This is a guest post from Dag Endresen, who is the manager for the Norwegian participant node of the Global Biodiversity Information Facility (GBIF).
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Are you interested in natural history? Help us to capture label information from images of specimens from the Norwegian natural history collections in Oslo. The Natural History Museum at the University of Oslo (NHM-UiO) and GBIF-Norway has released a new citizen science crowdsourcing portal for transcription of specimen label information (figure 1). This new portal was developed in collaboration with the Notes from Nature (NfN) team and follows the design example established by NfN (Hill et al. 2012). The crowdsourcing portal was presented last week for the 200-year jubilee of the Botanical Garden in Oslo (NHM-UiO)

Figure 1: A new citizen science platform to capture label information from photographs of specimens in the natural history collections of Norway. Available at http://gbif.no/transcribe/

Primary biodiversity information (where, when and what):

Data for biological species on where, when and what define the so-called primary biodiversity information and is recognized as the minimum information requirement for fundamental biodiversity scientific research activities. Species distribution modelling is one of the important research tools for understanding the ecology of species, and is dependent on available primary biodiversity information (where, when and what) (Soberón and Peterson 2004). Other notable uses of primary biodiversity data includes refining range map boundaries, understanding inventory completeness, validating knowledge about habitat associations, understanding trait variation, providing knowledge about preparations that include tissues, and more.

The total number of museum specimens in worldwide natural history collections are estimated to be somewhere between 1.2 billion and 3 billion (Ariño 2010, Duckworth et al. 1993). The specimens archived in the natural history museums during more or less 250 years, provide a unique resource for understanding biological and ecological processes across time and place. A very low proportion of these museum specimens have been recorded electronically into databases, perhaps as low as 5% for many collections (Krishtalka and Humphrey 2000). The biggest challenge for efficient utilization of the natural history collections remains the lack of access to electronic information on the specimens (Smith and Blagoderov 2012, Pensoft Publishers 2012). Large-scale digitization activities, such as the prioritized and ongoing digitization of the natural history collections in Norway, are forced to focus on the registration of only the very bare minimum information for as many specimens as possible to make any reasonable progress with these enormous specimen collections. To maintain maximum speed of digitization, specimens are photographed and only the scientific name of the species and the country where the specimen was collected is registered. The specimen images include a visual representation of the label and for most specimens this is the only source of information regarding the collecting site and the data when the specimen was collected (the so-called fundamental primary biodiversity information of where, when and what). Crowdsourcing the electronic registration of these details from the specimen labels could be one realistic approach to capture this information in a reasonable timeframe (Hill et al. 2012).

Why participate and contribute to citizen science transcription:

* Discovery of biodiversity information: Transcription of label information and electronic registration into online databases greatly improve the discoverability of museum specimens for the purpose of scientific research and other public use.
* Education: Students from high school level to graduate and post-graduate level can engage with the photographs of the museum specimens and take part in a first class learning experience in interaction with this resource of primary biodiversity information.
* Scientific research: Scientists that study natural history need readily access to primary biodiversity information made available from museums and their online databases. Using the transcription portal they can directly take part in making the primary biodiversity information they need for their own research available by transcribing the labels for the respective species groups and or countries that they study.
* Public good, open and free online biodiversity information: The information that we gather from the transcription portal will flow into the museum specimen database and be published to open and free data portals such as the Global Biodiversity Information Facility (GBIF) and the Encyclopedia of Life (EOL). The valuable information to document historic biodiversity patterns are thus preserved not only for future generations, but also made available for current research using up-to-date and modern web technologies.

Digitization of natural history collections in Norway:

The collections at the Natural History Museum in Oslo include an estimated total of more than 6 million specimens (Mehlum et al., 2011). The collections in Oslo are estimated to hold more than 65% of the specimens held by natural history museums in Norway. The digitization of the Norwegian natural history collections has high priority and has reached a level of more than 50% of the specimens recorded and added into an electronic database system. This is a high proportion digitized when compared to other large natural history collections worldwide, but the estimated efforts required to complete the appropriate registration of all remaining specimens are enormous. The Natural History Museum in Oslo started a large-scale digitization activity in 2013 where specimens are photographed and only the very minimum information of the scientific name and the country where the specimen was collected is registered. Capturing additional information such as the collecting location (where), collecting date (when) and the verified current scientific name (what) will substantially increase the scientific value of these data records.

