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6,575 results for “Bees”
Survey of Wild Bee Pollinators on Nyssa Sylvatica at Harvard Forest since 2021
Black gum (Nyssa sylvatica) is amongst the latest blooming canopy species to produce vast numbers of flowers and abundant nectar and pollen within forests of the Northeastern United States, a position previously held by the American Chestnut (Castanea dentata). Prior research indicates N. sylvatica is insect pollinated and wild bees have been observed visiting flowers; we are unaware, however, of any detailed surveys and/or characterization of the Nyssa-associated wild bee community in the Northeastern United States. Wild bee species frequent the canopy from early to late spring, presumably to forage, prior to being found in blooming crops such as apple and strawberry later in the season. The late bloom time of N. sylvatica (in early June) may extend floral resource availability in the temperate forest canopy and support forest-associated wild bee communities prior to the bloom of summer-flowering plant species.
Minneapolis-St. Paul Metro Area Residential Bee Lawn Survey, 2022
We surveyed Minneapolis-St. Paul (MSP) metropolitan region residents who have an interest in pollinators and pollinator habitat (e.g., bee lawns). Potential respondents were recruited from MSP pollinator-friendly email listservs. The survey was open from May 24, 2022 to June 30, 2022, and distributed to 494 individuals. Our final N, accounting for broken email addresses, non-responses, and less than 50% survey completion, is 256. The survey contains six categories of questions along with a set of panel data. Question categories include: home, yard, and lawn characteristics and observations; lawn care responsibilities and opinions; bee and bee lawn knowledge and opinions; formal and informal rules shaping lawn decisions; and lawn information sources, trust, authority, and advocacy. Survey respondents were primarily white, older, wealthy, and well-educated women. Our findings show that values influencing lawn management are primarily environmentally focused (e.g., pollinators, climate) rather than aesthetically focused (e.g., increases property values, fits neighborhood look). Generally, individuals reported that there are no—or they are unsure-- whether there are formal (e.g., ordinances), and informal (e.g., neighbor expectations) rules about lawn management. Respondents overwhelmingly report that University of Minnesota Extension is a highly trusted source of lawn management information. Finally, the majority of respondents promote or encourage alternative lawn management practices (e.g., bee lawns, pollinator gardens), primarily through informal activities (e.g., block parties, word of mouth).
Bee species abundance and composition in three ecosystem types at the Sevilleta National Wildlife Refuge, New Mexico, USA
This study was designed to examine community- or population-level fluctuations in bee species at the Sevilleta National Wildlife Refuge, both intra- and inter-annually. From 2002 to 2019, passive funnel traps were used to collect bees at three sites, each representing a different ecosystem type of the southwestern U.S. (Plains grassland, Chihuahuan Desert grassland, and Chihuahuan Desert shrubland). Bees were collected during each month from March through October, and were identified to species by taxonomic experts.
Occurrence Record Dataset from "Annotated checklist of the bees of Bonaire, with a focus on host plants"
<p>This is the occurrence dataset created for the publication "Annotated checklist of the bees of Bonaire, with a focus on host plants" (<a href="https://natuurtijdschriften.nl/pub/1026875" target="_blank" rel="noopener">https://natuurtijdschriften.nl/pub/1026875</a>).</p> <p>Observation and specimen data were assembled for this dataset, with the majority of records obtained during the Bonaire Estafette Expeditie (BEE). All citizen science records from Observation.org and iNaturalist.org up to December 2023 have been critically reviewed.<br>A project was created (<a href="https://www.inaturalist.org/projects/flower-visitors-and-pollinators-of-the-caribbean" target="_blank" rel="noopener">Flower visitors and pollinators of the Caribbean</a>) to improve standardized data collecting of plant-pollinator interactions and on <a href="https://observation.org/">observation.org</a> the standardized fields for interactions were used.<br>Records from passive trapping methods are not included. All bees were either observed or collected by hand or insect net. The majority of specimens will be accessible in the collection of Naturalis Biodiversity Center (RMNH), Leiden (the Netherlands). A synoptic collection is retained at the University of Tartu Zoological Collections in Tartu, Estonia (TUZ).</p> <p>The occurrence dataset (Version 1.4 and later) is:</p> <ul> <li>conform Darwin Core (DwC): <a href="https://dwc.tdwg.org/terms/">https://dwc.tdwg.org/terms</a></li> <li>in the data format CSV (tab delimited values) and UTF-8 encoded</li> </ul> <p> </p> <p><strong>DwC terms (Column labels) used in the dataset with their description:</strong></p> <table> <tbody> <tr> <td><strong>Column label</strong></td> <td><strong>Column description</strong></td> </tr> <tr> <td>occurrenceID</td> <td>Unique identifier or URI (GUID) for each record, mainly unique URLs generated by the web-based data holder.</td> </tr> <tr> <td>catalogNumber</td> <td>Unique code derived from URI in occurrenceID. Each specimen bears a label with this identifier and multimedia are tagged with this identifier.</td> </tr> <tr> <td>recordNumber</td> <td>Sample field ID used to manage data of preserved specimen occurrence records.</td> </tr> <tr> <td>otherCatalogNumbers</td> <td>Other unique identifiers used on specimen labels, but not derived from an URI.</td> </tr> <tr> <td>scientificName</td> <td>The scientific name of the lowest taxonomic rank to which the individual(s) was identified.</td> </tr> <tr> <td>scientificNameAuthorship</td> <td>The author name and year of publication in accordance with ICZN rules.</td> </tr> <tr> <td>verbatimIdentification</td> <td>The original identification, including qualifiers if needed.</td> </tr> <tr> <td>individualCount</td> <td>The number of individuals present at the time of the occurrence.</td> </tr> <tr> <td>sex</td> <td>The sex of the individual(s). The values female, male or unknown are used, if a mixed group is observed multiple values are listed.