The presented dataset is a mirror of the online database “Online bird observation diaries” (Fig. 1), an Internet system for registering bird observations, created by Ilya Ukolov in 2013. The owner and customer of the database is the Moscow State University Zoological Museum (Kalyakin and Volzit 2019).

The database allows registered users to keep records of bird observations, see results of their colleagues, generate various analytical reports and much more. The main goals of creating an online database are:

• Creating an online publicly accessible database for the international scientific community;
• Keeping birdwatcher sightings from sinking into oblivion;
• Giving amateurs the opportunity to independently manage their data;
• Allowing participants to view results of their colleagues and to access summary information on the database;
• Generating summary reports on bird availability (by region, date, biotope or other features), species ranges, seasonal migrations;
• Providing an information basis for other ornithological programmes, such as preparation of the European Breeding Bird Atlas 2 (Keller et al. 2020, see also Kalyakin and Volzit 2019) and implementation of the programme “Birds of Moscow and Moscow Region” (Kalyakin and Volzit 2007, Kalyakin and Volzit 2008, Kalyakin et al. 2008).

From such a database, scientists can obtain extremely useful data on bird species distribution over time, detect species disappearing from a certain area, species invasions, migrations, occurrences of rare/endangered/non-indigenous species etc. Then they can use this information in scientific research or reports on national ecosystem services (Bukvareva and Zamolodchikov 2016).

The database was created on the Russian software “1C: Enterprise”, version 8.3 (1C Company 2015a, 1C Company 2015b), a popular environment for the development of business, home and scientific applications.

One of the important features of the database is an intuitive interface (different for PC and mobile devices), which allows users to easily upload their data and provides means for error control. The interface is provided with a map showing observation points. A user can set locations of occurrences directly on the map or manually, by entering coordinates in the text form, either by groups or one by one.

It is possible to build and execute queries and produce a variety of reports. The reporting system allows users to make a selection for any period, region, species and observer and sort the output by a number of fields. Moreover, there is a possibility to manually outline the research area and obtain a query for it (Fig. 2). Along with the fully-customisable report, there are various out-of-the-box reports that users need most. The results can be saved and downloaded in several widely used formats or printed out.

Useful functionality for ornithologists, ecologists and modellers include:

• various reports;
• a map with a wide range of data display functionality;
• possibility of data analysis in the context of 10 x 10 km squares according to European Russia (with the possibility of expanding to other types of grids and to other territories);
• data analysis for an arbitrary region within arbitrary boundaries (for example, for a Reserve);
• displaying the nesting status of observed species;
• integration with GBIF, EBP.

There are means for providing compatibility of the database with other ornithological projects. The first of them is the automatic import of data into the web GIS "Faunistics" (Karyakin et al. 2020a, Karyakin et al. 2020b). The second one is the connectivity with the squares of the pan-European Atlas of nesting birds (Fig. 3). The data from the dataset have already been used for the preparation of the European Breeding Bird Atlas 2 (Keller et al. 2020).

Being created by an amateur ornithologist, the database is well adapted for birdwatchers and possesses a number of special means related to birding activity including:

• e-mail subscription to interesting occurrences by selected region or by lifers;
• formation of regional checklists with the possibility of testing one's observation for completeness;
• a map of interesting places for observing birds (http://www.ru-birds.ru/mesta-nablyudenij.html) with automatically updated checklists;
• providing of the competition “Big Year” and rating “Club – 300” (observers who collected more than 300 species in the territory of Northern Eurasia);
• ability to control access to your own data (data closed from other users);
• displaying observation points on the map together with the species range;
• automatic identification of rare species for the region by checklist.

As an example of database analysis, let us consider the possibility of generating checklists. It is possible to generate a report for any region (both administrative and arbitrary), which presents a list of species in a given region, detailed by month. Different seasons are highlighted in colour (Fig. 7).

