cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.23925291.v8
title: Dataset Artificial warming decreases heterotroph densities and trophic transfer efficiency in aquatic systems

description: This file holds the data and scripts used in the article "Artificial warming decreases heterotroph densities and trophic transfer efficiency in aquatic systems".Data are available in files whose name begins with the prefix "df". The variables names "date" and "date_c" represent the sampling date in discrete or continue value, respectively. "mesocosm_ID" is the identity of the mesocosm. "fish" represent the fish treatment (NF for No Fish, LB for Large-bodied fish and SB for Small-bodied fish). "warming" is the temperature treatment (NW for no warming, W for warming). "tmean15" is the mean temperature of the 15 days prior to the sampling date. "compartment", "class", "order" and "taxa" represent the taxonomy of the organisms. "N_l" is the density per liter. "C_mug_l" is the carbon biomass in microgram per liter. "S" and "H" are the species richness and the Shannon diversity index, respectively. "ZP" is the zooplankton:phytoplankton biomass ratio. The variables in df_bss.csv file are described in the main text of the article.Files with a numerical prefix are the scripts used for the statistical analyzes (multivariate analyzes, ecological index, community density and biomass, Predator Prey Biomass Ratio and Biomass Size Spectra).
DOI: 10.25582/data.2020-10.2071642/2076355
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2072428/2076356
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2071668/2076357
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2073803/2076358
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2073835/2076359
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2073857/2076360
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2073849/2076361
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2020-10.2074861/2076362
title: Data for EMSL Project 51129 from October 2020
DOI: 10.25582/data.2019-11.1885288/2076363
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895050/2076364
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885354/2076365
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885051/2076366
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886184/2076367
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882884/2076368
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882877/2076369
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882881/2076370
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885391/2076371
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883826/2076372
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884571/2076373
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1889329/2076374
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895066/2076375
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882868/2076376
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888540/2076377
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885134/2076378
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883888/2076379
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885395/2076380
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885128/2076381
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884811/2076382
title: Data for EMSL Project 51127 from November 2019
DOI: 10.5281/zenodo.10008184
title: The Green View Index of Tartu street network

description: This dataset is based on the OpenStreetMap street network for Tartu. The Green View Index (GVI) values have been calculated from Google Street View images using semantic segmentation and joined to street network data.
DOI: 10.25582/data.2019-12.1901060/2076383
title: Data for EMSL Project 51127 from December 2019
DOI: 10.5281/zenodo.10008183
title: The Green View Index of Tartu street network

description: This dataset is based on the OpenStreetMap street network for Tartu. The Green View Index (GVI) values have been calculated from Google Street View images using semantic segmentation and joined to street network data.
DOI: 10.25582/data.2019-11.1883857/2076384
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882864/2076385
title: Data for EMSL Project 51127 from November 2019
DOI: 10.5281/zenodo.10008201
title: geNomad output
DOI: 10.5281/zenodo.10008202
title: geNomad output
DOI: 10.25582/data.2019-11.1888542/2076386
title: Data for EMSL Project 51127 from November 2019
DOI: 10.5281/zenodo.10008218
title: NCBInrblast
DOI: 10.5281/zenodo.10008217
title: NCBInrblast
DOI: 10.5281/zenodo.10008068
title: Baltic Sea flood maps under the influence of sea-levle rise, dike height increases and managed realignment

description: The previously mentioned article provides inundation maps representing the current state of adjustment in terms of levee lines and associated elevations. The previously mentioned article provides inundation maps representing the current state of adjustment in terms of levee lines and associated elevations. The previously mentioned article provides inundation maps representing the current state of adjustment in terms of levee lines and associated elevations. 
The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).
The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will be accessible via the journal webpage):
Kiesel, J., Honsel, L.E., Lorenz, M., Gräwe, U., and Vafeidis, A. T.: Raising dikes and managed realignment may be insufficient for maintaining current flood risk along the German Baltic Sea coast, Communications Earth & Environment, accepted for publication, 2023.
 