</td> </tr> <tr> <td>lifeStage</td> <td>The life stage of the individual(s).</td> </tr> <tr> <td>basisOfRecord</td> <td>The specific nature of the data record at the time of the identification (e.g. PreservedSpecimen).</td> </tr> <tr> <td>identifiedBy</td> <td>The name of the person who made the identification in the field or based on collected evidence (e.g. specimen or photo).</td> </tr> <tr> <td>identificationQualifier</td> <td>In case the identification could be given only to a species group 'cf.' is recorded.</td> </tr> <tr> <td>dateIdentified</td> <td>The year when the identification was made.</td> </tr> <tr> <td>previousIdentifications</td> <td>The scientific name originally given to the observed or collected individual(s).</td> </tr> <tr> <td>order</td> <td>The name of the order (e.g. Hymenoptera).</td> </tr> <tr> <td>family</td> <td>The name of the family (e.g. Apidae).</td> </tr> <tr> <td>genus</td> <td>The name of the genus (e.g. Apis).</td> </tr> <tr> <td>subgenus</td> <td>The name of the subgenus (e.g. Apis).</td> </tr> <tr> <td>specificEpithet</td> <td>The name of the species, epithet as given in dwc:scientificName.</td> </tr> <tr> <td>taxonRank</td> <td>The taxonomic rank of the most specific name in dwc:scientificName.</td> </tr> <tr> <td>eventDate</td> <td>The date-time when the event was observed and recorded. The event date uses the ISO 8601-1:2019 standard, with the following formatting being used: format YYYY-MM-DD, or YYYY if only the year is known. If time of capture is known, then format is YYYY-MM-DDTHH:MM, with HH:MM the local time.</td> </tr> <tr> <td>year</td> <td>The year in which the event was observed and recorded.</td> </tr> <tr> <td>month</td> <td>The month in which the event was observed and recorded.</td> </tr> <tr> <td>day</td> <td>The day in which the event was observed and recorded.</td> </tr> <tr> <td>eventTime</td> <td>The time or interval during which the event occurred.</td> </tr> <tr> <td>samplingProtocol</td> <td>The name or description of the collecting or recording method used.</td> </tr> <tr> <td>behavior</td> <td>A description of the behavior shown by the individual(s) recorded in this occurrence.</td> </tr> <tr> <td>decimalLatitude</td> <td>The geographic latitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>decimalLongitude</td> <td>The geographic longitude in decimal degrees recorded by a GPS device (WGS84) when observing and recording the occurrence.</td> </tr> <tr> <td>geodeticDatum</td> <td>The ellipsoid, geodetic datum, or spatial reference system (SRS) upon which the geographic coordinates given in dwc:decimalLatitude and dwc:decimalLongitude is based.</td> </tr> <tr> <td>verbatimLocality</td> <td>The original textual description of the place.</td> </tr> <tr> <td>island</td> <td>The name of the island.</td> </tr> <tr> <td>countryCode</td> <td>The standard ISO 3166-1 alpha-2 country code for the country.</td> </tr> <tr> <td>coordinateUncertaintyInMeters</td> <td> <p>The horizontal distance (in meters) from the given dwc:decimalLatitude and dwc:decimalLongitude describing the smallest circle containing the actual location, usually the EPE (Estimated Position Error) from the GPS device. The EPE is here measured as the horizontal position error in meters.</p> </td> </tr> <tr> <td>recordedBy</td> <td>A person, group, or organization observing and recording the occurrence.</td> </tr> <tr> <td>associatedTaxa</td> <td>The type of association and the scientific name of the host taxon is recorded that is associated/has relationship with the taxon in dwc:scientificName. The association/relationship is recorded using the format as in the following example: "floral host":"Lantana sp."</td> </tr> <tr> <td>occurrenceRemarks</td> <td>Comments or notes about the dwc:Occurrence.</td> </tr> <tr> <td>associatedSequences</td> <td>A list (concatenated and separated) of identifiers (publication, global unique identifier, URI) of genetic sequence information.</td> </tr> <tr> <td>typeStatus</td> <td>A list (concatenated and separated) of nomenclatural types (type status, typified scientific name, publication) applied to the subject.</td> </tr> <tr> <td>collectionCode</td> <td>The name, acronym, coden, or initialism identifying the collection or data set from which the record was derived.</td> </tr> <tr> <td>identificationRemarks</td> <td>Comments or notes about the identification.</td> </tr> <tr> <td>identificationReferences</td> <td>A reference or list of references (publication, global unique identifier, URI) used for the identification.</td> </tr> <tr> <td>nameAccordingTo</td> <td>A reference to the checklist or publication that was followed to record the name in dwc:scientificName.</td> </tr> <tr> <td>samplingEffort</td> <td>The amount of effort, expressed in minutes or hours, to obtain and record the occurrences.</td> </tr> <tr> <td>occurrenceStatus</td> <td>A statement about the presence or absence of a taxon during the time of an event.</td> </tr> <tr> <td>disposition</td> <td>The current state of a specimen with respect to a collection.</td> </tr> <tr> <td>language</td> <td>The language of the record using ISO 639-1 codes, e.g. en</td> </tr> </tbody> </table>
Minneapolis-St. Paul (MSP) Metro Area bee lawn assessments and bumble bee survey, 2023
Eighty residential lawns were recruited across the seven-county Minneapolis-St. Paul Metropolitan Area to take part in a survey of bumble bee species, vegetation and soil characteristics across lawns with differing management regimes. Lawns were selected to capture a range across the urban to rural gradient (based on percent of impervious surface) and household incomes and ethnicity at the Census block group level. Approximately half the lawns were characterized as "traditional" lawns, while half were considered "bee lawns" based on initial plant and bee community data. Three non-residential sites were included in this study: Katharine Ordway Natural History Study Area, The Minnesota Bell Museum of Natural History, the University of St. Thomas Stewardship Garden. The Bombus spp. composition was surveyed NON-LETHALLY at each site twice in the summer, with six endangered rusty-patched bumble bees (Bombus affinis) observed in 2023. Each property was also surveyed for plant species composition and soil moisture. Additional soil and site characteristics will accompany this dataset at a later date. Locations of the lawns are jittered randomly to protect the privacy of participating residents in the study.