Lichen herbarium, Hildur Krog collection from eastern Africa:

The first specimen collection that has been loaded to the Norwegian citizen science transcription portal includes lichens collected by the Norwegian biologist Hildur Krog and others in East Africa. Professor Hildur Krogh was originally introduced to limnology as a student of professor Eilif Dahl (1916-1993). Eilif and Hildur pioneered the work on chemical methods for identification of lichen species. Hildur was appointed curator of the lichen herbarium at the Botanical Museum of the University of Oslo in 1971. Between 1972 and 1996 Hildur Krog and T.D.V. Swinscow explored systematically the lichen genera of East Africa for the development of the flora “Macrolichens of East Africa” (figure 2). Already more than 10,000 specimens from Africa has been digitized and registered in the museum database, but still some 2,850 exist only as image files. We are asking the help from volunteer citizen scientists to register the label information from these image files. The imaging of this collection was made late 2013 and early 2014 by Silje Larsen Rekdal and Even Stensrud under the coordination of lichen curator Einar Timdal and Siri Rui (NHM-UiO) and with funding from GBIF-Norway (figure 3).

Figure 2: Collecting sites for the already registered East African Macrolichens collected by Hildur Krog et al. (1972-1996). The images loaded to the crowdsourcing portal describe specimens from similar locations in the same region.

Figure 3: Curator Einar Timdal photographing specimens from the lichen type herbarium at the Natural History Museum in Oslo, January 2013. Photo: Dag Endresen (NHM-UiO) CC-by-4.0.

Notes from Nature as a platform for connecting crowdsourcing portals
The aim is to integrate this Norwegian crowdsourcing portal for Lichen specimens better into the NfN platform. The NfN site provides already a gateway overview to natural history collections and herbaria datasets from several distributed institutions. So far, all of these connected datasets use transcription software provided as an integrated part of the NfN portal. However, to allow the NfN platform to grow, we propose a slightly modified model where NfN could act more as a gateway “consortium” platform with more flexibility for individual datasets of specimen images to run on locally installed transcription services, including services using different software implementations. The appropriate role of NfN could be more of the entry point portal and a common gateway for volunteer citizen scientists, and less of the actual technical software solution for making the transcriptions. A more modular design would also make it easier for a team of somewhat loosely connected developers to improve on smaller parts of the system in a collaborative manner.

Participating institutes could use a simple web interface, such as a standardized json format, for reporting progress and other key properties of sub-collections that are offered for transcription to volunteer citizen scientists. Technical solutions such as OpenId login could provide easier user registration and synchronization of user information for users contributing to the transcription of many datasets.

References

Ariño A (2010). Approaches to estimating the universe of natural history collections data. Biodiversity Informatics, 7:81–92.

Duckworth WD, Genoways HH and Rose CL (1993). Preserving Natural Science Collections: Chronicle of our Environment Heritage. Washington, DC: National Institute for the Conservation of Cultural Property 140 pp.

Hill A, Guralnick R, Smith A, Sallans A, Gillespie R, Denslow M, Gross J, Murrell Z, Conyers T, Oboyski P, Ball J, Thomer A, Prys-Jones R, de la Torre J, Kociolek P, Fortson L (2012). The notes from nature tool for unlocking biodiversity records from museum records through citizen science. ZooKeys 209: 219-233. doi: 10.3897/zookeys.209.3472

Krishtalka L and Humphrey PS (2000). Can natural history museums capture the future? BioScience 50(7): 611–617. DOI:10.1641/0006-3568(2000)050[0611:CNHMCT]2.0.CO;2

Mehlum F, Lønnve J, and Rindal E (2011). Samlingsforvaltning ved NHM – strategier og planer. Versjon 30. juni 2011. Naturhistorisk museum, Universitetet i Oslo. Rapport nr. 18, pp. 1-89. ISBN: 978-82-7970-030-2. Available at http://www.nhm.uio.no/forskning/publikasjoner/rapporter/NHM-rapport-18-samlingsplan.pdf, accessed 28 May 2014.

