Downloads provided by UsageCounts
The data and Python script are part of the forum article "A text mining analysis of the climate change literature in industrial ecology" authored by Dayeen, F.R., Sharma, A.S., and Derrible, S., and published in the Journal of Industrial Ecology in 2020. The Python script and instructions are included in the LiTCoF_v1.00-py.zip file. The original data is available in two formats: .csv and .pkl. Updates of the script will be posted at https://github.com/csunlab/LiTCoF and at https://csun.uic.edu/codes/LiTCoF.html. The data is also available at https://csun.uic.edu/datasets.html#AbstractsIE. Feel free to contact any of the authors for information and questions about the data and code.
{"references": ["Dayeen FR, Sharma AS, Derrible S. A text mining analysis of the climate change literature in industrial ecology. Journal of Industrial Ecology. 2020;1\u20139. https://doi.org/10.1111/jiec.12998"]}
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 20 | |
| downloads | 21 |

Views provided by UsageCounts
Downloads provided by UsageCounts