Tab and comma delimited versions of Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila)
<p><span><em><strong>Introduction</strong></em></span></p> <p>This archive includes a tab-delimited (tsv) and comma-delimited (csv) version of the <a href="http://www.discoverlife.org/mp/20q?act=x_checklist&guide=Apoidea_species">Discover Life bee species guide and world checklist </a>(Hymenoptera: Apoidea: Anthophila). Discover Life is an important resource for bee species names and this update is from Draft-55, November 2020. Data were accessed and transformed into a tsv file in August 2023 using <a href="https://www.globalbioticinteractions.org/">Global Biotic Interactions</a> (GloBI) <a href="https://github.com/globalbioticinteractions/nomer">nomer</a> software. GloBI now incorporates the Discover Life bee species guide and world checklist in its functionality for searching for bee interactions.</p> <p><span><strong>Update! New Dataset also includes Subgenera Names</strong></span></p> <p>A new, tab-delimited version of the Discover Life taxonomy as derived from Dorey et. al, 2023 can be found via Zenodo at <a href="https://doi.org/10.5281/zenodo.10463762">https://doi.org/10.5281/zenodo.10463762</a>. This version of the Discover Life world species guide and checklist includes subgeneric names.</p> <p><span><strong>Citation</strong></span></p> <p><strong>Please cite the original source for this data as:</strong></p> <blockquote> <p><strong>Ascher, J. S. and J. Pickering. 2022.<br>Discover Life bee species guide and world checklist (Hymenoptera: Apoidea: Anthophila).<br>http://www.discoverlife.org/mp/20q?guide=Apoidea_species </strong>Draft-56, 21 August, 2022</p> </blockquote> <p><span><strong><em>nomer</em></strong></span></p> <p>nomer is a command-line application for working with taxonomic resources offline. nomer incorporates many of the present taxonomic catalogs (e.g., catalog of life, ITIS, EOL, NCBI) and provides simple tools for comparing between resources or resolving taxonomic names based on one or more taxonomic name catalogs. Discover Life is in nomer version 0.5.1 and this full dataset can be recreated by installing nomer from <a href="https://github.com/globalbioticinteractions/nomer">https://github.com/globalbioticinteractions/nomer</a> and running</p> <blockquote> <p>$ nomer list discoverlife > discoverlife.tsv</p> </blockquote> <p><span><em><strong>Data Columns</strong></em></span></p> <p>Discover Life provides a world name checklist and includes other names (synonyms and homonyms) that refer to the same species. In the tsv file, the provided name is both the accepted, or checklist name, or "other name." All names will be listed as a providedName. Below is an example subset of the transformed version of the data.</p> <ul> <li>providedExternalId= link to name on Discover Life</li> <li>providedName=an accepted or "<em>other name</em>" in the Discover Life bee checklist. "Other names" can be synonyms or homonyms.</li> <li>providedAuthorship=authorship for the providedName</li> <li>providedRank=rank of the providedName</li> <li>providedPath=higher taxonomy of the providedName. This will be the same as the accepted name or resolvedName</li> <li>relationName=relationship between the "<em>other name</em>" and the bee name in the Discover Life checklist. It may include itself</li> <li>resolvedExternalID=an <strong>accepted name</strong> in the Discover Life bee checklist</li> <li>resolvedExternalId=link to name on Discover Life</li> <li>resolvedAuthorship=authorship of the accepted, or checklist name</li> <li>resolvedRank=rank of the accepted, or checklist name</li> <li>resolvedPath=higher taxonomy of the accepted, or checklist name</li> </ul> <p><span><em><strong>Changes</strong></em></span></p> <p>No major changes to format in this version.</p> <p><span><em><strong>References</strong></em></span></p> <p>Jorrit Poelen, & José Augusto Salim. (2022). globalbioticinteractions/nomer: (0.2.11). Zenodo. https://doi.org/10.5281/zenodo.6128011</p> <p>Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Dorey, J.B., Fischer, E.E., Chesshire, P.R. et al. A globally synthesised and flagged bee occurrence dataset and cleaning workflow. Sci Data 10, 747 (2023). https://doi.org/10.1038/s41597-023-02626-w</p>
Xylocopa sonorina - UCSB-IZC00012194 - Bee Library - 73e389aa-5886-4c48-8778-ba8932d1bd7e hash://sha256/96bfde1efa599e0e8e61de18b14d61dd308737f684950e4079c04e9bc0f33958 hash://md5/4940f68c84cffa4412f7ffb98bb255bd
<p>A biodiversity dataset graph: UCSB-IZC00012194</p> <p>The intended use of this archive is to facilitate (meta-)analysis of the Xylocopa sonorina - UCSB-IZC00012194 - Bee Library - 73e389aa-5886-4c48-8778-ba8932d1bd7e (UCSB-IZC00012194). UCSB-IZC00012194 provides an animated GIF, and wavefront 3D object model, of bee specimen Xylocopa sonorina UCSB-IZC00012194 University of Santa Barbara Invertebrate Zoology Collection as well as the original digital data/image files that were used to find and build this animated GIF and associated 3D model. </p> <p>This dataset provides versioned snapshots of the UCSB-IZC00012194 network as tracked by Preston [2,3] between 2022-09-26 and 2022-09-26 using "preston update -u https://library.big-bee.net/portal/content/dwca/UCSB-IZC_DwC-A.zip". </p> <p>The archive consists of individual files with hexadecimal filenames (e.g., 03d2f9c6912935f54326d3e8c418cab6eddca5f69fb4f299e322cf2d114d0d03) to allow for parallel file downloads. The archive contains three types of files: index files, provenance logs and data files. Index files provide a way to links provenance files in time to establish a versioning mechanism. Provenance files