cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.4421754
title: Quantitative dissection of color patterning in the foliar ornamental Coleus reveals underlying features driving aesthetic value

description: readMe.txt (this file) /Coleus_scans.zip: This directory contains 3 sub-directories: 1. raw_scans: Raw scans of coleus leaves. Data collection described below. 2. binary_leaves: A zip file of individual binary leaves isolated from the raw scans. Data processing described below. 3. colored_leaves: A zip file of individual colored leaves isolated from the raw scans using the binary leaf silhouettes. Data processing described below. Data collection Coleus leaf scans were collected from a starting population of 50,000 seedlings that were originally harvested from 133 open-pollinated mother plants in early January in Gainesville, FL. We organized the seedlings into families based on their maternal parents, grew the plants for five weeks and then selected ~2,000 individuals as potential new cultivars based on their foliar color patterning and branching architecture in mid-February. This data represents the youngest fully expanded leaf from each plant between 5-6 weeks of age. Leaves were imaged on Epson Perfection V550 Scanners with Kodak KOCSGS color separation guides included for color calibration. Analysis App Color analysis can be performed using an open-access software program called ColourQuant (Li et al., 2019); available on github: github.com/maoli0923/ColourQuant). Explanation of isolated binary and colored leaf files To isolate individual leaves from the raw data scans - we adjusted the RGB color balance on each scan by a white balance method so that the white swatch in the Kodak KOCSGS color separation guide is pure white, to ensure that scanners were not biasing the color data. Next, we segmented the leaves from the background by converting the RGB matrix into hue-saturation-value (HSV) format. Since most background pixels become grey in HSV, this was used to set a threshold that separates grey values from true leaf values. We then used the binary leaf silhouettes to extract the individual colored leaves by setting the background to pure white, and the foreground to pure black. We manually adjusted the thresholding for leaves that could not be automatically extracted due to shadows in the scan. The binary and colored leaf folder contain outliers, including leaves that were overlapping on the scanner, very small, or broken. These can be manually removed before analysis. File ID Key Files are named with the following code: Year_Family_Scan#. Files containing selected leaves for cultivar development are prepended with an “S” and files containing maternal leaf scans are prepended with an “M.” For questions regarding released dataset contact: Margaret Frank mhf47@cornell.edu <br> Li, M., Frank, M.H., and Migicovsky, Z.. 2019a. ColourQuant: A high throughput technique to extract and quantify colour phenotypes from plant images. arXiv 190301652. http://arxiv.org/abs/1903.01652
DOI: 10.5281/zenodo.4421753
title: Quantitative dissection of color patterning in the foliar ornamental Coleus reveals underlying features driving aesthetic value

description: readMe.txt (this file) /Coleus_scans.zip: This directory contains 3 sub-directories: 1. raw_scans: Raw scans of coleus leaves. Data collection described below. 2. binary_leaves: A zip file of individual binary leaves isolated from the raw scans. Data processing described below. 3. colored_leaves: A zip file of individual colored leaves isolated from the raw scans using the binary leaf silhouettes. Data processing described below. Data collection Coleus leaf scans were collected from a starting population of 50,000 seedlings that were originally harvested from 133 open-pollinated mother plants in early January in Gainesville, FL. We organized the seedlings into families based on their maternal parents, grew the plants for five weeks and then selected ~2,000 individuals as potential new cultivars based on their foliar color patterning and branching architecture in mid-February. This data represents the youngest fully expanded leaf from each plant between 5-6 weeks of age. Leaves were imaged on Epson Perfection V550 Scanners with Kodak KOCSGS color separation guides included for color calibration. Analysis App Color analysis can be performed using an open-access software program called ColourQuant (Li et al., 2019); available on github: github.com/maoli0923/ColourQuant). Explanation of isolated binary and colored leaf files To isolate individual leaves from the raw data scans - we adjusted the RGB color balance on each scan by a white balance method so that the white swatch in the Kodak KOCSGS color separation guide is pure white, to ensure that scanners were not biasing the color data. Next, we segmented the leaves from the background by converting the RGB matrix into hue-saturation-value (HSV) format. Since most background pixels become grey in HSV, this was used to set a threshold that separates grey values from true leaf values. We then used the binary leaf silhouettes to extract the individual colored leaves by setting the background to pure white, and the foreground to pure black. We manually adjusted the thresholding for leaves that could not be automatically extracted due to shadows in the scan. The binary and colored leaf folder contain outliers, including leaves that were overlapping on the scanner, very small, or broken. These can be manually removed before analysis. File ID Key Files are named with the following code: Year_Family_Scan#. Files containing selected leaves for cultivar development are prepended with an “S” and files containing maternal leaf scans are prepended with an “M.” For questions regarding released dataset contact: Margaret Frank mhf47@cornell.edu <br> Li, M., Frank, M.H., and Migicovsky, Z.. 2019a. ColourQuant: A high throughput technique to extract and quantify colour phenotypes from plant images. arXiv 190301652. http://arxiv.org/abs/1903.01652
DOI: 10.6073/pasta/4ea870d702032f23aadaf392d06de486
title: Little Rock Lake Experiment at North Temperate Lakes LTER: Nutrients 1996 - 2000

