cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15159/ds.23.01
title: Estonian small lakes studied in 1999-2018 : [register]

description: Methods of phytoplankton processing. Samples were preserved with Lugol’s (acidified iodine) solution and processed using the Utermöhl (1958) method. Phytoplankton biomass was calculated from counts of cells or colonies using a Nikon Eclipse Ti-S inverted microscope at x200 and x400 magnification. Preserved sample (3 ml) was settled overnight. Identification and measurements took place in the course of counting. Counting units are independent (single) algal cells, colonies, or filaments/trichomes. Biovolumes of algal cells, colonies and/or filaments were calculated using assigned geometric shapes dimensions and converted to biomass assuming the specific density of 1 g cm-3 in accordance with Edler (1979). References of methods accepted Approved by CEN on 14 July 2006 “Water quality - Guidance standard on the enumeration of phytoplankton using inverted microscopy (Utermöhl technique)” (CEN 15204, 2006) European Standard EN 15204:2006 Edler, L. (ed.) 1979. Recommendations on methods for marine biological studies in the Baltic Sea. Phytoplankton and chlorophyll. – BMB WG 9. Utermöhl, H., (1958). Zur Vervollkommnung der quantitativen Phytoplankton-Methodik. Mitteilungen der Internationale Vereinigung für Theoretische und Angewandte Limnologie 9: 1-38. Biovolume calculation for pelagic and benthic microalgae | Request PDF. Available from: https://www.researchgate.net/publication/220031275_Biovolume_calculation_for_pelagic_and_benthic_microalgae [accessed Oct 29, 2018]. The most commonly used traditional biomass estimate for microalgae is cell biovolume, which is calculated from microscopically measured linear dimensions (Steinman et al. 1991, Snoeijs 1994, Sommer 1994, 1995, Hillebrand and Sommer 1997). Huber-Pestalozzi, G., Komarek, J., Fott, B. 1983. Das Phytoplankton des Süsswassers. 7(1). Chlorophyceae. Chlorococcales. Stuttgart. 1044. S. Komarek, J., Anagnostidis, K. 1999. Süsswasserflora von Mitteleuropa. 19/1. Cyanoprocaryota. 1. Chroococcales. Elsevier Spectrum Academischer Verlag. Heidelberg. Berlin. 548 S. Komarek, J., Anagnostidis, K. 2005. Süsswasserflora von Mitteleuropa. 19/2. Cyanoprocaryota. 2. Oscillatoriales. Elsevier Spectrum Academischer Verlag. 759 S. Komárek, J., 2013. Cyanoprokaryota 3. Teil: Heterocystous Genera. Süsswasserflora von Mitteleuropa. B. 19/3. Springer Spektrum. 1130 S. Krammer, K., Lange-Bertalot, H. 1997-1991. Süsswasserflora von Mitteleuropa. Bacillariophyceae. B. 2, 1-4. Spectrum Academischer Verlag.Heidelberg. Berlin. Popovský, J., Pfiester, L.A. 2008. Dinophyceae (Dinoflagellida). Süsswasserflora von Mitteleuropa. B. 6. Springer Spektrum. 272 S. Косинская Е.К. 1960. Флора споровых растений СССР. Том 5. Конъюгаты и Сцеплянки. (2). Изд. АН СССР. Москва-Ленинград. 706 стр. In Russian.
DOI: 10.6084/m9.figshare.21828453
title: CORA 2.0 2016-2019

description: The reanalysis dataset from the National Marine Data and Information Service (NMDIS) of China (http://mds.nmdis.org.cn/pages/dataView.html?type=2&id=a5da2a0528904471b3a326c3cc85997d).
DOI: 10.5281/zenodo.7509557
title: IDE Interactions of Novices Transitioning Between Programming Environments

description: Novices in introductory programming courses typically learn the fundamentals of programming using one of a wide of programming<br> environments. These vary greatly in terms of the mechanisms they employ to assist programmers, including their approaches to compilation and error message presentation. It is yet to be established which, if any, of these mechanisms are more beneficial for learning. In this study, we utilize Java programming process data to investigate the interaction between novices and two different versions of the BlueJ pedagogical IDE, which differ substantially in terms of compilation mechanism and error message presentation. Specifically, we compare novices that used both BlueJ 3 and BlueJ 4 with those who exclusively used either and the effects of the order in which they transition between BlueJ versions. We find substantial differences between different cohorts in terms of error messages and compilation which provides evidence that programming environments play an important part in influencing the programming<br> practices of novices. This work supports the hypothesis that the choice of programming environment significantly affects user behavior with respect to specific programming interactions and therefore it is reasonable to expect a difference in how these environments affect learning.
DOI: 10.5281/zenodo.7509556
title: IDE Interactions of Novices Transitioning Between Programming Environments

