Raw Dataset Downloads

Public access to FeederWatch data

The Cornell Lab of Ornithology and Birds Canada are committed to making data gathered through our programs freely accessible to students, journalists, and the general public. Trend graphs, summary tables by state, and more are all accessible online in the Explore section of the FeederWatch web site.

Raw data access for research scientists

Researchers seeking to conduct formal analyses using FeederWatch data are invited to download the raw data by following the links below. As with use of any data set, knowing the data structure, understanding the metadata, grasping the data collection protocols, and being cognizant of the unique aspects of the program are all critical for conducting analyses and interpreting results in ways that provide meaningful insights. Although the data are freely available, we invite researchers to consult with researchers at the Cornell Lab of Ornithology or Birds Canada (contact information below) to ensure that the data are being handled and analyzed in a meaningful way.

Note that raw data files are large (> 1.8 million checklists) and require proficiency in statistical software (e.g. SAS or R) or advanced database tools (e.g. MySQL, Microsoft Access). Project FeederWatch does not have the staff available to assist with these tools or to create custom subsets of the raw data. Nonetheless we are happy to provide access to the full dataset and instructions for how to use and interpret it.

Raw data access

The Project FeederWatch Data Dictionary explains all fields and codes used in the database and is essential for understanding the dataset.

Data files are in .csv format and will be downloaded to your computer when the link is clicked. Data are divided into multiple observation (checklist) files due to their large size (range is 380 MB – 1.3 GB):
checklist data 1988-1995
checklist data 1996-2000
checklist data 2001-2005
checklist data 2006-2010
checklist data 2011-2015
checklist data 2016-2020
checklist data 2021-2024
In addition, there is a single file containing supplementary information about the count locations (sites):
site description data (all years)

There is also a species translation table that has a list of the recognizable forms stored in the Cornell Lab of Ornithology database using a “species code” following eBird’s taxonomic system.

species translation table

Last update: 5 June 2024. Data are scheduled to be updated annually on or about June 1.

Important information to review before analyzing FeederWatch data

A paper describing the dataset is available for additional detail beyond what is described here: Over 30 years of standardized bird counts at supplementary feeding stations in North America: A citizen science data report for Project FeederWatch. 2021. D. N. Bonter and E. I. Greig. Frontiers in Ecology and Evolution, doi.org/10.3389/fevo.2021.619682.

Data validation
As with all large-scale participatory science programs, it is impossible to validate each of the millions of records submitted to FeederWatch. Although we attempt to minimize errors, a small percentage of FeederWatch reports are incorrect and analysts must be aware that misidentifications, data entry errors, and other sources of error can evade our data validation system.

All FeederWatch data are passed through a series of geographically and temporally specific filters that “flag” reports of species (or high counts) that are unexpected at a given location at a certain time of the year. The geographic resolution is relatively coarse (one filter per state/province), and the temporal resolution is monthly. Only reports that are flagged by the filters undergo a systematic manual review. A flag may be removed by the expert reviewer without a request for supporting information, or additional evidence may be requested. If additional information is requested but is insufficient to validate the report, that record remains in the database and is identified as an unconfirmed report. Flagged records are identified using a combination of the Valid field and the Reviewed field as defined here:

Valid = 1; Reviewed = 0
Interpretation: Report did not trigger the automatic flagging system and was accepted into the database without review

Valid = 1; Reviewed = 1
Interpretation: Report triggered the flagging system and was approved by an expert reviewer

Valid = 0; Reviewed = 1
Interpretation: Report triggered a flag by the automated system and was reviewed; insufficient evidence was provided to confirm the report

Valid = 0; Reviewed = 0
Interpretation: Report triggered a flag by the automated system and awaits the review process

Potential errors not captured by automated filters
The flagging system does not identify all potential errors. For instance, if a species is misidentified as another species that could occur in the region, that report will not be flagged for review. In other words, a Downy Woodpecker may be misidentified as a Hairy Woodpecker as these species are often sympatric. As such, we recommend that data analysts carefully consider which species are included in their analyses. We often lump difficult-to-distinguish species in our analyses. For instance, Carolina Chickadee and Black-capped Chickadee reports are analyzed as “chickadee species” in regions of geographic overlap. Similar lumping is suggested for Sharp-shinned and Cooper’s Hawks (Accipiter sp.), and for House, Purple, and Cassin’s Finches (Haemorhous sp.).

