Understanding the evolutionary consequences of anthropogenic change is imperative for estimating long-term species resilience. While contemporary genomic data can provide us with important insights into recent demographicic histories, investigating past change using present genomic data alone has limitations. In comparison, temporal genomics studies, defined herein as those that incorporate time series genomic data, leverage museum collections and repeated field sampling to directly examine evolutionary change. As temporal genomics is applied to more systems, species, and questions, best practices can be helpful guides to make the most efficient use of limited resources. Here, we conduct a systematic literature review to synthesize the effects of temporal genomics methodology on our ability to detect evolutionary changes. We focus on studies investigating recent change within the past 200 years, highlighting evolutionary processes that have occurred during the past two centuries of accelerated anthropogenic pressure. We first identify the most frequently studied taxa, systems, questions, and drivers, before highlighting overlooked areas where further temporal genomics studies may be particularly enlightening. Then, we provide guidelines for future study and sample designs while identifying key considerations that may influence statistical and analytical power. Our aim is to provide recommendations to a broad array of researchers interested in using temporal genomics in their work.

This dataset was collected via a literature search in Web of Science with the following 12 keywords: temporal genomics, "temporal genomics," temporal genetics, "temporal genetics," hDNA, "historical DNA," historical DNA, aDNA, "ancient DNA," ancient DNA, museum DNA, and "museum DNA". The literature search was conducted on February 16, 2021.

We retained studies from 2000–2020 that investigated genetic or genomic change in wild animal populations using temporal samples from no earlier than 1800. To do this, we first filtered our literature search results with the Literature_Search_Markdown.Rmd script to remove duplicates, non-journal entries, and articles published before the year 2000. Studies were also removed based on research area, source title, Web of Science category, author keywords, and article title. We then conducted a title/abstract filter by hand, following the guidelines listed in Database_Guidelines.docx. Finally, when we recorded data for each paper, we also filtered out papers based on the same guidelines as used for the title/abstract paper (for the studies that were missed during the initial filtering steps).

There are no special programs and/or software required to open the data files. All data files are saved in either CSV or XLSX format and can be opened in many different programs (ex: Excel & Google Sheets).