description: Novices in introductory programming courses typically learn the fundamentals of programming using one of a wide of programming<br> environments. These vary greatly in terms of the mechanisms they employ to assist programmers, including their approaches to compilation and error message presentation. It is yet to be established which, if any, of these mechanisms are more beneficial for learning. In this study, we utilize Java programming process data to investigate the interaction between novices and two different versions of the BlueJ pedagogical IDE, which differ substantially in terms of compilation mechanism and error message presentation. Specifically, we compare novices that used both BlueJ 3 and BlueJ 4 with those who exclusively used either and the effects of the order in which they transition between BlueJ versions. We find substantial differences between different cohorts in terms of error messages and compilation which provides evidence that programming environments play an important part in influencing the programming<br> practices of novices. This work supports the hypothesis that the choice of programming environment significantly affects user behavior with respect to specific programming interactions and therefore it is reasonable to expect a difference in how these environments affect learning.
DOI: 10.5281/zenodo.7509542
title: Soil Moisture and Vegetation Optical Depth

description: Two datasets, namely soil moisture and vegetation optical datasets are available from the year 2012 to 2020. Spatial resolution: 0.1<sup>o</sup> x 0.1<sup>o</sup> Temporal resolution: Two days. Dimensions: 339 x 298. The data description is as follows: <strong>Soil moisture: </strong>Files are written in NetCDF (.nc) format, containing information on latitude, longitude and soil moisture over Indian region. File naming format: LPRM_AMSR2_01T_L3_SM10_DYYYYMMDD; LPRM – Model AMSR2 – Satellite (Sensor) 01T - Version (Two day) SM - Variable D - Type of pass (Descending in this case) YYYYMMDD - Date (Format: YYYYMMDD) <strong>Vegetation Optical Depth (VOD):</strong> Files are written in NetCDF (.nc) format, containing information on latitude, longitude and VOD over Indian region. File naming format: LPRM_AMSR2_01T_L3_VOD10_DYYYYMMDD LPRM – Model AMSR2 – Satellite (Sensor) 01T - Version (Two day) L3 - Level 3 product VOD - Variable D - Type of pass (Descending in this case) YYYYMMDD - Date (Format: YYYYMMDD)
DOI: 10.5281/zenodo.7509541
title: Soil Moisture and Vegetation Optical Depth

description: Two datasets, namely soil moisture and vegetation optical datasets are available from the year 2012 to 2020. Spatial resolution: 0.1<sup>o</sup> x 0.1<sup>o</sup> Temporal resolution: Two days. Dimensions: 339 x 298. The data description is as follows: <strong>Soil moisture: </strong>Files are written in NetCDF (.nc) format, containing information on latitude, longitude and soil moisture over Indian region. File naming format: LPRM_AMSR2_01T_L3_SM10_DYYYYMMDD; LPRM – Model AMSR2 – Satellite (Sensor) 01T - Version (Two day) SM - Variable D - Type of pass (Descending in this case) YYYYMMDD - Date (Format: YYYYMMDD) <strong>Vegetation Optical Depth (VOD):</strong> Files are written in NetCDF (.nc) format, containing information on latitude, longitude and VOD over Indian region. File naming format: LPRM_AMSR2_01T_L3_VOD10_DYYYYMMDD LPRM – Model AMSR2 – Satellite (Sensor) 01T - Version (Two day) L3 - Level 3 product VOD - Variable D - Type of pass (Descending in this case) YYYYMMDD - Date (Format: YYYYMMDD)
DOI: 10.6084/m9.figshare.21828438
title: SST and SLA

description: The SST and SLA data are provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and are available at: https://data.marine.copernicus.eu/product/SST_GLO_SST_L4_NRT_OBSERVATIONS_010_001/download, https://data.marine.copernicus.eu/product/SEALEVEL_GLO_PHY_L4_MY_008_047/download.
DOI: 10.34740/kaggle/dsv/4815428
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.34740/kaggle/ds/2285387
title: 📉Huge crash in the Stock market 2022
DOI: 10.5517/ccdc.csd.cc2dpybd
title: CCDC 2225624: Experimental Crystal Structure Determination

description: Related Article: Y. Saadallah, Z. Setifi, D.K. Geiger, M.H. Al-Douh, A. Satour, F. Setifi|2022|IUCrData|7|x221180|doi:10.1107/S2414314622011804
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5517/ccdc.csd.cc2dql3v
title: CCDC 2226237: Experimental Crystal Structure Determination