Pensoft Publishers (2012). No specimen left behind: Mass digitization of natural history collections [special issue]. Editors: Blagoderov, V. and Smith, V. ZooKeys 209: 1-267. ISBN: 9789546426451. Available at http://www.pensoft.net/journals/zookeys/issue/209/

Smith VS and Blagoderov V (2012). Bringing collections out of the dark. ZooKeys 209: 1-6. DOI: 10.3897/zookeys.209.3699

Soberón J and Peterson AT (2004) Biodiversity informatics: managing and applying primary biodiversity data. Trans. R. Soc. Lond., B, Biol. Sci., 359, 689–698. DOI:10.1098/rstb.2003.1439

A year ago to this day, on Earth Day 2013, we launched Notes from Nature. Our choice of launch date reflects our hope that Notes from Nature can be a part of the Earth Day mission of creating a sustainable, green future. Every transcription that has been done over the last year brings a new data point onto our map of biodiversity. Taken together, we can finally assemble richer and deeper understanding of this amazing world all of us inhabit. With that knowledge, we can be better able to make wise choices about our planet’s future. Whether bugs, mushrooms, plants, birds or the myriad millions of species described or undescribed, our world is brimming with life, and our understanding deepened and stregthened with the meticulous efforts to collect and catalog that diversity.

So we are having a birthday party, in case you couldn’t guess from our front page, and we want you to join us! Come celebrate Earth Day and Notes from Nature both. Help us do something we never even thought possible — help us get our 500,000th transcription. We are that close, and we think it might be possible to get over the hump on our birthday. How cool would that be?

We mostly wanted to take the opportunity today to again thank every single transcriber out there for even tackling one record on Notes from Nature. We continue to be awed and amazed and appreciative of the effort you put in. We are pleased to have some new and exciting features coming your way at Notes from Nature in the next days and weeks and we hope we can keep this engaging and fun and productive in the long term for everyone. We also hope that the cool specimens and organisms you see on Notes from Nature speak for themselves. We think they tell the story of this green planet, on Earth Day no less, better than anything else.

Thanks again and take a second to wish us a happy one year anniversary!

We have put together a list of frequently asked questions and useful tools related to transcribing records. These are  based on discussion threads in the NfN discussion forums (linked at the bottom). Please take a look, and let us know if there are any questions we missed or useful tools that you can suggest. These will eventually go on a page of the Notes from Nature website.

NOTE: The herbarium interface (SERNEC), now has it’s own FAQ. It can be found here: https://blog.notesfromnature.org/2015/04/14/updated-faq-and-useful-tools-herbarium-interface/

We really appreciate your input and all that you do for Notes from Nature. Thank You!

Common issues or questions that you may encounter while transcribing Notes from Nature records:

1.) Interpretation: In general, you should minimize interpretation of open-ended fields and enter information verbatim. This way, we can better achieve consensus when checking multiple records against one another (see below, on that process). However, some discretion would be nice. Here are examples:

Interpretation that you should make: Simple spacing errors (e.g. “3miN. of Oakland” should be “3 mi N. of Oakland”)

Interpretation you should leave to us: Don’t interpret abbreviations, we’ll sort that out. (e.g. “Convict Lk.” )

2.) Not in English: Transcribe exactly as written. Match label content to transcription fields as best as you can.

3.) Abbreviations: Transcribe exactly as written.

4.) Spelling mistakes: Transcribe exactly as written, unless you have looked it up and are absolutely certain of a simple spelling mistake. In this case, you can enter the correct spelling.

