5194/nhess-21-1-2021</p> <p>The dataset contains:</p> <p>- Historical tide gauge data for Venice (relative sea level, or RSL, and RSL corrected for vertical land movement) for the winter (JFM) and autumn (OND) seasons.</p> <p>- sea-level projections for Venice for the RCP2.6, the RCP8.5 and a high-end scenario.</p>
Last Interglacial sea-level proxies in the Western Atlantic and Southwestern Caribbean, from Brazil to Honduras
<p>This spreadsheet is a complete record of the Last Interglacial sea-level proxies in the Western Atlantic and Southwestern Caribbean, from Brazil to Honduras. It has been exported from "The World Atlas of Last Interglacial Shorelines (WALIS) Database" (<a href="https://warmcoasts.eu/world-atlas.html">https://warmcoasts.eu/world-atlas.html</a>).</p>
A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-level System Model v4.19 using Gaussian Markov random fields -- Datasets and results
<p>Data archives for test experiments (Section 3) and Pine Island Glacier application (Section 4) from the manuscript "Kevin Bulthuis and Eric Larour, A new sampling capability for uncertainty quantification in the Ice-sheet and Sea-Level System Model v4.19 using Gaussian Markov random fields"</p> <p>Source code is available at https://doi.org/10.5281/zenodo.5532775.</p>
Database of US Regional Sea-level Rise Assessment Reports (Current for 2021)
<p>Database of regional sea-level rise assessment reports in the U.S. The data set includes nearly 400 projections from 31 reports for 54 locations in the U.S. and Puerto Rico, and accompanies the publication "Evaluating Knowledge Gaps in Sea-level Rise Assessments from the United States", Garner et al., <em>Earth's Future</em>. The data set is comprised of the most recent published assessment reports for each location (deadline of December 31<sup>st</sup>, 2021). Fields included in the database are listed below. </p> <p>Though substantial effort was made to ensure that all available and relevant assessment reports were included in the database, it is perhaps inevitable that a small number of reports were overlooked and may not be included here. </p> <p>1) Title of the Assessment Report</p> <p>2) Region of focus for the projection</p> <p>3) Broader geographical region for the projection (U.S. Northeast, U.S. South, or U.S. West)</p> <p>4) Latitude of the projection</p> <p>5) Longitude of the projection</p> <p>6) Lead Author of the report</p> <p>7) Sectors with which the authors are affiliated</p> <p>8) Third-party report flag (Yes = not locally produced, No = locally produced)</p> <p>9) Year the report was published</p> <p>10) Year the previous iteration of the report was published, if applicable</p> <p>11) Methodology of the projection</p> <p>12) Emission scenario used for the project</p> <p>13) Baseline year for the projection</p> <p>14) End year for the projection</p> <p>15) Lower estimate of sea-level rise</p> <p>16) Definition of the lower estimate of sea-level rise</p> <p>17) Central estimate of sea-level rise</p> <p>18) Definition of the central estimate of sea-level rise</p> <p>19) Upper estimate of sea-level rise</p> <p>20) Definition of the upper estimate of sea-level rise</p> <p>21) Vertical Land Motion (Yes = included, No = excluded)</p> <p>22) Land Water Storage (Yes = included, No = excluded)</p> <p>23) Greenland Ice Sheet (Yes = included, No = excluded)</p> <p>24) Antarctic Ice Sheet (Yes = included, No = excluded)</p> <p>25) Glaciers (Yes = included, No = excluded)</p> <p>26) Thermal Expansion (Yes = included, No = excluded)</p> <p>27) Ocean Dynamics (Yes = included, No = excluded)</p> <p>28) Link to the report containing the projection</p> <p>29) Notes relevant to the projection's database entry</p>
Framework for Assessing Changes To Sea-level (FACTS) Module Data
<p>Input module data sets from the Framework for Assessing Changes To Sea-level. These files should be installed in the modules-data/ directory. See https://github.com/radical-collaboration/facts for more information.</p>
Polymeric and inorganic sorbents as a green option to recover critical raw materials at trace levels from sea saltwork bitterns
<p>Seawater mining is certainly a green alternative source for obtaining minerals as seawater is a natural renewable and unlimited available resource. Based on the lack of ways to obtain certain raw materials, the European Union has created the Critical Raw Materials (CRM) list. Seawater contains almost all elements, including some of those present in the CRM list, but only a few are economically feasible to be extracted as most of them are considered Trace Elements (TEs) (μg L−1). Therefore, an improvement in TEs extraction must be carried out. Saltwork brines can be considered as they are naturally concentrated (20–40 times) compared to seawater, which makes the extraction and recovery of TEs easier. Selective polymeric and inorganic sorbents were evaluated for TEs recovery (Li, B, Co, Ga, Ge, Rb, Sr, and Cs) from synthetic brines mimicking sea saltwork bitterns. Distribution coefficients were determined to characterize selectivity patterns toward TEs. Although amine and sulphonic sorbents showed low sorption of TEs, carboxylic sorbents presented good sorption and recovery for Co and Ga. Among phosphonic/phosphinic sorbents, MTX8010 achieved >98% sorption and desorption of Ga. Aminophosphonic and iminodiacetic are the best sorbents for Sr, but its desorption was incomplete. B was only sorbed by N-Methylglucamine (>98%) and N-Methylpyridine sorbents (75%), and its desorption was 37–64% and 66−>99%, respectively. SbTreat presented good performance targeting Ga and Ge, and CsTreat demonstrated high Cs uptake, but its desorption was unachieved. The most highly selective sorbents could provide the possibility of building a green option to recover critical elements for societal development in the next decade.</p>
Modelling the response of mangroves and saltmarshes to sea-level rise: model development and validation
<p>Data used to parameterise and calibrate a model (IWEM0D) of the response of coastal wetlands to sea-level rise. Model parameterisation with core data from Westernport Bay, Victoria, Australia.</p> <p>IWEM0D (Intertidal Wetland Evolution Model - 0D) simulates how mangrove forests and saltmarsh wetlands respond to sea-level rise. The model framework, as detailed in Rogers et al. (in review), treats surface elevation change over time as a function of:</p> <ul> <li>Present elevation <code>E</code></li> <li>Inorganic/mineral matter accumulation rate <code>MAR</code></li> <li>Organic matter addition rate <code>OAR</code> for mangroves and saltmarsh</li> <li>Autocompaction <code>AC</code></li> </ul> <p>Specifically, incremental change in surface elevation <code>E</code> over time <code>t</code> is modelled as:</p> <p><code>E[t+1] = E[t] + MAR[t] + OAR[t] - AC[t]</code></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.