description: Related Article: S. Jiajaroen, R. Diaz-Torres, S. Laksee, K. Chainok|2023|Acta Crystallogr.,Sect.E:Cryst.Commun.|79|38|doi:10.1107/S2056989022011884
description: An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
DOI: 10.5281/zenodo.7464099
title: Sentinel-5P Tropospheric Nitrogen Dioxide Density at 2 km from 2018-05 to 2022-11 Monthly Aggregation

description: Layers include: Tropospheric Nitrogen Dioxide Density monthly median value May 2018 – November 2022. Derived using the eumap package in Python. We derived three standard statistics: (1) 10th percentile (p10), median (m), and 90th percentile (p10). Band info Name Units Scale Description NO<sub>2</sub> (µmol m<sup>-</sup><sup>2</sup>) 0.1 tropospheric nitrogen dioxide density Warning: Original data have the different range of latitude among months. In December, there are no data above N 58°, where is approximately between Iceland and Scotland. Therefore, when it comes to monthly aggregation, there is a strip across N 58° as an artifact. It is not suggested to use this dataset in the region above N 58°. For more info about the s5p NO<sub>2</sub> product see: <strong>https://maps.s5p-pal.com/</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention: no2 = variable: nitrogen dioxide (µmol m<sup>-</sup><sup>2</sup>), s5p.l3.trop.tmwm= determination method: Copernicus Sentinel-5P product, level 3, tropospheric, temporal moving window median p10/p50/p90 = aggregation/statistics method: 10th/50th/90th percentile, 2km = spatial resolution / block support: 2 km, a = vertical reference: above ground, start date_end date (i.e. 20180501_20180531) = time reference: from start date to end date go = bounding box: global land without Antarctica epsg.4326 = ESPG code: epsg.4326 v20221219 = version code: creation date 20221219
DOI: 10.5281/zenodo.7464098
title: Sentinel-5P Tropospheric Nitrogen Dioxide Density at 2 km from 2018-05 to 2022-11 Monthly Aggregation

description: Layers include: Tropospheric Nitrogen Dioxide Density monthly median value May 2018 – November 2022. Derived using the eumap package in Python. We derived three standard statistics: (1) 10th percentile (p10), median (m), and 90th percentile (p10). Band info Name Units Scale Description NO<sub>2</sub> (µmol m<sup>-</sup><sup>2</sup>) 0.1 tropospheric nitrogen dioxide density Warning: Original data have the different range of latitude among months. In December, there are no data above N 58°, where is approximately between Iceland and Scotland. Therefore, when it comes to monthly aggregation, there is a strip across N 58° as an artifact. It is not suggested to use this dataset in the region above N 58°. For more info about the s5p NO<sub>2</sub> product see: <strong>https://maps.s5p-pal.com/</strong>. Antarctica is not included. To access and visualize maps use: OpenLandMap.org If you discover a bug, artifact or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: https://gitlab.com/openlandmap/global-layers/-/issues General questions and comments: https://disqus.com/home/forums/landgis/ All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL in Cloud Optimised GeoTiff (COG). File naming convention: no2 = variable: nitrogen dioxide (µmol m<sup>-</sup><sup>2</sup>), s5p.l3.trop.tmwm= determination method: Copernicus Sentinel-5P product, level 3, tropospheric, temporal moving window median p10/p50/p90 = aggregation/statistics method: 10th/50th/90th percentile, 2km = spatial resolution / block support: 2 km, a = vertical reference: above ground, start date_end date (i.e. 20180501_20180531) = time reference: from start date to end date go = bounding box: global land without Antarctica epsg.4326 = ESPG code: epsg.4326 v20221219 = version code: creation date 20221219
DOI: 10.5281/zenodo.7508068
title: Dataset Philoxenite-marbles_v.1

description: Dataset of unpublish marble finds from Philoxenite (Egypt)
description: Financed by the Polish National Science Centre (no. 2020/37/B/HS/00307)
DOI: 10.5281/zenodo.7508067
title: Dataset Philoxenite-marbles_v.1

description: Dataset of unpublish marble finds from Philoxenite (Egypt)
description: Financed by the Polish National Science Centre (no. 2020/37/B/HS/00307)
DOI: 10.6084/m9.figshare.21829032.v1
title: formatdata_trigger10.08-2565 Percutaneous (2).xlsx

description: Data trigger finger
DOI: 10.6084/m9.figshare.21829032
title: formatdata_trigger10.08-2565 Percutaneous (2).xlsx

