http://deepblue.lib.umich.edu/data/concern/data_sets/000001074
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Michigan Department of Natural Resources (MDNR) historically collected lake survey data on index cards. We used the Zooniverse crowdsourcing platform for volunteer transcription of these records using various workflows that captured different data. To be included in the dataset, each card was transcribed by three or more volunteers.
Zooniverse transcriptions require significant cleaning and curation before the data is in a usable format. We used code to aggregate the transcribed data from each person in order to provide a consensus-based “final answer” and confidence score for each data field, based on how well entries from the different volunteers matched. We then standardized data using techniques such as changing all text to lowercase, trimming excess whitespace, and converting fractions to decimals. We separated numeric and alphabetic values into different data columns. Finally, we standardized units for each variable into a single unit, and when applicable, transformed to metric units (e.g. inches to millimeters). We checked data numeric values by plotting, identifying outliers, and reviewing the original document.
In order to combine multiple sampling events for one lake or connect the transcribed data to more contemporary survey data from the MDNR, we matched the records with the corresponding MDNR unique lake identifiers. The transcribed data included each lake’s name, county, and in some instances geographic reference data in the form of Township, Range, and Section from the United States Public Land Survey System (TRS). We joined data entries on lake names, counties, and TRS when available. Remaining lakes that were unmatched due to issues like lakes crossing county lines or changing names over time, were manually matched to data using experts from the research team. Finally, we were unable to match some of the historical data due to insufficient geographic information.
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| Description |
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Michigan lakes are an important resource, however, their ecosystems are declining and projected to continue to face further impacts under future land use and climate change. Understanding how lake ecosystems respond to environmental stressors and management actions is critical for identifying resilient lakes and developing adaptation strategies. However, the ability to manage lakes is hampered by a lack of historical information. Historical lake data in Michigan were originally archived as index cards at the Michigan Department of Natural Resources. All of the images of these cards are stored in this collection, Collections, Heterogeneous data, and Next Generation Ecological Studies (CHANGES) - Michigan Lake Surveys, and the images for this specific dataset are stored in the CHANGES Project- Fish Collection (FISHc) dataset. The CHANGES project used a crowd sourcing platform called Zooniverse to transcribe at least basic information (i.e. dates, collected by) from all of these cards. Some of the card types, such as the one in this dataset, were prioritized to transcribe to produce a usable (i.e. machine-readable, uniform, and standardized) historical dataset.
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Fish collection card types include targeted and non-targeted fisheries surveys by the Department of Natural Resources and this information was transcribed and curated into a csv file (fishc_data.csv). These records include information on the gear types used, the area surveyed and the length and mesh size of nets fished. The number and common name of fish species caught were recorded as well and included in a species table (fishc_species_table). A description of all data fields can be found in the fishc_datadictionary.
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Michigan Institute For Data & AI In Society (MIDAS) Propelling Original Data Science Grant
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| Citations to related material |
- King, K.B.S., Schell, J, Wehrly, K.E., Lenard, M., Singer, R., López-Fernández, H., Thomer, A.K., & Alofs, K.M. Community science helps digitize 78 years of fish and habitat data for thousands of lakes in Michigan, USA. under review
- King, K.B.S, Giacomini, H.C., Wehrly, K., López-Fernández, H., Thomer, A.K., & Alofs, K.M. (2023). Using historical fish catch data to evaluate predicted changes in relative abundance in response to a warming climate. Ecography. 2023:8. https://doi.org/10.1111/ecog.06798
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- https://doi.org/10.7302/1pz4-x763
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