The dataset contains:
- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model. The flood maps cover two sea-level rise (1 m and 1.5 m) and three adaptation scenarios (state dikes plus 1.5 m, all dikes plus 1.5 m and potential managed realignment sites including state dikes plus 1.5 m)
- the potential for physically plausible managed realignment sites along the German Baltic Sea coast
- a readme file containing further information on the datasets and related data and publications
 
For methodological details we refer the reader to the publication cited above and the publication presenting the modelling setup (Kiesel et al., 2023: https://doi.org/10.5194/nhess-23-2961-2023)
DOI: 10.25582/data.2019-11.1895049/2076387
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895052/2076388
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885379/2076389
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885272/2076390
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882878/2076391
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885357/2076392
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885061/2076393
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883672/2076394
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885366/2076395
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885283/2076396
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895043/2076397
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884620/2076398
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-12.1900894/2076399
title: Data for EMSL Project 51127 from December 2019
DOI: 10.25582/data.2019-11.1888469/2076400
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885183/2076401
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885275/2076402
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883799/2076403
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1887245/2076404
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886229/2076405
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882870/2076406
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882854/2076407
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885338/2076408
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882867/2076409
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885286/2076410
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1889326/2076411
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885394/2076412
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895082/2076413
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883690/2076414
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883892/2076415
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885956/2076416
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883903/2076417
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888059/2076418
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885053/2076419
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882915/2076420
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882890/2076421
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888459/2076422
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885084/2076423
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886503/2076424
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888492/2076425
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895024/2076426
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884806/2076427
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885273/2076428
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882865/2076429
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895047/2076430
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885287/2076431
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886494/2076432
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885205/2076433
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885847/2076434
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1887258/2076435
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884410/2076436
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883774/2076437
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883827/2076438
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884812/2076439
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885364/2076440
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883828/2076441
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884808/2076442
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885261/2076443
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1891822/2076444
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885057/2076445
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886767/2076446
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888588/2076447
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888892/2076448
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1889325/2076449
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883893/2076450
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886495/2076451
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888499/2076452
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885270/2076453
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886542/2076454
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1885191/2076455
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884132/2076456
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884260/2076457
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883668/2076458
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1887243/2076459
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884133/2076460
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888680/2076461
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886543/2076462
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-12.1901043/2076463
title: Data for EMSL Project 51127 from December 2019
DOI: 10.25582/data.2019-11.1883780/2076464
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25422/azu.data.24162528.v1
title: <i>Aedes aegypti</i> mosquito eggshell LC-MS/MS proteomics data

description: Two independent biological replicates from both RNAi-Fluc and RNAi-EOF1 were used in the <i>Aedes aegypti</i> mosquito eggshell proteomic analysis. Ovary tissues were dissected from RNAi-mosquitoes 4days PBM. Mature primary follicles were removed from dissected ovaries and thoroughly washed in 1X PBS to completely remove shed follicular epithelial cells, secondary follicles, the germarium, and the ovarian tissues including common and lateral oviducts.The enriched eggshells were homogenized in 6.0 M guanidine hydrochloride and incubated at 37<sup>o</sup>C overnight. Eggshell protein samples were separated about 1.0 cm into resolving gel on SDS-12% PAGE gels. LC-MS/MS analysis was performed on a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with an EASY-Spray nanoESI source at the University of Arizona Analytical and Biological Mass Spectrometry Facility.<i><br></i><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i><br>
DOI: 10.25422/azu.data.24162528
title: <i>Aedes aegypti</i> mosquito eggshell LC-MS/MS proteomics data

description: Two independent biological replicates from both RNAi-Fluc and RNAi-EOF1 were used in the <i>Aedes aegypti</i> mosquito eggshell proteomic analysis. Ovary tissues were dissected from RNAi-mosquitoes 4days PBM. Mature primary follicles were removed from dissected ovaries and thoroughly washed in 1X PBS to completely remove shed follicular epithelial cells, secondary follicles, the germarium, and the ovarian tissues including common and lateral oviducts.The enriched eggshells were homogenized in 6.0 M guanidine hydrochloride and incubated at 37<sup>o</sup>C overnight. Eggshell protein samples were separated about 1.0 cm into resolving gel on SDS-12% PAGE gels. LC-MS/MS analysis was performed on a Q Exactive Plus mass spectrometer (Thermo Fisher Scientific, San Jose, CA) equipped with an EASY-Spray nanoESI source at the University of Arizona Analytical and Biological Mass Spectrometry Facility.<i><br></i><i>For inquiries regarding the contents of this dataset, please contact the Corresponding Author listed in the README.txt file. Administrative inquiries (e.g., removal requests, trouble downloading, etc.) can be directed to data-management@arizona.edu</i><br>
DOI: 10.25582/data.2019-11.1885340/2076465
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888420/2076466
title: Data for EMSL Project 51127 from November 2019
DOI: 10.5281/zenodo.10008067
title: Baltic Sea flood maps under the influence of sea-level rise, dike height increases and managed realignment

description: The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).
The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will be accessible via the journal webpage):
Kiesel, J., Honsel, L.E., Lorenz, M., Gräwe, U., and Vafeidis, A. T.: Raising dikes and managed realignment may be insufficient for maintaining current flood risk along the German Baltic Sea coast, Communications Earth & Environment, accepted for publication, 2023.
 