description: Data trigger finger
DOI: 10.5281/zenodo.7509616
title: Table S1 & Table S2

description: Table S1: KEGG significantly enriched pathways associated with lipid metabolism. Table S2: DEGs associated with lipid metabolism.
DOI: 10.34740/kaggle/dsv/4815523
title: 2021 World Population (updated daily)
DOI: 10.6084/m9.figshare.21829059
title: Data.xlsx

description: It is about the ontogenetic changes in diestive enzyme activities of a common water flea under different food levels.
DOI: 10.6084/m9.figshare.21820863
title: Site of Small Hydropower stations, Water Resources and Hydropower Potential Simulations in the upper Amu Darya

description: <strong>1. Optimum location of sHP</strong> Shapefile of small hydropower stations cite. <strong>2. Discharge simulations</strong> Monthly discharge simulations by CFSR data, and monthly discharge simulations based on GCM data and quantile mapping correction processing. <strong>3.HP simulations</strong> Monthly HP of 10 small hydropower stations during 1981-2020, and monthly HP simulation results of 4 small hydropower stations after correction.
DOI: 10.6084/m9.figshare.21820863.v2
title: Best small hydropower stations site, discharge and HP simulations in the upper Amu Darya.

description: <strong>1. Optimum location of sHP</strong> Shapefile of small hydropower stations cite. <strong>2. Discharge simulations</strong> Monthly discharge simulations by CFSR data, and monthly discharge simulations based on GCM data and quantile mapping correction processing. <strong>3.HP simulations</strong> Monthly HP of 10 small hydropower stations during 1981-2020, and monthly HP simulation results of 4 small hydropower stations after correction.
DOI: 10.6084/m9.figshare.21820863.v3
title: Site of Small Hydropower stations, Water Resources and Hydropower Potential Simulations in the upper Amu Darya

description: <strong>1. Optimum location of sHP</strong> Shapefile of small hydropower stations cite. <strong>2. Discharge simulations</strong> Monthly discharge simulations by CFSR data, and monthly discharge simulations based on GCM data and quantile mapping correction processing. <strong>3.HP simulations</strong> Monthly HP of 10 small hydropower stations during 1981-2020, and monthly HP simulation results of 4 small hydropower stations after correction.
DOI: 10.34740/kaggle/dsv/4815616
title: YouTube Trending Video Dataset (updated daily)
DOI: 10.6084/m9.figshare.21829056
title: Data-HN kaolinite-manuscript entitled quantifying coupling effects between soil matric potential and osmotic potenital.xlsx

description: Sorption isotherm data of HN kaolinite
DOI: 10.6084/m9.figshare.21829056.v1
title: Data-HN kaolinite.xlsx

description: Sorption isotherm data of HN kaolinite
DOI: 10.6084/m9.figshare.20300773
title: The drawdown phase of dam decommissioning is a hot moment of gaseous carbon emissions from a temperate reservoir

description: Dam decommissioning (DD) is a viable management option for thousands of ageing dams. Reservoirs are large carbon sinks, and reservoir drawdown results in important carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) emissions. We studied the effects of DD on CO<sub>2</sub> and CH<sub>4</sub> fluxes from impounded water, exposed sediment, and lotic water before, during, and 3–10 months after drawdown of the Enobieta Reservoir, north Iberian Peninsula. During the study period, impounded water covered 0–100%, exposed sediment 0–96%, and lotic water 0–4% of the total reservoir area (0.14 km<sup>2</sup>). Areal CO<sub>2</sub> fluxes in exposed sediment (mean [SE]: 295.65 [74.90] mmol m<sup>−2</sup> d<sup>−1</sup>) and lotic water (188.11 [86.09] mmol m<sup>−2</sup> d<sup>−1</sup>) decreased over time but remained higher than in impounded water (−36.65 [83.40] mmol m<sup>−2</sup> d<sup>−1</sup>). Areal CH<sub>4</sub> fluxes did not change over time and were noteworthy only in impounded water (1.82 [1.11] mmol m<sup>−2</sup> d<sup>−1</sup>). Total ecosystem carbon (CO<sub>2</sub> + CH<sub>4</sub>) fluxes (kg CO<sub>2</sub>-eq d<sup>−1</sup>) were higher during and after than before reservoir drawdown because of higher CO<sub>2</sub> fluxes from exposed sediment. The reservoir was a net sink of carbon before reservoir drawdown and became an important emitter of carbon during the first 10 months after reservoir drawdown. Future studies should examine mid- and long-term effects of DD on carbon fluxes, identify the drivers of areal CO<sub>2</sub> fluxes from exposed sediment, and incorporate DD in the carbon footprint of reservoirs.
DOI: 10.6084/m9.figshare.20300773.v1
title: The drawdown phase of dam decommissioning is a hot moment of gaseous carbon emissions from a temperate reservoir