5.) Problem records: If you come across a problem record that may need to be addressed by a scientist, like a faulty image or a record with illegible handwriting, you can flag the record by commenting on it (e.g. with the hashtag #error) and indicate what is in error.

6.) Provinces: Provinces go in the Location field (e.g. Coastal Plain Province, Piedmont Province).

7.) Capitalization: Sometimes information may be in all capital letters on the labels. Unless this is an abbreviation, you should capitalize only the first letter of every word in your transcription (e.g. “COASTAL PLAIN PROVINCE” should be “Coastal Plain Province”).

8.) Many collectors: In many cases, collectors may be listed on different lines of the label with no punctuation separating them. In your transcription, please separate the collectors with commas.

9.) Missing information: What should you do when there is no information available for a field? When information is not given on the label, you should leave the field blank (in the case of open-ended fields) or select “Unknown” or “Not Shown” in the drop-down lists.

10.) Inconsistent collector names: You will often find several variations of the same collector name (e.g. “R. Kral” or “R.Kral”, “RWG” or “R.W.Garrison”). Use similar discretion when transcribing these variations as in the localities.

Interpretation that you should make: Simple spacing errors (e.g. “R.Kral” should be “R. Kral”)

Interpretation you should leave to us: Don’t interpret abbreviations, we’ll sort that out. (e.g. “RWG” should remain “RWG”)

11.) Many scientific names: For Calbug, you do not need to enter the species name (we have this info already), but if there is a scientific name that is different from what is listed in the record, put it into the “Other Notes” field. These could be old names, or plant host names.

For SERNEC Herbarium specimens, copy only the most recent name. This can be determined based on the date that appears on the ‘annotation label.’ If you do not see a date then enter the name that appears on the primary label.

12.) Variations and subspecies (SERNEC): Record the subspecies, but omit the scientific author’s name. So “Cyperus odoratus var. squarrosus (Britton) Jones, Wipff & Carter” becomes “Cyperus odoratus var. squarrosus”. “Echinodorus cordifolius (Linnaeus) Grisebach ssp. cordifolius” becomes “Echinodorus cordifolius ssp. cordifolius”.

13.) Scientific name (SERNEC): Provide the most recent name, whether it is a species name (a two-word combination of the genus and what is called the “specific epithet” in botanical nomenclature) or a one-word name that is at a higher taxonomic rank (e.g., just the genus or family name). Names at higher taxonomic ranks than species are used when a more precise identification has not been made.  The species name should typically take the form of a genus name that begins with a capital letter and a specific epithet that begins with a lowercase letter.  If any of the names are given in all capitals, such as “CYPERUS ODORATUS”, the name should be entered using the typical convention, “Cyperus odoratus” in this case.

14.) Latitude and Longitude: How do you enter latitude and longitude values, and where do these values go? Enter exactly as written, you can find symbols in Word or by searching online (e.g. 33° 62’ 22” N  116° 41’ 42” W). You can also produce the degree symbol ° using key combinations (alt + 0 on a mac; alt + 0176 on a PC, with the key pad on the right side of your keyboard). This information should go into the “Location” or “Locality” field, depending on the project you are working on.

15.) Special Characters: What should you type when there is a special character in a text string, such as a degree symbol or language-specific characters? You can do a google search for the symbol or copy and paste it from Microsoft Word symbols. There are also key combinations for common symbols. As mentioned above, you can produce the degree symbol ° using key combinations (alt + 0 on a mac; alt + 0176 on a PC, with the key pad on the right side of your keyboard).

16.) Elevation: Enter elevation verbatim into the “Other Notes” field for Calbug and the “Habitat and Description” field for SERNEC Herbarium records.

17.) County: If the county is not given on the label, please find the appropriate county using google search. However, if there are multiple potential counties for a locality, please leave the county field blank.

18.) Checking your transcription: You can use the link to the left of the “Finish Record” button (e.g. “1/9” or “9/9”) to check the information that you entered. Just click on any of the fields to make any necessary edits to your transcription.