The dataset contains:
- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model. The flood maps cover two sea-level rise (1 m and 1.5 m) and three adaptation scenarios (state dikes plus 1.5 m, all dikes plus 1.5 m and potential managed realignment sites including state dikes plus 1.5 m)
- the potential for physically plausible managed realignment sites along the German Baltic Sea coast
- a readme file containing further information on the datasets and related data and publications
 
For methodological details we refer the reader to the publication cited above and the publication presenting the modelling setup (Kiesel et al., 2023: https://doi.org/10.5194/nhess-23-2961-2023). The previously mentioned article provides inundation maps representing the current state of adaptation in terms of dike lines and associated elevations (https://doi.org/10.5281/zenodo.7886455). The code to detect the potential physically plausible managed realignment sites is publically available from https://gitlab.com/larsenno/sumare.
DOI: 10.5281/zenodo.10008225
title: Baltic Sea flood maps under the influence of sea-level rise, dike height increases and managed realignment

description: The provided data was produced as part of the Ecas-Baltic project (2020 - 2023). The project is funded by the Federal Ministry of Education and Research in Germany (BMBF, funding code 03F0860H).
The dataset contains information supporting the conclusions presented in the following publication (the final, revised version of the article will be accessible via the journal webpage):
Kiesel, J., Honsel, L.E., Lorenz, M., Gräwe, U., and Vafeidis, A. T.: Raising dikes and managed realignment may be insufficient for maintaining current flood risk along the German Baltic Sea coast, Communications Earth & Environment, accepted for publication, 2023.
 
The dataset contains:
- the flood maps containing both the maximum flood extent and maximum inundation depth at every grid cell of the coastal inundation model. The flood maps cover two sea-level rise (1 m and 1.5 m) and three adaptation scenarios (state dikes plus 1.5 m, all dikes plus 1.5 m and potential managed realignment sites including state dikes plus 1.5 m)
- the potential for physically plausible managed realignment sites along the German Baltic Sea coast
- a readme file containing further information on the datasets and related data and publications
 
For methodological details we refer the reader to the publication cited above and the publication presenting the modelling setup (Kiesel et al., 2023: https://doi.org/10.5194/nhess-23-2961-2023). The previously mentioned article provides inundation maps representing the current state of adaptation in terms of dike lines and associated elevations (https://doi.org/10.5281/zenodo.7886455). The code to detect the potential physically plausible managed realignment sites is publically available from https://gitlab.com/larsenno/sumare.
DOI: 10.25582/data.2019-11.1883852/2076467
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1884835/2076468
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888885/2076469
title: Data for EMSL Project 51127 from November 2019
DOI: 10.5281/zenodo.7575926
title: Circumpolar mid-winter thaw and refreeze based on fusion of Metop ASCAT and SMOS, 2011/2012 - 2021/2022

description: Rain-on-Snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. Snow pack properties are changing and in extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event.


Active and passive microwave data have been combined to identify events over land North of 65°N (Bartsch et al. 2023). In a first step Metop ASCAT (C-Band radar) was used to identify potential sudden snow structure change. In a second step, results have been masked for coincident observation of wet snow within +- 3 days based on SMOS (derived from Centre Aval de Traitement des Données SMOS (CATDS) level 3 product). Note that the SMOS retrievals can have data gaps due to radio frequency interferences (RFI) what leads to gaps in the event detection.


The dataset is structured by centre points of the hexagonal grid of the used Metop ASCAT product (EUMETSAT, approximately 12.5 km nominal resolution). Attributes include point ID (GPI), latitude, longitude and



	
aggregated number of events for the months November to February, 2011/12 to 2021/22, and their sum per winter (referred to as annual), or
	
in case of daily results (date in file name) the magnitude of ASCAT backscatter change in dB (DSigma0; no data value is '0.0').



The dataset extents Seawinds QuikScat (Ku-band) based results for 2000-2009 (Bartsch 2010, Freund and Bartsch 2020).
description: The study has received funding under the European Union's Horizon 2020 Research and Innovation Programme under
Grant Agreement No. 869471 (CHARTER). This work was further supported by the European Space Agency CCI+ Permafrost project.
DOI: 10.25582/data.2019-11.1895039/2076470
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1889327/2076471
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1895056/2076472
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1882852/2076473
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1891845/2076474
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1888591/2076475
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1886502/2076476
title: Data for EMSL Project 51127 from November 2019
DOI: 10.25582/data.2019-11.1883782/2076477
title: Data for EMSL Project 51127 from November 2019