description: The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Parameters characterizing the nutrient chemistry of the treatment and reference basins of Little Rock Lake are measured at one station in the deepest part of each basin at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. These parameters include total nitrogen, total dissolved nitrogen, nitrate, ammonia, total phosphorus, total dissolved phosphorus, dissolved reactive phosphorus, bicarbonite-reactive filtered and unfiltered silica, dissolved reactive silica, total inorganic carbon, dissolved inorganic carbon, total organic carbon, dissolved organic carbon, and total particulate matter. Sampling Frequency: varies - Number of sites: 2
DOI: 10.6073/pasta/3899b327a2bb11fa27253e3067d5b863
title: Little Rock Lake Experiment at North Temperate Lakes LTER: Major Ions 1996 - 2000

description: The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Parameters characterizing the major ions of the treatment and reference basins of Little Rock Lake are measured at one station in the deepest part of each basin at the top and bottom of the epilimnion, mid-thermocline, and top, middle, and bottom of the hypolimnion. These parameters include chloride, sulfate, calcium, magnesium, sodium, potassium, iron, and manganese Sampling Frequency: varies - Number of sites: 2
DOI: 10.6073/pasta/45003a96dbfce631585ce9fbd8da4224
title: Little Rock Lake Experiment at North Temperate Lakes LTER: Physical Limnology 1983 - 2000

description: The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Parameters characterizing the physical limnology of the treatment (north basin, stations 1 and 3) and reference basin (south basin, station 2 and 4) are usually measured at one station in the deepest part of each basin (stations 1 and 2) at 0.5 to 1-m depth intervals depending on the parameter. Parameters measured at depth include water temperature, vertical penetration of photosynthetically active radiation (PAR), dissolved oxygen, chlorophyll and phaeopigments. Additional derived parameters include fraction of surface PAR at each depth and percent oxygen saturation. Auxiliary data include time of day, air temperature, cloud cover, and wind speed and direction and secchi depth. Sampling Frequency: varies - Number of sites: 4
DOI: 10.6073/pasta/19dace2e8cf9fbb78ba6f5ddec722905
title: Little Rock Lake Experiment at North Temperate Lakes LTER: Snow and Ice Depth 1984 - 2000

description: The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Snow and ice depth are measured during the winter months on the reference and treatment basins of Little Rock Lake. Sampling Frequency: varies - Number of sites: 4
DOI: 10.6073/pasta/93c7f5ee72eda7b75c3080c7879ed7bb
title: Little Rock Lake Experiment at North Temperate Lakes LTER: Secchi Disk Depth; Other Auxiliary Sample Data 1983 - 2000

description: The Little Rock Acidification Experiment was a joint project involving the USEPA (Duluth Lab), University of Minnesota-Twin Cities, University of Wisconsin-Superior, University of Wisconsin-Madison, and the Wisconsin Department of Natural Resources. Little Rock Lake is a bi-lobed lake in Vilas County, Wisconsin, USA. In 1983 the lake was divided in half by an impermeable curtain and from 1984-1989 the northern basin of the lake was acidified with sulfuric acid in three two-year stages. The target pHs for 1984-5, 1986-7, and 1988-9 were 5.7, 5.2, and 4.7, respectively. Starting in 1990 the lake was allowed to recover naturally with the curtain still in place. Data were collected through 2000. The main objective was to understand the population, community, and ecosystem responses to whole-lake acidification. Funding for this project was provided by the USEPA and NSF. Secchi Disk data were collected from the treatment and reference basins of Little Rock Lake at one station in the deepest part of each basin. Auxiliary data associated with each sampling event include time of day, air temperature, wind direction and speed, wave height, and cloud cover at the time of sampling. Sampling Frequency: varies - Number of sites: 2
DOI: 10.6084/m9.figshare.13115624
title: Publications assessing impacts of mining on ecosystem services