19.) When is a record finished?: These blog posts describe the data checking process that uses 4 transcriptions of the same record to derive a consensus.

https://blog.notesfromnature.org/2014/01/14/checking-notes-from-nature-data/

http://soyouthinkyoucandigitize.wordpress.com/2014/01/14/412/

Some Useful Tools (discovered by NfN users)

Counties and Cities: Good tools for finding counties etc. are lists on wikipedia, there are lists of municipalities in each state of the USA (there are also similar lists for others). For example, https://en.wikipedia.org/wiki/List_of_municipalities_in_Florida (via the linkbox you can also change the state).

Mountains: https://en.wikipedia.org/wiki/Category:Lists_of_mountains_of_the_United_States

Uncertain Localities: Geographic Names Information System, U.S. Geological Survey.

https://geonames.usgs.gov/pls/gnispublic

Mapping tool with topo quads: To find uncertain counties or localitieshttp://mapper.acme.com

Collector Names: The Essig Museum of Entomology database has lists of collector names and periods of activity for many collectors you will find in Calbug records.  http://essigdb.berkeley.edu/query_people.html

Hard-to-read text: Use “Sheen”, the visual webpage filter, for some hard-to-read handwriting written in pencil. (Tip was from the War Diary Zooniverse project) https://chrome.google.com/webstore/detail/sheen/mopkplcglehjfbedbngcglkmajhflnjk?hl=en-GB

Special symbols: You should be able to find symbols in word or by doing a google search and copy and paste. Here are a few:

–           degree symbol for coordinates:  °

–          plus minus: ±

–          fractions: ⅛ ¼ ⅓ ⅜ ½ ⅝ ⅔ ¾ ⅞

–          non-English symbols: Ä ä å Å ð ë ğ Ñ ñ õ Ö ö Ü ü Ž ž

The Plant List: Search for scientific names of plants –http://www.theplantlist.org/

List of Trees:   https://en.wikipedia.org/wiki/Category:Trees_of_the_United_States

Integrated Taxonomic Information System (ITIS): http://www.itis.gov/

Essig database (for Calbug): Unsure if you spelled a collector name or species name right? It might also be worth checking similar entries already in the database, http://essigdb.berkeley.edu/.

NfN Discussion threads that this is based on:

FAQs

http://talk.notesfromnature.org/#/boards/BNN0000001/discussions/DNN000024q

Useful Tools

http://talk.notesfromnature.org/#/boards/BNN0000001/discussions/DNN00001vl

This article was written by Matthew Foltz, who is the manager of the Macrofungi digitization project at the University of Michigan.  We commissioned this article because most of the labels that are currently available  for transcription in Macrofungi are from this institution.  The University of Michigan Herbarium has an unparalleled history of contributions to the scientific study of fungi, and also for contributions to bringing an understanding of fungi to a general audience.

The University of Michigan Herbarium is internationally recognized as one of the leading repositories for natural history collections including Vascular Plants, Bryophytes (that is, mosses and related plants), Algae, Lichens, and Fungi. It has a rich history dating back to the late 1830s when state geologist Douglass Houghton conducted a geological survey of Michigan and deposited about 800 collections at the University of Michigan.  The herbarium is now home to over 1.7 million specimens of plants and fungi collected from around the world, including about 280,000 collections of fungi.

Figure 1. The University of Michigan Herbarium houses over 1.7 million collections in 1,200 cabinets in a 16,000 square-foot climate-controlled range. Photo by M. Foltz.llections of fungi.

The University of Michigan Herbarium has strong roots in mycology. In 1921 the herbarium became its own department under the directorship of mycologist Calvin H. Kauffman. Directorship of the herbarium went on to several other mycologists in the 1900s including Edwin B. Mains, Alexander H. Smith, and Robert L. Shaffer. Calvin Kauffman was a mycological and scientific pioneer. His publication on the Agaricaceae of Michigan (as well as the series of fungal monographs that preceded it) was not only the most comprehensive for the state, but at the time it was also one of the best fungal surveys for North America. The family Agaricaceae contains the common grocery store mushroom.