describe how, when, what and where the UCSB-IZC00012194 content was retrieved. For more information, please visit https://preston.guoda.bio or https://doi.org/10.5281/zenodo.1410543 . </p> <p>To retrieve and verify the downloaded UCSB-IZC00012194 biodiversity dataset graph, download all files. Then, extract the archives into a "data" folder. Alternatively, you can use the preston[2] command-line tool to "clone" this dataset using:</p> <p>$ java -jar preston.jar clone --remote https://zenodo.org/record/7114321/files</p> <p>After that, verify the index of the archive by reproducing the following provenance log history:</p> <p>$ java -jar preston.jar history --log tsv<br> urn:uuid:0659a54f-b713-4f86-a917-5be166a14110 http://purl.org/pav/hasVersion hash://sha256/9a5ab7b2278f2dea3fa329e9426dd4712e288b2586616e64986c8f09e76658c6 <br> hash://sha256/af2bc3d2ac9ef865bedc33114e3c12232be58b46ac7033686d1eaed900c34a8d http://purl.org/pav/previousVersion hash://sha256/9a5ab7b2278f2dea3fa329e9426dd4712e288b2586616e64986c8f09e76658c6 <br> hash://sha256/781c17950a96d772c161552a8dff187ab427bcaa830f819758d0fdb8c60cf80e http://purl.org/pav/previousVersion hash://sha256/af2bc3d2ac9ef865bedc33114e3c12232be58b46ac7033686d1eaed900c34a8d <br> hash://sha256/3239876613860452a47603946d3b961580447825b92cc7e566c3c9f4e8bb2b84 http://purl.org/pav/previousVersion hash://sha256/781c17950a96d772c161552a8dff187ab427bcaa830f819758d0fdb8c60cf80e <br> hash://sha256/419548ae006070af3ac9b1bbc90e9e9d51bf36131b23e8e057803cf7086a6842 http://purl.org/pav/previousVersion hash://sha256/3239876613860452a47603946d3b961580447825b92cc7e566c3c9f4e8bb2b84 <br> hash://sha256/c9696c514e404b240d2362f0db545917e357d62c9f6c26b0b7a0b7df6444285e http://purl.org/pav/previousVersion hash://sha256/419548ae006070af3ac9b1bbc90e9e9d51bf36131b23e8e057803cf7086a6842 <br> hash://sha256/7d5a7fa413535390375687ff4cad53568b04761e5108da24400446bc7e8d57bb http://purl.org/pav/previousVersion hash://sha256/c9696c514e404b240d2362f0db545917e357d62c9f6c26b0b7a0b7df6444285e <br> hash://sha256/86aec74994e16ea4bf509141b406546cdc491522705d948eee1f2b4ccefbd4b1 http://purl.org/pav/previousVersion hash://sha256/7d5a7fa413535390375687ff4cad53568b04761e5108da24400446bc7e8d57bb <br> hash://sha256/dcd61980ca9d78669e523fa643c9fa47e255481465384f98253f1b1ac7a5a8d0 http://purl.org/pav/previousVersion hash://sha256/86aec74994e16ea4bf509141b406546cdc491522705d948eee1f2b4ccefbd4b1 <br> hash://sha256/8ecc7754cbab0c1ae169ed868bd9e4e68f7c592bb3609f27b32334c0a6c5e89f http://purl.org/pav/previousVersion hash://sha256/dcd61980ca9d78669e523fa643c9fa47e255481465384f98253f1b1ac7a5a8d0 <br> hash://sha256/d3c2c1ec6697a627607caab51135afa4b8d35c4795c9267f5c24ed3009b77fbe http://purl.org/pav/previousVersion hash://sha256/8ecc7754cbab0c1ae169ed868bd9e4e68f7c592bb3609f27b32334c0a6c5e89f <br> hash://sha256/96bfde1efa599e0e8e61de18b14d61dd308737f684950e4079c04e9bc0f33958 http://purl.org/pav/previousVersion hash://sha256/d3c2c1ec6697a627607caab51135afa4b8d35c4795c9267f5c24ed3009b77fbe </p> <p>To check the integrity of the extracted archive, confirm that each line produce by the command "preston verify" produces lines as shown below, with each line including "CONTENT_PRESENT_VALID_HASH". Depending on hardware capacity, this may take a while.</p> <p>$ java -jar preston.jar verify<br> replace wwith preston verify | head -n4</p> <p>Note that a copy of the java program "preston", preston.jar, is included in this publication. The program runs on java 8+ virtual machine using "java -jar preston.jar", or in short "preston". </p> <p>Files in this data publication:</p> <p>--- start of file descriptions ---</p> <p>-- description of archive and its contents (this file) --<br> README </p> <p>-- executable java jar containing preston [2,3] v0.4.5. --<br> preston.jar</p> <p>-- wavefront 3D object files<br> UCSB-IZC00012194.jpg<br> UCSB-IZC00012194.mtl<br> UCSB-IZC00012194.obj</p> <p>-- animated gifs<br> bee.gif<br> UCSB-IZC00012194.gif</p> <p>-- QR code<br> label.png</p> <p>-- preston archives containing UCSB-IZC00012194 data files, associated provenance logs and a provenance index --<br> 03d2f9c6912935f54326d3e8c418cab6eddca5f69fb4f299e322cf2d114d0d03<br> 064bc7772b2284c42917b785706ae72c196e9443dd394069ceee0f9cd8237e93<br> 093cbfe0e642bcea957785c6593db364ffe5254433bec3b3d0bb942764c47033<br> 0a921d571873916c6c806682e2ab8ead98213deb849f8fe72b34f7902174e655<br> 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Toward Reliable Biodiversity Dataset References. Ecological Informatics. https://doi.org/10.1016/j.ecoinf.2020.101132</p> <p>This project made possible by National Science Foundation Awards: 1839201, 2102006, 2101929, 2101908, 2101876, 2101875, 2101851, 2101345, 2101913, 2101891 and 2101850.</p>
MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees
<p>We present a one-year-long <strong>M</strong>ulti-<strong>S</strong>ensor dataset with <strong>P</strong>henotypic trait measurements from honey <strong>B</strong>ees (MSPB). Data were continuously collected between April-2020 and April-2021 from 53 hives located at two apiaries in Québec, Canada. The sensor data included audio features, temperature, and relative humidity. The phenotypic measurements contained beehive population, number of brood cells (eggs, larva and pupa), <em>Varroa</em> destructor infestation levels, defensive and hygienic behaviors, honey yield, and winter mortality. Our study is amongst the first to provide a wide variety of phenotypic trait measurements annotated by apicultural science experts, which facilitate a broader scope of analysis on honey bees, such as bee acoustics analysis, multi-modal hive monitoring, queen presence detection, <em>Varroa </em>infection detection, hive population estimation, biological analysis of bees, etc.</p> <h3>Related Info</h3> <p>The data collection process, feature pre-processing, preliminary data analysis, and usage notes can be found in our paper <a href="https://arxiv.org/abs/2311.10876">https://arxiv.org/abs/2311.10876</a></p> <p>Check the project webpage (<a href="https://zhu00121.github.io/MSPB-webpage/">https://zhu00121.github.io/MSPB-webpage/</a>) and Github repo (<a href="https://github.com/MuSAELab/MSPB">https://github.com/MuSAELab/MSPB</a>) for more information.</p> <h3>Citation</h3> <p>Kindly cite the following paper:</p> <p>@misc{zhu2023mspb,</p> <p> title={MSPB: a longitudinal multi-sensor dataset with phenotypic trait measurements from honey bees}, </p> <p> author={Yi Zhu and Mahsa Abdollahi and Ségolène Maucourt and Nico Coallier and Heitor R. Guimarães and Pierre Giovenazzo and Tiago H. Falk},</p> <p> year={2023},</p> <p> eprint={2311.10876},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={eess.AS}</p> <p>}</p> <h3>Contact</h3> <p>You can contact us at Yi.Zhu@inrs.ca, if you encounter any questions accessing the data.</p>
Data from: Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones
<p>This vcf file contains 7.023.689 SNPs and 870 honey bee samples, as described in the paper "Complex population structure and haplotype patterns in Western Europe honey bee from sequencing a large panel of haploid drones" by Wragg et al., available at https://doi.org/10.1101/2021.09.20.460798 as preprint.</p> <p>Eight hundred and seventy haploid drone samples from several honey bee subspecies hybrids were sequenced and aligned to the HAv3.1 reference genome. Sequence read alignment and genotyping quality filters were used to obtain a selection of 7.023.689 high-quality SNPs. The file Diversity_Study_629_Samples.txt corresponds to the 629 unique samples that were used for the diversity study described in the paper and can be used to recreate the restricted diversity dataset using bcftools or an equivalent software.</p> <p>Having sequenced haploid drones, heterozygous SNPs resulting from duplicated regions could be filtered out and the data is phased.</p>
Bee Interaction Data from Global Biotic Interactions
<p>New versions of this dataset are found at: <a href="https://doi.org/10.5281/zenodo.16689326">https://doi.org/10.5281/zenodo.16689326</a></p> <p> </p> <p>This repository includes the following:</p> <ol> <li><strong>interactions-GloBI-September-14-2021.tsv.gz</strong>: a full version of the Global Biotic Interactions downloaded on September 14, 2021. No data transformations have occurred on this dataset after the download</li> <li><strong>globi_bee_data.sh</strong>: Shell script for extracting bee records using bee family names from the full version of Global Biotic Interactions</li> <li><strong>all_bee_data_unique.txt</strong>: a file that includes only bee interactions, based on extracting bee names from interactions-GloBI-September-14-2021.tsv.gz</li> </ol> <p>Global Biotic Interactions (GloBI - https://globalbioticinteractions.org) aims to simplify access to existing records of species interactions, such as predator-prey, plant-pollinator, and virus-host interactions. To achieve this, GloBI follows a process where existing, versioned datasets on species interactions are transformed into various aggregate formats, including tsv, csv, neo4j, rdf/nquad, and darwin core-ish archives, with applied name maps included for explicit taxonomic linking.</p> <p>GloBI owes its success to researchers, collections, projects, and institutions that openly share their datasets. Whenever you use this data, please credit the original data contributors, including citing the specific datasets used in derivative work. Each species interaction record in GloBI is linked to a reference and dataset citation. If you have any suggestions on how to make it easier to cite original datasets, you are welcome to join a discussion on https://globalbioticinteractions.org or related projects.</p> <p><strong>Introduction to Global Bee Interaction Data</strong></p> <p>The dataset available here includes all bee interactions recorded in the <a href="https://www.globalbioticinteractions.org/">Global Biotic Interactions</a> (GloBI; Poelen et al. 2014) index as of September 21, 2021. These interactions are gathered quarterly by the <a href="http://big-bee.net/">Big Bee Project </a>(Seltmann et al. 2021) from various sources, including natural history collections, community science observations (such as iNaturalist), and scientific literature. The dataset covers a wide range of bee interactions, including flower visitation, parasitic interactions (such as mite and viral interactions), and lecty, among others. The dataset is filtered for unique records based on interaction description and source citation to ensure accuracy and consistency. For other versions of the bee interaction dataset, please refer to <a href="https://zenodo.org/record/7315159">Seltmann, 2022</a>.</p> <p><strong>Data Description</strong><br>Please see the <a href="https://www.globalbioticinteractions.org/process">integration process page</a> to better understand how Global Biotic Interactions combines datasets from various sources. The complete interaction dataset for all species can be accessed via <a href="https://www.globalbioticinteractions.org/data">https://www.globalbioticinteractions.org/data</a> and the <a href="https://doi.org/10.5281/zenodo.3950589">GloBI Community Zenodo publication</a>.</p> <p><strong>Dataset column names</strong> definitions <a href="https://api.globalbioticinteractions.org/interactionFields">https://api.globalbioticinteractions.org/interactionFields</a> or <a href="https://api.globalbioticinteractions.org/interactionFields">https://api.globalbioticinteractions.org/interactionFields</a></p> <p><strong>References</strong></p> <p>Jorrit H. Poelen, James D. Simons and Chris J. Mungall. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a></p> <p>Katja C. Seltmann. (2022). Global Bee Interaction Data (v2.02) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7315159">https://doi.org/10.5281/zenodo.7315159</a></p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p>
