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Love Data Week research data case studies: human ecology

By ucyldva, on 13 February 2018

This week is Love Data Week an international event ‘to raise awareness and build a community to engage on topics related to research data management, sharing, preservation, reuse, and library-based research data services.’ As part of Love Data Week, a number of free events are taking place across Bloomsbury. We will also be publishing short research data case studies as part of Love Data Week. These case studies cover a range of disciplines and types of data generated accross UCL.
The second case study looks at research data that was collected as part of the Poverty and Ecosystems Impacts of Tanzania’s Wildlife Management Areas (PIMA) project.

Research Area: Human Ecology

Professor Katherine Homewood’s research explores the interaction between conservation and development, focusing particularly on sub-Saharan African. Katherine’s research looks at the implications of natural resource policies and management for local people’s livelihoods and welfare, and the implications of changing land use for environment and biodiversity

About the project

Poverty and Ecosystems Impacts of Tanzania’s Wildlife Management Areas (PIMA) is a three-year interdisciplinary research project which aims to discover how Tanzania’s Wildlife Management Areas have changed people’s lives and their effects on wildlife and the environment.

Project funding

The project received funding from Ecosystem Services for Poverty Alleviation (ESPA) a project co-funded by NERC, DfID, and the ESRC.

Research Dat

The interdisciplinary nature of the project led to a broad range of data being collected including data on household income, environmental data and qualitative data.

Storage

Data was captured electronically, anonymised, and stored using cloud storage.

Data sharing

The data will be shared using the UK Data Archive. The data will also be described in a forthcoming data descriptor paper in Scientific Data. This paper will provide an opportunity to provide further context to the project’s data and aid others in reusing this data.

Student involvement

 PhD and MSc Students from the University of Copenhagen were involved in the project. The insights from the project also inform teaching from Katherine Homewood.

Challenges

There were a number of challenges during the project. One challenge was dealing with the volume of the data. Carrying out statistical methods on the data which aimed to be quasi-experimental involved the use of Bayesian hierarchical models. Though there was support from a statistician during the project further support in this area of analysis would have been valuable for the project.

Further information

Further support for the project can be found on the project’s website.

 

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