Image Copyright Glynis Pierson
Fenland Farm Survey Research Assistant – Data Cleaning Specialist
The Centre for Landscape Regeneration
Assignment length: Up to two months (full time or part time, details to be determined). Remote or hybrid working can be considered.
The CLR Fenland Farm Survey interviewed over 70 farmers from across the Fenland region and we are seeking support from someone experienced with quantitative data to collate and consolidate data for use and analysis by researchers across the CLR team.
We are seeking a detail-oriented Data Cleaning Specialist. You will be responsible for ensuring the accuracy, consistency, and integrity of our dataset from the recent CLR Fenland Farm Survey by collating the data, standardizing formats and where needed, identifying and rectifying inconsistencies. You will utilize a variety of tools and techniques to clean, validate, and standardize data, ensuring it is fit for analysis.
The ideal candidate will have a strong analytical mindset, excellent problem-solving skills, and a keen eye for detail. Experience of dealing with data regarding farming, agriculture, conservation, ecology or similar would be beneficial. Experience in data manipulation tools may be helpful (but not essential).
The survey seeks to understand the economic outputs of the Fenland region, as well as the opportunities that exist for farmers to farm differently, what appetite there might be for this and what barriers might exist. This is part of our work looking at a sustainable future for the Fenland region, and how the landscape can be managed to deliver broad economic and societal benefits, alongside biodiversity recovery, climate mitigation and adaptation.
Key Responsibilities:
- The raw data are stored in Excel files; these data should be processed and consolidated into a standardised dataset and format that can be analysed by researchers across the CLR.
- Sorting through the data, and, where needed, identifying errors, and making corrections to ensure accuracy. This includes removing duplicates, standardising formats, and rectifying inconsistencies.
- Ensure integrity and usability of the dataset for analysis.
The University actively supports EDI and encourages applications from all sections of society. We also support family-friendliness, and we welcome applications from individuals who wish to be considered for flexible working or part-time arrangements.
Requirements:
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Essential |
Desirable |
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Education |
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A-Level standard of education (or overseas equivalent). |
✓ |
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A degree or qualification in a relevant subject (any subject where quantitative data skills are required) or equivalent experience. |
✓ |
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A PhD or other further study involving quantitative data collection and/or analysis. |
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✓ |
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Knowledge and Skills |
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An eye for detail and a methodical approach |
✓ |
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Ability to produce accurate & reliable work |
✓ |
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Knowledge of data relevant to agriculture (for example, fertiliser usage and quantities, production, calculation of cost data, farm inputs) |
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✓ |
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Knowledge of data manipulation tools |
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✓ |
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Ability to work proactively and manage own workload |
✓ |
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Relevant Experience |
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Proven experience of ensuring accuracy, consistency, and integrity of datasets |
✓ |
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Proven experience of identifying and rectifying errors, resolving inconsistencies, and incomplete data points. |
✓ |
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Experience of using Excel and other software as needed. |
✓ |
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Experience of dealing with data regarding farming, agriculture, conservation, ecology or similar. |
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✓ |
Informal enquiries should be directed to: Helen Driver hvd23@cam.ac.uk
Assignment information:
All applicants will be required to demonstrate the right to work in the UK.
The role holder will be paid at University Salary Spine Grade 5. This assignment will be administered through the University of Cambridge Temporary Employment Service or the Cambridge Casual Worker System.
Application process
Applicants should send their CV and covering letter to clr_admin@plantsci.cam.ac.uk by Sunday 2nd June. The covering letter should outline your suitability for the role based on the points in the ‘Requirements’ section above, (1 side maximum).
Interviews will take place via Teams in June.
The Centre for Landscape Regeneration (CLR) is a five-year, £10 million investment from the Natural Environment Research Council (UKRI NERC). It is one of four large interdisciplinary projects, funded under the Changing the Environment Programme.
The Centre is co-led by Professor David Coomes, Director of the Cambridge Conservation Research Institute, and Professor Emily Shuckburgh OBE, Director of Cambridge Zero. The Centre comprises over 30 co-investigators across 14 University Departments and four external partners; the Endangered Landscapes Programme (ELP), the UK Centre for Ecology and Hydrology (UKCEH), the Royal Society for the Protection of Birds (RSPB) and NIAB.
The Centre for Landscape Regeneration is applying a whole systems approach to deliver the knowledge and tools necessary to regenerate UK landscapes using ‘Nature-based solutions’, which can contribute significantly both to preserving biodiversity and to achieving net zero emissions.
You will join an interdisciplinary team, working together to establish and oversee landscape–scale research in three iconic UK landscapes—the Cambridgeshire Fens, the Lake District and the Scottish Cairngorms—in partnership with farmers, NGOs, businesses and local communities. Together, these three landscapes produce nearly half the UK’s home-grown vegetables and hold more than a quarter of its rare and endangered wild animals. We work with key stakeholders in each of these landscapes, to assess the benefits of protecting and restoring nature and transparently debate the trade-offs. We will also co-design incentives to encourage long-term conservation, food security, restoration and regeneration strategies.
The project brings together biodiversity and ecosystem science with engineering, computer science, chemistry, political science and economics, each of which has equal relevance in understanding how to deliver climate mitigation and landscape regeneration.
https://www.cam.ac.uk/stories/fens-and-landscape-regeneration