Data used in Machine learning reveals the waggle drift's role in the honey bee dance communication system
<p><strong>Data and metadata used in "Machine learning reveals the waggle drift’s role in the honey bee dance communication system" </strong></p> <p>All timestamps are given in ISO 8601 format.</p> <p><strong>The following files are included:</strong></p> <p><strong>Berlin2019_waggle_phases.csv, Berlin2021_waggle_phases.csv</strong></p> <p>Automatic individual detections of waggle phases during our recording periods in 2019 and 2021.</p> <ul> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>x_median, y_median: Median position of the bee during the waggle phase (for 2019 given in millimeters after applying a homography, for 2021 in the original image coordinates).</p> </li> <li> <p>waggle_angle: Body orientation of the bee during the waggle phase in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_dances.csv</strong></p> <p>Automatic detections of dance behavior during our recording period in 2019.</p> <ul> <li> <p>dancer_id: Unique ID of the individual bee.</p> </li> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the dance.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>median_x, median_y: Median position of the individual during the dance.</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> </ul> <p><strong>Berlin2019_followers.csv</strong></p> <p>Automatic detections of attendance and following behavior, corresponding to the dances in Berlin2019_dances.csv.</p> <ul> <li> <p>dance_id: Unique ID of the dance being attended or followed.</p> </li> <li> <p>follower_id: Unique ID of the individual attending or following the dance.</p> </li> <li> <p>ts_from, ts_to: Date and time of the beginning and end of the interaction.</p> </li> <li> <p>label: “attendance” or “follower”</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> </ul> <p><strong>Berlin2019_dances_with_manually_verified_times.csv</strong></p> <p>A sample of dances from Berlin2019_dances.csv where the exact timestamps have been manually verified to correspond to the beginning of the first and last waggle phase down to a precision of ca. 166 ms (video material was recorded at 6 FPS).</p> <ul> <li> <p>dance_id: Unique ID of the dance.</p> </li> <li> <p>dancer_id: Unique ID of the dancing individual.</p> </li> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>feeder_cam_id: ID of the feeder that the bee was detected at prior to the dance.</p> </li> <li> <p>dance_start, dance_end: Manually verified date and times of the beginning and end of the dance.</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_labels.csv</strong></p> <p>Manually annotated waggle phases or following behavior for our recording season in 2019 that was used to train the dancing and following classifier. Can be merged with the supplied individual detections.</p> <ul> <li> <p>timestamp: Timestamp of the individual frame the behavior was observed in.</p> </li> <li> <p>frame_id: Unique ID of the video frame the behavior was observed in.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>label: One of “nothing”, “waggle”, “follower”</p> </li> </ul> <p><strong>Berlin2019_dance_classifier_unlabeled.csv</strong></p> <p>Additional unlabeled samples of timestamp and individual ID with the same format as Berlin2019_dance_classifier_labels.csv, but without a label. The data points have been sampled close to detections of our waggle phase classifier, so behaviors related to the waggle dance are likely overrepresented in that sample.</p> <p><strong>Berlin2021_waggle_phase_classifier_labels.csv</strong></p> <p>Manually annotated detections of our waggle phase detector (bb_wdd2) that were used to train the neural network filter (bb_wdd_filter) for the 2021 data.</p> <ul> <li> <p>detection_id: Unique ID of the waggle phase.</p> </li> <li> <p>label: One of “waggle”, “activating”, “ventilating”, “trembling”, “other”. Where “waggle” denoted a waggle phase, “activating” is the shaking signal, “ventilating” is a bee fanning her wings. “trembling” denotes a tremble dance, but the distinction from the “other” class was often not clear, so “trembling” was merged into “other” for training.</p> </li> <li> <p>orientation: The body orientation of the bee that triggered the detection in radians (0: facing to the right, PI /4: facing up).</p> </li> <li> <p>metadata_path: Path to the individual detection in the same directory structure as created by the waggle dance detector.</p> </li> </ul> <p><strong>Berlin2021_waggle_phase_classifier_ground_truth.zip</strong></p> <p>The output of the waggle dance detector (bb_wdd2) that corresponds to Berlin2021_waggle_phase_classifier_labels.csv and is used for training. The archive includes a directory structure as output by the bb_wdd2 and each directory includes the original image sequence that triggered the detection in an archive and the corresponding metadata. The training code supplied in bb_wdd_filter directly works with this directory structure.