description: Dataset of 60 papers with a combined 366 unique relationships between mining and ecosystem services.
DOI: 10.6084/m9.figshare.13115624.v2
title: Publications assessing impacts of mining on ecosystem services

description: Dataset of 60 papers with a combined 366 unique relationships between mining and ecosystem services.
DOI: 10.6084/m9.figshare.13115624.v1
title: Publications assessing impacts of mining on ecosystem services

description: Dataset of 60 papers with a combined 366 unique relationships between mining and ecosystem services.
DOI: 10.21227/mkg2-1t48
title: ARASTI database

description: Character recognition has been widely understood as a means of mechanizing the process of understanding text in the written form to facilitate fast and efficient use of text. Indeed, text existing all around us presents information for peoples. However, tourists in foreign countries are unable to understand what indicate text on road signs, shop names, product advertisements, posters, etc. when they are unfamiliar with the native language of the visited country.Currently there is no available dataset of Arabic script text images in the wild. Since our aim is to help the research community in standardizing the evaluation of scene Arabic text recognition, the Tunisian Research Groups in Intelligent Machines of University of Sfax (REGIM lab of Sfax) will provide the Arabic Scene Text Image Database of Regim Lab (ARASTI database) freely of charge to mainly Arabic scene character recognition researchers and to increase total of researches done to enhance scene character recognition.All documents and papers that uses the Arabic Scene Text Image Database of Regim Lab (ARASTI database) will acknowledge the use of the database by including an appropriate citation to the following:[1]: Maroua Tounsi, Ikram Moalla, Adel M. Alimi, "ARASTI: A database for Arabic scene text recognition." 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE, 2017.[2]: Maroua Tounsi, Ikram Moalla, Adel M. Alimi, Frank Lebouregois, “Feature Representation using Sparse Coding for Robust Arabic Characters Recognition in Natural Scenes” , ICDAR 2015.
DOI: 10.21227/3247-rn93
title: ARASTI database

description: Character recognition has been widely understood as a means of mechanizing the process of understanding text in the written form to facilitate fast and efficient use of text. Indeed, text existing all around us presents information for peoples. However, tourists in foreign countries are unable to understand what indicate text on road signs, shop names, product advertisements, posters, etc. when they are unfamiliar with the native language of the visited country.Currently there is no available dataset of Arabic script text images in the wild. Since our aim is to help the research community in standardizing the evaluation of scene Arabic text recognition, the Tunisian Research Groups in Intelligent Machines of University of Sfax (REGIM lab of Sfax) will provide the Arabic Scene Text Image Database of Regim Lab (ARASTI database) freely of charge to mainly Arabic scene character recognition researchers and to increase total of researches done to enhance scene character recognition.All documents and papers that uses the Arabic Scene Text Image Database of Regim Lab (ARASTI database) will acknowledge the use of the database by including an appropriate citation to the following:[1]: Maroua Tounsi, Ikram Moalla, Adel M. Alimi, "ARASTI: A database for Arabic scene text recognition." 2017 1st International Workshop on Arabic Script Analysis and Recognition (ASAR). IEEE, 2017.[2]: Maroua Tounsi, Ikram Moalla, Adel M. Alimi, Frank Lebouregois, “Feature Representation using Sparse Coding for Robust Arabic Characters Recognition in Natural Scenes” , ICDAR 2015.
DOI: 10.5281/zenodo.4421988
title: Damage Monitoring of Structural Resins Loaded with Carbon Fillers: Experimental and Theoretical Study

description: Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Simulation model, Dynamic Light Scattering, FTIR spectroscopy Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera , Water sorption measurement, Transmission Electron Microscopy, Stress Strain
DOI: 10.5281/zenodo.4421987
title: Damage Monitoring of Structural Resins Loaded with Carbon Fillers: Experimental and Theoretical Study

description: Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Simulation model, Dynamic Light Scattering, FTIR spectroscopy Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera , Water sorption measurement, Transmission Electron Microscopy, Stress Strain
DOI: 10.5281/zenodo.4419655
title: Design of Multifunctional Composites: New Strategy to Save Energy and Improve Mechanical Performance

description: dataset on Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement, Transmission Electron Microscopy and Stress Strain
DOI: 10.5281/zenodo.4419654
title: Design of Multifunctional Composites: New Strategy to Save Energy and Improve Mechanical Performance