Figure 2. Calvin H. Kauffman. Photo courtesy of University of Michigan.

To truly appreciate the impact that Kauffman and his predecessors have had on mycology, one needs to look no further than the Kauffman Lineage which is a part of Meredith Blackwell and Robert Gilbertson’s Genealogy of North American Mycologists. This lineage of education includes many of the most significant mycologists of all time, including some of today’s most prominent scientists.

The fungal collection at the herbarium is strong in both Macrofungi (mushrooms and shelf fungi) and microfungi (molds and mildews, etc.). Kauffman’s work was strong in the agarics (mushrooms) of the Great Lakes region, but he also collected in many localities across North American and internationally. Alex Smith followed in his footsteps and went on to become a leading expert of the agarics as well as other groups such as the gasteroid fungi (puffballs, earthballs, stinkhorns, etc.) and the boletes. Like Kauffman, Smith contributed to both the Great Lakes region flora, and the North American flora. Smith was a prominent collector and deposited over 92,000 specimens at the herbarium during his lifetime.

Figure 3. Dow V. Baxter. Photo courtesy of University of Michigan.

The value and depth of the Michigan fungal collection is strengthened by Dow Baxter’s extensive collection of wood-decay fungi, along with collections of agarics from R.L. Shaffer and hypogeous (underground) fungi from R. Fogel. The Michigan herbarium also owns several classical exsiccati (historical sets of reference collections), as well as historically important personal collections from H.A. Kelly, H.C. Beardslee Jr., and many others. In addition to macrofungi, Michigan also has extensive collections of microfungi from prominent mycologists including Bessie B. Kanouse (discomycetes), E.B. Mains (Uredinales, Geoglossaceae, insecticolous fungi), L.E. Wehmeyer (pyrenomycetes), F.K. Sparrow (aquatic fungi), and others.

Michigan mycologists have a history of supporting citizen science and collaborating with amateur mycologists and the general public. Alex Smith was an advisor and supporter of the premier amateur mycology group the North American Mycological Association (NAMA) in its early days.

Figure 4. Alexander H. Smith. Photo courtesy of University of Michigan.

Smith was a close friend to many of the top “amateur” mycologists such as Ellen Trueblood, Virginia Wells, Phyllis Kempton, and others.

These mycologists kept detailed records and notes with their specimens, and these important collections and their ancillary materials are deposited at the Michigan herbarium. More recently, Robert Fogel, a past curator of fungi at Michigan, was one of the first mycologists to create a website for learning about fungi in the 1990s (Fun Facts About Fungi).

The tradition of outreach continues today through the efforts of mycologist Tim James, the assistant curator of fungi at the herbarium. Recently, James has held lectures and forays for several amateur groups including the Michigan Botanical Club and the Michigan Mushroom Hunters Club. He is also leading the efforts at Michigan as part of the Macrofungi Collection Consortium, a nationwide project to digitize the fungal collections and make them available online to researchers and the general public. The digital records produced by that project are the ones posted here on Notes from Nature.

Figure 5. James lab (Umich) on a John Cage inspired mushroom foray with the Michigan Mushroom Hunters Club. Photo courtesy of T. James.

On behalf of the University of Michigan Herbarium, we thank you for your participation in this effort to transcribe information from these historical records.

As you enter information from old labels, have you ever wondered about the collector’s experience—20, 60, or even 100 years ago? Have you thought about what the river, lake or meadow was like?  Or how the place may have changed since the collector was there?  I have, and so I spent a couple of years revisiting sites originally sampled by C.H. Kennedy for dragonflies in 1914-1915. Check out my recent blog post on this experience, entitled An ode to collecting: following the path of an early 20th century dragonfly collector.

You may have wondered how we will use the data you are transcribing… Especially after you spend a few minutes trying to figure out the meaning of some unclear handwriting, and are uncertain whether the year on the label actually says “1976” or “1879,” or if the locality is “Vino” or “Vina” in California.