Russian Federation, 12 ex-USSR countries (Latvia, Kazakhstan, Belarus, Ukraine, Uzbekistan, Azerbaijan, Georgia, Armenia, Lithuania, Tajikistan, Estonia, Kyrgyzstan) and 12 more countries of Eastern and Western Europe (Great Britain, Belgium, Czech Republic, Luxembourg, Germany, France, Turkey, Finland, Poland, Hungary, Greece and The Netherlands).

There are two different ways of user interaction with the database, either through a browser (usually from PC; Fig. 4) or via the mobile interface (both Android and iOS applications are available; Fig. 5). Both web and mobile interfaces provide similar functionalities. For this, the web interface is best used when working with data, making queries and generating reports, while the mobile application is intended mainly for recording observations and recommended for use in the field, in real-time mode.

There are two main sampling methods: observation card and route card. The difference between them is that an observation card is essentially a checklist of observed birds together with their quantities and coordinates, while a route card is always attached to a certain route, which should be created separately. Specifically, a route card has additional fields, such as distance to the birds, the number of males and females, length of the route and time spent.

When working with an observation card, the observer enters observed species in each location together with the number of individuals. It can be done either manually or by selecting the species from the reference species list. Manual entry activates the auto-complete function, which greatly simplifies the input, allowing them to select the desired species by its first letters.

The most convenient way to indicate the location is through using a mobile application in real-time mode. In this case, the coordinates are determined by the mobile device and automatically entered in the field. When working from a PC, the user can enter the location by clicking on the digital map. Finally, the coordinates field can be filled or edited manually.

The database is filled both by professional ornithologists and amateurs; therefore, it is not immune to mistakes in species identification. That is why it possesses a number of means for control of data quality. Some potential errors are prevented at the stage of data entry due to the thought-out interface.

One of them is the standardised list of species (Koblik and Arkhipov 2014), which not only prevents typos, but also provides compatibility of the database with GBIF and other databases. When entering data, the programme automatically gives a hint, suggesting a list of species that can be found in the selected area. When a card is completed, it is checked for compliance with the region's checklist and, in case discrepancies are found (such as new species for the region), a warning is issued about the need for verification. Rare finds are manually checked by the database moderators separately. If needed, the identification can be corrected or, alternatively, commented in a special field of the database.

Another useful feature is the automatic determination of coordinates (by clicking on the map when working from a PC or fully automatically when using a smartphone on-the-go). Finally, some fields, such as date, can be also pre-filled automatically.

All these greatly reduce the likelihood of an error.

The dataset covers bird occurrences from 25 countries, including Russia, ex-USSR and some other European countries (Fig. 6). Most of the occurrences (488907 occurrences or 93%) belong to Russia. Together with five other countries, it covers 99% of the occurrences. These countries are Latvia (14401 occurrences), Kazakhstan (6568 occurrences), Belarus (4768 occurrences), Ukraine (3777 occurrences) and Great Britain (1051 occurrences). The remaining 1% of the database (4815 occurrences) is represented by 19 countries, listed in descending order: Uzbekistan, Azerbaijan, Georgia, Armenia, Belgium, Lithuania, Tajikistan, Estonia, Czech Republic, Luxembourg, Germany, Kyrgyzstan, France, Turkey, Finland, Poland, Hungary, Greece and The Netherlands.

36.782 and 81.522 Latitude; 20.933 and -169.614 Longitude.

The taxonomic coverage of the dataset includes 25 orders of birds, following GBIF Backbone Taxonomy (GBIF Secretariat 2021). More than 90% of occurrences belong to six orders: Passeriformes, Charadriiformes, Anseriformes, Accipitriformes, Piciformes and Columbiformes (Table 1).

This work is licensed under a Creative Commons Attribution Non-Commercial (CC-BY-NC) 4.0 Licence.

RU-BIRDS.RU, Birds observations database from Russia and neighbouring regions. Zoological Museum of M.V. Lomonosov Moscow State University.

https://www.gbif.org/dataset/ba19fc1d-670c-426b-b99d-49f003792ac4

https://doi.org/10.15468/5cjx70, http://www.ru-birds.ru/temp/dwca-rubirds-ru.zip, 672abba8-f26b-4256-ae27-e820258f5c8a

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