Additionally, errors in reporting can mimic errors in identification. Participants may intend to report one species but enter their information for the wrong species. The evolution of the data-entry process has created designs for paper forms and web pages that minimize the likelihood of such errors. Nevertheless, such errors are possible.

While we know that errors exist in the data, our experience based on handling and use of these data lead us to believe that such errors are generally minimal and that biologically real patterns will emerge from analysis of these data. All large data sets contain errors. We strive to minimize such errors, but nevertheless advise anyone analyzing these data to handle, analyze, and interpret these data with the understanding that these data are not perfect.

Effort data
As with any monitoring data, a recorded observation is a function of both the biological event (number of species actually present) and the observation process (probability that an individual, when present, will be observed). Detection probabilities can be formally estimated with FeederWatch data (see Zuckerberg et al. 2011 paper in list of FeederWatch publications). When formal estimation cannot be done, we strongly suggest that analysts minimally include predictors of the observation process, the effort expended by participants (number of half-days and/or number of hours of observation), as predictors in their statistical models, in order to describe increasing probabilities of observing birds with increasing time spent in making observations.

Zero-filling and Taxonomic Roll-up

For most uses, researchers will want to manipulate FeederWatch data in one or two of the following ways before using the data: 1) zero-filling and 2) “taxonomic roll-up.” Zero-filling is adding counts of zero birds for species that were not reported as being detected during an observation period, and is a key step because the data in its raw form is available as presence-only data. We use the term “taxonomic roll-up” to refer to the combining of subspecies and distinct taxonomic forms when multiple species codes exist for focal taxa. For a subset of species, FeederWatch participants can specify the level of recognizable form instead of reporting their observation at the taxonomic level of species (e.g., dark-eyed junco has many forms, including slate-colored and pink-sided). Therefore, under most circumstances, one needs to make sure all the data from a species has the same species code value.

To assist researchers analyzing FeederWatch data, we have provided R code that enables users to perform zero-filling and taxonomic roll-up. This resource is freely available in multiple formats via:

Public GitHub repository

Webpage

We reference two files in the documentation: 1) sample dataset using 2021 data, and 2) species translation table that has a list of the recognizable forms stored in the Cornell Lab of Ornithology database using a “species code” following eBird’s taxonomic system. The files can be accessed below:

Scientific Publications as a Resource for Analysts
Analysts will find previous publications informative in providing more detailed information on the process of analyzing FeederWatch data. See a list of scientific articles using FeederWatch data.

Personal data
FeederWatch participants are identified in the database by their unique Cornell Lab of Ornithology (CLO) or Birds Canada (BC) identification number. We do not share names, addresses, contact information, or any personal information about our participants without express permission from each individual participant. For confirmed rare bird reports, we may post reports along with the name, city, and state of the observer on the FeederWatch website, and we withhold any such reports from public view when asked. Please note that rare bird reports posted on our website may also be viewable on the rare birds map.

Acknowledging FeederWatch
Our unique dataset is completely dependent on the efforts of our network of volunteer participants. We ask that all data analysts give credit to the thousands of participants who have made FeederWatch possible, as well as to Birds Canada and the Cornell Lab of Ornithology for developing and managing the program.

Consulting with CLO or BC staff
Analyzing large data sets is complicated and requires skill in both conducting the analyses themselves but also in manipulating the data into the appropriate form for analysis. These data are best analyzed in collaboration or consultation with CLO or BC research staff who have experience working with FeederWatch data. Please note that our resources are limited, so the responsiveness and extent of support may be constrained. We will do our best to meet all requests as time and resources permit. We will concentrate our efforts on answering questions about the general processes of analyzing data from FeederWatch rather than the mechanics of using specific software to work through this process. Suggested contacts include:

Dr. David Bonter, Co-Director, Center for Engagement in Science & Nature, Cornell Lab of Ornithology: dnb23 at cornell.edu

Dr. Wesley Hochachka, Senior Research Associate, Cornell Lab of Ornithology: wmh6 at cornell.edu

Dr. Danielle Ethier, Senior Scientist, Birds Canada dethier@birdscanada.org