The first step in using Notes from Nature transcription data in research is checking data quality. Each specimen is transcribed independently by four different users, so mistakes in a single transcription do not impact the accuracy of our data very much. But how do we determine the right one out of the multiple transcriptions? For controlled fields such as country and state, this is simple: we perform a “vote-counting” procedure, where the value with the most “votes” is considered correct. However, this is much more difficult for open-ended fields such as locality or collector names.

One of the early approaches was to quantify differences between transcriptions. We did this by using the Levenshtein edit distance, named for the Russian information scientist, Vladimir Levenshtein, who developed a way to quantify the minimum number of single-character edits required to change one string of text to another. For example, “12 mi W Oakland” and “12 mi. W Oakland” would have a score of 1 because of the period after “mi”. We then looked for highly similar pairs of transcriptions for each specimen (pairs that were separated by the smallest edit distance), and then chose one of the pair. Our assumption was that if two separate transcriptions are almost identical to one another then either one must be very close to being correct. However, this assumption becomes violated if users are consistently wrong (either because the label is partially obscured, or the handwriting difficult to read, etc.). Furthermore, this was not a true consensus in the sense that our algorithm only decides which transcription out of the four is the best.

During the Hackathon last month (discussed in an earlier blog), it was quickly obvious that this approach was not ideal. In discussions with other researchers and experts, we developed an approach to combine the best elements of each individual transcription into a locality string that was greater than the sum of its parts. We did this using a concept from bioinformatics known as “sequence alignment.”

Genetics researchers and molecular ecologists use sequence alignment to match up multiple DNA or amino acid sequences in studies of evolutionary history or DNA mutation. To evaluate alignment, algorithms look for particular strings of amino acids or nucleotides within multiple sequences, and match similar or identical regions. Even closely related sequences, however, are often not identical and have small differences due to substitutions, deletions or insertions. So, alignment algorithms use complicated scoring mechanisms to generate alignments requiring as little “arrangement” as possible. The goal is to balance potential insertions, deletions and substitutions in characters from one sequence to the next.

In Notes from Nature data assessment, we use this method and assume that differences between transcriptions in a particular location may be due to substitutions. Or, we introduce gaps so that two separate chunks of sequence might be better aligned. This is especially useful if there are certain chunks of text within an individual transcription that are not consistently shared among all other transcriptions.

We implemented sequence alignment in two ways, using token and character alignment. For token alignment, we converted each chunk of text (usually individual words) and assigned each unique chunk a “token.” We then aligned the tokens. For character alignment, we compared each character in the text string directly. This proved to be the better method, as we inherently had a lot more information for the alignment software to work on.

An Illustration of Character and Token Alignment

To compare how well our algorithm was doing, we manually transcribed around 200 of Calbug’s specimens, and ran our algorithm on user transcriptions. 70% of the consensus transcriptions were either EXACTLY identical or within 5 edits to our manual transcriptions. While this result is preliminary and we’re still working on improvements, it is worth mentioning that the testing data set included quite a lot of the very worst examples of handwriting on a teeny tiny label.

Proportion of records that were exactly identical or within 5 edits to manual transcriptions 

Proportion of specimens that were a certain edit distance away from our manually-transcribed set of specimens

In closing, we’d like to emphasize the importance of typing exactly what you see in the open-ended fields for collector and locality. A certain degree of interpretation may be necessary. For example, if there are no apparent spaces between words (e.g. DeathCanyonValley) it would be useful to add them. But, it is often very difficult (probably impossible) to derive a consensus if the inconsistency of label data is compounded by inconsistencies in data entry. We have ways of dealing with the huge diversity of abbreviations, so it would be helpful if you enter them as they are when you see them with minimal interpretation. The overall lesson from our data quality comparisons is that so far we are doing great, thanks to your careful work!

Want to learn more? Check out a related blog post, here.

—

By Junying Lim