</p> <p><strong>Berlin2019_tracks.zip</strong></p> <p>Detections and tracks from the recording season in 2019 as produced by our tracking system. As the full data is several terabytes in size, we include the subset of our data here that is relevant for our publication which comprises over 46 million detections. We included tracks for all detected behaviors (dancing, following, attending) including one minute before and after the behavior. We also included all tracks that correspond to the labeled and unlabeled data that was used to train the dance classifier including 30 seconds before and after the data used for training.<br> We grouped the exported data by date to make the handling easier, but to efficiently work with the data, we recommend importing it into an indexable database.</p> <p>The individual files contain the following columns:</p> <ul> <li> <p>cam_id: Camera ID (0: left side of the hive, 1: right side of the hive).</p> </li> <li> <p>timestamp: Date and time of the detection.</p> </li> <li> <p>frame_id: Unique ID of the video frame of the recording from which the detection was extracted.</p> </li> <li> <p>track_id: Unique ID of an individual track (short motion path from one individual). For longer tracks, the detections can be linked based on the bee_id.</p> </li> <li> <p>bee_id: Unique ID of the individual bee.</p> </li> <li> <p>bee_id_confidence: Confidence between 0 and 1 that the bee_id is correct as output by our tracking system.</p> </li> <li> <p>x_pos_hive, y_pos_hive: Spatial position of the bee in the hive on the side indicated by cam_id. Given in millimeters after applying a homography on the video material.</p> </li> <li> <p>orientation_hive: Orientation of the bees’ thorax in the hive in radians (0: oriented to the right, PI / 4: oriented upwards).</p> </li> </ul> <p><strong>Berlin2019_feeder_experiment_log.csv</strong></p> <p>Experiment log for our feeder experiments in 2019.</p> <ul> <li> <p>date: Date given in the format year-month-day.</p> </li> <li> <p>feeder_cam_id: Numeric ID of the feeder.</p> </li> <li> <p>coordinates: Longitude and latitude of the feeder. For feeders 1 and 2 this is only given once and held constant. Feeder 3 had varying locations.</p> </li> <li> <p>time_opened, time_closed: Date and time when the feeder was set up or closed again.<br> sucrose_solution: Concentration of the sucrose solution given as sugar:water (in terms of weight). On days where feeder 3 was open, the other two feeders offered water without sugar.</p> </li> </ul> <p> </p> <ul> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline">bb_pipeline: Tag localization and decoding pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_pipeline_models">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_binary">bb_binary: Raw detection data storage format</a></p> </li> <li> <p><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_imgacquisition">bb_imgacquisition: Recording and network storage </a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_behavior">bb_behavior: Database interaction and data (pre)processing, feature extraction</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_tracking">bb_tracking: Tracking of bee detections over time</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd2">bb_wdd2: Automatic detection and decoding of honey bee waggle dances</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_wdd_filter/">bb_wdd_filter: Machine learning model to improve the accuracy of the waggle dance detector</a></p> </li> <li> <p><a href="https://github.com/BioroboticsLab/bb_dance_networks/tree/master/bb_dance_networks">bb_dance_networks: Detection of dancing and following behavior from trajectories</a></p> </li> </ul> <p> </p>
Big Bee indexed biotic interactions and review summary
<p><strong>Extending Anthophila research through image and trait digitization (Big-Bee) indexed biotic interactions and review summary.</strong></p> <p>Declining populations of bees impact plant-pollinator interactions in both natural and agricultural systems. While bees and other insects pollinate most wild plants and are critical to sustaining a large proportion of global food production, they are decreasing in both numbers and diversity. Our understanding of the factors driving these declines is limited because we lack sufficient data on the distribution of bee species, and on the behavioral and anatomical traits that may make them either vulnerable or resilient to human-induced environmental changes, such as habitat loss and climate change. Fortunately, wild bees have been collected by researchers and deposited in natural history collections for over 100 years, retaining a wealth of associated attributes that can be extracted from specimen images. This project will digitally capture data and images from these historic specimens, develop tools to measure bee traits from these images and generate a comprehensive bee trait and image dataset to measure changes through time. This will increase our understanding of specific traits that put bee species at risk of decline - a critical need for both sustaining our agricultural economy and the conservation of our natural resources. In addition, the large image datasets created by this project can be used for new artificial intelligence identification tools that will help improve our future pollinator observation and monitoring efforts.