description: dataset on Dynamic Mechanical Analysis, Electro-Mechanical Measurement, Dynamic Light Scattering FTIR spectroscopy, Thermogravimetric analysis, Differential Scanning Calorimetry, Electro-Temperature Measurement, Thermal Image Camera, Water sorption measurement, Transmission Electron Microscopy and Stress Strain
DOI: 10.5281/zenodo.3979965
title: DWDE drifters: Trajectories of surface drifters in the western Gulf of Mexico from the Deep-Water Dispersion Experiment

description: This dataset is comprised of the final processed data collected by 207 surface drifters deployed in the western Gulf of Mexico during the Deep Water Dispersion Experiment (DWDE), as part of the project “Implementación de redes de observación oceanográficas (físicas geoquímicas, ecológicas) para la generación de escenarios ante posibles contingencias relacionadas a la exploración y producción de hidrocarburos en aguas profundas del Golfo de México” of the Gulf de Mexico Research Consortium (CIGoM), funded by CONACYT‐SENER‐Hydrocarbon Fund, Mexico”. The DWDE drifter data is also made available with the GulfDriftersWithDWDE dataset (http://doi.org/10.5281/zenodo.3985916), which is a merged and uniformly processed surface drifter dataset which includes publicly available data from several different drifter experiments. These latter database is the basis for the gridded product GulfFlow (http://doi.org/10.5281/zenodo.3978793). See Lilly and Pérez-Brunius (2020) "A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements" for details. García Carrillo, P., Ronquillo Méndez, A., Rodríguez Outerelo, J., Pérez Brunius, P. 2018. Experimento de Dispersión en Aguas Profundas ( DWDE ). Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California. 253 pp.
description: The acquisition of the surface drifter data from the DWDE experiment has been financed by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico (project 201441), developed by the Gulf of Mexico Research Consortium (CIGoM). We recognize PEMEX for promoting to the Fund the specific demand on oil spills and the environment. See database webpage with additional information, as well as request for download form (https://giola.cicese.mx/database/DWDE)
description: {"references": ["Garc\u00eda Carrillo, P., Ronquillo M\u00e9ndez, A., Rodr\u00edguez Outerelo, J., P\u00e9rez Brunius, P. 2018. Experimento de Dispersi\u00f3n en Aguas Profundas ( DWDE ). Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, Baja California. 253 pp."]}
DOI: 10.5281/zenodo.3993733
title: DWDE drifters: Trajectories of surface drifters in the western Gulf of Mexico from the Deep-Water Dispersion Experiment

description: This dataset is comprised of the final processed data collected by 207 surface drifters deployed in the western Gulf of Mexico during the Deep Water Dispersion Experiment (DWDE), as part of the project “Implementación de redes de observación oceanográficas (físicas geoquímicas, ecológicas) para la generación de escenarios ante posibles contingencias relacionadas a la exploración y producción de hidrocarburos en aguas profundas del Golfo de México” of the Gulf de Mexico Research Consortium (CIGoM), funded by CONACYT‐SENER‐Hydrocarbon Fund, Mexico”. The DWDE drifter data is also made available with the GulfDriftersWithDWDE dataset (http://doi.org/10.5281/zenodo.3985916), which is a merged and uniformly processed surface drifter dataset which includes publicly available data from several different drifter experiments. These latter database is the basis for the gridded product GulfFlow (http://doi.org/10.5281/zenodo.3978793). See Lilly and Pérez-Brunius (2020) "A gridded surface current product for the Gulf of Mexico from consolidated drifter measurements" for details. García Carrillo, P., Ronquillo Méndez, A., Rodríguez Outerelo, J., Pérez Brunius, P. 2018. Experimento de Dispersión en Aguas Profundas ( DWDE ). Centro de Investigación Científica y de Educación Superior de Ensenada, Baja California. 253 pp.
description: The acquisition of the surface drifter data from the DWDE experiment has been financed by the CONACYT-SENER-Hydrocarbons Sector Fund, Mexico (project 201441), developed by the Gulf of Mexico Research Consortium (CIGoM). We recognize PEMEX for promoting to the Fund the specific demand on oil spills and the environment. See database webpage with additional information, as well as request for download form (https://giola.cicese.mx/database/DWDE)
description: {"references": ["Garc\u00eda Carrillo, P., Ronquillo M\u00e9ndez, A., Rodr\u00edguez Outerelo, J., P\u00e9rez Brunius, P. 2018. Experimento de Dispersi\u00f3n en Aguas Profundas ( DWDE ). Centro de Investigaci\u00f3n Cient\u00edfica y de Educaci\u00f3n Superior de Ensenada, Baja California. 253 pp."]}