Automated Author ProfileBarenblitt, Abigail
Barenblitt, Abigail
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 4.6 (sum of 3 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
These data are associated with an article published in Science of the Total Environment in 2021 (currently in revision). In this study, the Landsat image archive available through Google Earth Engine was used to quantify the total footprint of vegetation loss due to artisanal gold mines in Ghana from 2002-2019 and understand how conversion of forested regions to mining has changed over a decadal period from 2007-2017. Areas that experienced anomalous vegetation loss from 2005 to 2019 were identified through assembling a Landsat imagery timestack from 2002 to 2005. A separate imagery timestack was assembled from 2010 to 2019 to establish an observation period. A combination of machine learning and change detection algorithms were then used to calculate land cover conversion to gold mining and the timing of conversion annually. Details regarding methods and Earth Engine code can be found in the supplemental information with the manuscript. File Description: FullConversiontoMiningExtent2019: Total area of gold mining conversion detected from 2002-2019. Attributes: MineType, AreaM2, AreaKM2. MineType 1 = Artisanal Mines MineType 2 = Industrial Mines Resolution: 30m MiningConversion_2007_2017Vec Annual gold mining conversion for 2007-2017. Attributes: count, classifica classifica = Year of Conversion (7 = 2007, 8= 2008, etc.) Resolution: 30m Barenblitt, Abigail; Payton, Amanda; Lagomasino, David; Fatoyinbo, Lola; Asare, Kofi; Aidoo, Kenneth; Pigott, Hugo; Som, Chalres Kofi; Seidu, Omar; Smeets, Laurents; Wood, Danielle (in review). The large footprint of small-scale artisanal gold mining in Ghana. Science of the Total Environment.
Authors
- Barenblitt, Abigail
These data are associated with an article published in Science of the Total Environment in 2021 (currently in revision). In this study, the Landsat image archive available through Google Earth Engine was used to quantify the total footprint of vegetation loss due to artisanal gold mines in Ghana from 2002-2019 and understand how conversion of forested regions to mining has changed over a decadal period from 2007-2017. Areas that experienced anomalous vegetation loss from 2005 to 2019 were identified through assembling a Landsat imagery timestack from 2002 to 2005. A separate imagery timestack was assembled from 2010 to 2019 to establish an observation period. A combination of machine learning and change detection algorithms were then used to calculate land cover conversion to gold mining and the timing of conversion annually. Details regarding methods and Earth Engine code can be found in the supplemental information with the manuscript. File Description: FullConversiontoMiningExtent2019: Total area of gold mining conversion detected from 2002-2019. Attributes: MineType, AreaM2, AreaKM2. MineType 1 = Artisanal Mines MineType 2 = Industrial Mines Resolution: 30m MiningConversion_2007_2017Vec Annual gold mining conversion for 2007-2017. Attributes: count, classifica classifica = Year of Conversion (7 = 2007, 8= 2008, etc.) Resolution: 30m Barenblitt, Abigail; Payton, Amanda; Lagomasino, David; Fatoyinbo, Lola; Asare, Kofi; Aidoo, Kenneth; Pigott, Hugo; Som, Chalres Kofi; Seidu, Omar; Smeets, Laurents; Wood, Danielle (in review). The large footprint of small-scale artisanal gold mining in Ghana. Science of the Total Environment.
Authors
- Barenblitt, Abigail
These data are associated with an article published in Science of the Total Environment in 2021 (currently in revision). In this study, the Landsat image archive available through Google Earth Engine was used to quantify the total footprint of vegetation loss due to artisanal gold mines in Ghana from 2002-2019 and understand how conversion of forested regions to mining has changed over a decadal period from 2007-2017. Areas that experienced anomalous vegetation loss from 2005 to 2019 were identified through assembling a Landsat imagery timestack from 2002 to 2005. A separate imagery timestack was assembled from 2010 to 2019 to establish an observation period. A combination of machine learning and change detection algorithms were then used to calculate land cover conversion to gold mining and the timing of conversion annually. Details regarding methods and Earth Engine code can be found in the supplemental information with the manuscript. File Description: FullConversiontoMiningExtent2019: Total area of gold mining conversion detected from 2002-2019. Attributes: MineType, AreaM2, AreaKM2. MineType 1 = Artisanal Mines MineType 2 = Industrial Mines Resolution: 30m MiningConversion_2007_2017Vec Annual gold mining conversion for 2007-2017. Attributes: count, classifica classifica = Year of Conversion (7 = 2007, 8= 2008, etc.) Resolution: 30m Barenblitt, Abigail; Payton, Amanda; Lagomasino, David; Fatoyinbo, Lola; Asare, Kofi; Aidoo, Kenneth; Pigott, Hugo; Som, Chalres Kofi; Seidu, Omar; Smeets, Laurents; Wood, Danielle (in review). The large footprint of small-scale artisanal gold mining in Ghana. Science of the Total Environment.
Authors
- Barenblitt, Abigail