description: Dam decommissioning (DD) is a viable management option for thousands of ageing dams. Reservoirs are large carbon sinks, and reservoir drawdown results in important carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) emissions. We studied the effects of DD on CO<sub>2</sub> and CH<sub>4</sub> fluxes from impounded water, exposed sediment, and lotic water before, during, and 3–10 months after drawdown of the Enobieta Reservoir, north Iberian Peninsula. During the study period, impounded water covered 0–100%, exposed sediment 0–96%, and lotic water 0–4% of the total reservoir area (0.14 km<sup>2</sup>). Areal CO<sub>2</sub> fluxes in exposed sediment (mean [SE]: 295.65 [74.90] mmol m<sup>−2</sup> d<sup>−1</sup>) and lotic water (188.11 [86.09] mmol m<sup>−2</sup> d<sup>−1</sup>) decreased over time but remained higher than in impounded water (−36.65 [83.40] mmol m<sup>−2</sup> d<sup>−1</sup>). Areal CH<sub>4</sub> fluxes did not change over time and were noteworthy only in impounded water (1.82 [1.11] mmol m<sup>−2</sup> d<sup>−1</sup>). Total ecosystem carbon (CO<sub>2</sub> + CH<sub>4</sub>) fluxes (kg CO<sub>2</sub>-eq d<sup>−1</sup>) were higher during and after than before reservoir drawdown because of higher CO<sub>2</sub> fluxes from exposed sediment. The reservoir was a net sink of carbon before reservoir drawdown and became an important emitter of carbon during the first 10 months after reservoir drawdown. Future studies should examine mid- and long-term effects of DD on carbon fluxes, identify the drivers of areal CO<sub>2</sub> fluxes from exposed sediment, and incorporate DD in the carbon footprint of reservoirs.
DOI: 10.5281/zenodo.7509723
title: Drivers of bat activity at wind turbines advocate for mitigating bat exposure using multicriteria algorithm-based curtailment

description: data used for the paper
DOI: 10.5281/zenodo.7509722
title: Drivers of bat activity at wind turbines advocate for mitigating bat exposure using multicriteria algorithm-based curtailment

description: data used for the paper
DOI: 10.6084/m9.figshare.21829326
title: Bumblebee raw data and corresponding R-Script

description: This dataset provides all data belonging to the corresponding publication in biology. <br> Article Abstract: Climate warming has been observed as the main cause of changes in diversity, community composition and spatial distribution of different species. Due to even stronger warming com-pared to the global mean, bumblebees in alpine ecosystems are particularly exposed to these changes. To investigate the effects of climate warming, we sampled bumblebees along an eleva-tional gradient, compared the records with historical data from 1935/1936, and related our re-sults to climate models. We found that bumblebee community composition differed significant-ly between the two sampling periods and that increasing temperatures in spring were the most plausible factor explaining these range shifts. In addition, species diversity estimates were sig-nificantly lower compared to historical records. The number of socio-parasitic species was sig-nificantly higher in the historical communities, while recent communities showed increases in climate generalists and forest species at lower elevations. Nevertheless, no significant changes in community-weighted means of a species temperature index (STI) or the number of cold-adapted species were detected, likely due to the resolution of the historical data. We conclude that the composition and functionality of bumblebee communities in the study area have been signifi-cantly affected by climate warming, with changes in land use likely playing an additional im-portant role.
DOI: 10.57745/0mlo2d
title: Ecoli_MicroplateGrowth_Filtered.zip

description: Filtered Ecoli microplate OD growth data. See the readme for details.
DOI: 10.57745/7hznjs
title: Ecoli_MicroplateGrowth_Raw.zip

description: Raw Ecoli microplate OD growth data. See the readme for details.
DOI: 10.57745/ynqtow
title: ODUVvis_vs_ODMicroplate_calibration_data.tab

description: OD readings with UV-Vis spectrophotometer (ODUVvis Data Read1 and Read2) or microplate reader (ODMicroplate Data A1-A8) of serial dilutions (Dilution 1-21) of E. coli culture grown at 37°C in modified MOPS medium containing 3 g/L of glucose. PBS was used as blank in ODUVvis readings. ODMicroplate data was not blanked. Read1 and Read2 correspond to technical duplicate with UV-Vis spectrophotometer. A1-A8 correpond to microplate wells. Label A corresponds to the first row of the microplate and numbers 1-8 correspond to labels of the columns.