</p> <p>The Big-Bee project began in 2021 and is funded by the National Science Foundation to mobilize data about worldwide bee species to data aggregators (e.g., iDigBio, GBIF). The Big-Bee Thematic Collection Network (Big-Bee) will create over one million high-resolution 2D and 3D images of bee specimens, representing over 5,000 worldwide bee species, including all of the major pollinating species of the United States. The Big-Bee network includes 13 institutions and partnerships with US government agencies. Novel mechanisms for sharing image datasets will be developed and datasets of bee traits will be available through an open data portal, the Bee Library, for research and education. The Big-Bee project will engage the general public in research through community science via crowdsourcing trait measurements and data transcription from images. In addition, training and professional development for natural history collection staff, researchers, and university students in data science will be provided through the creation and implementation of workshops focusing on bee traits and species identification. All data resulting from this award will be shared with and publicly available through the national digitized biocollections resource, iDigBio.org.</p> <p>This is the first archive of Big-Bee data indexed by Global Biotic Interactions (GloBI). GloBI provides open access to finding species interaction data (e.g., predator-prey, pollinator-plant, pathogen-host, parasite-host) by combining existing open datasets using open-source software. This version of the Big Bee dataset includes interactions that are not just bees. Also in this version, the datasets included in this publication are specifically those institutions in the Big Bee project network and do not represent all bee interaction data found at Global Biotic Interactions.</p> <p><strong>Bee Library Information - Statistics about Big Bee data providers</strong></p> <p>The specimens indexed by GloBI are also found in the <a href="https://library.big-bee.net/portal/">Bee Library</a>. To date, the number of specimens and images in the library are listed below. The Bee Library taxonomic backbone is not yet complete, so information regarding the number of species is not yet available. Further summary statistics are available in the Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf file.</p> <p><strong>From Bee Library (partner indexed records)</strong><br> 1,234,107 occurrence records<br> 993,692 (81%) georeferenced<br> 351,592 (28%) occurrences imaged<br> 986,323 (80%) identified to species<br> 9 families<br> 526 genera<br> 10,700 species<br> 11,386 total taxa (including subsp. and var.)</p> <p><strong>Statistics Per Collection</strong></p> <table> <tbody> <tr> <td>Collection</td> <td>Occurrences</td> <td>Georeferenced</td> <td>Imaged</td> <td>Interactions Indexed in GloBI (all)</td> <td>Interactions Indexed in GloBI (bees)</td> </tr> <tr> <td>ASU Hasbrouck Insect Collection - Bee<br> Records</td> <td>13223</td> <td>13221</td> <td>2352</td> <td>21300</td> <td>3834</td> </tr> <tr> <td>Bee Biology and Systematics Laboratory,<br> USDA-ARS Pollinating Insect-Biology,<br> Management, Systematics Research</td> <td>561820</td> <td>547461</td> <td>0</td> <td>0</td> <td>0</td> </tr> <tr> <td>California Academy of Sciences</td> <td>884</td> <td>300</td> <td>3</td> <td>16984</td> <td>117</td> </tr> <tr> <td>California Academy of Sciences - Type<br> Collection</td> <td>1838</td> <td>59</td> <td>83</td> <td>0</td> <td>0</td> </tr> <tr> <td>Essig Museum of Entomology, University<br> of California Berkeley</td> <td>58551</td> <td>55028</td> <td>0</td> <td> </td> <td>0</td> </tr> <tr> <td>Florida State Collection of Arthropods</td> <td>17134</td> <td>12349</td> <td>7816</td> <td>559</td> <td> </td> </tr> <tr> <td>Museum of Comparative Zoology, Harvard<br> University</td> <td>22020</td> <td>21099</td> <td>11595</td> <td>6777</td> <td>1535</td> </tr> <tr> <td>Natural History Museum of Los Angeles<br> County</td> <td>24685</td> <td>7421</td> <td>3480</td> <td>0</td> <td>0</td> </tr> <tr> <td>San Diego Natural History Museum<br> Entomology Department</td> <td>4065</td> <td>1690</td> <td>1982</td> <td>8688</td> <td>90</td> </tr> <tr> <td>University of California Santa Barbara<br> Invertebrate Zoology Collection</td> <td>8674</td> <td>8410</td> <td>2751</td> <td>1940</td> <td>660</td> </tr> <tr> <td>University of Colorado Museum of Natural<br> History, Entomology Collection</td> <td>18043</td> <td>18043</td> <td>0</td> <td>9589</td> <td>4723</td> </tr> <tr> <td>University of Kansas Natural History<br> Museum Entomology Division</td> <td>464927</td> <td>275200</td> <td>304415</td> <td>119963</td> <td>112677</td> </tr> <tr> <td>University of Michigan Museum of Zoology<br> Division of Insects</td> <td>17764</td> <td>15305</td> <td>15269</td> <td>53755</td> <td>4134</td> </tr> <tr> <td>University of New Hampshire, Donald S.<br> Chandler Entomological Collection</td> <td>17685</td> <td>17393</td> <td>0</td> <td>3137</td> <td>3137</td> </tr> <tr> <td>USGS Native Bee Inventory and Monitoring<br> Lab</td> <td>101</td> <td>101</td> <td>0</td> <td>0</td> <td>0</td> </tr> </tbody> </table> <p><strong>GloBI Data Review Report - Datasets in Review from Global Biotic Interactions</strong></p> <p>Datasets under review:<br> - UUniversity of Michigan Museum of Zoology, Division of Insects accessed via https://github.com/globalbioticinteractions/ummz-ummzi/archive/d9282e51f29f3157af2e5869a09ea8a111ddea34.zip on 2023-07-24T22:06:08.671Z<br> - Arizona State University Hasbrouck Insect Collection accessed via https://github.com/globalbioticinteractions/asu-asuhic/archive/4ed77cb9ca8e526269d4678692e2844c950022f8.zip on 2023-07-24T22:07:09.630Z<br> - California Academy of Sciences Entomology and Entomology Type Collection accessed via https://github.com/globalbioticinteractions/cas-ent/archive/47d385b73a63aa379cd5e6d3615005ba78b0ffc1.zip on 2023-07-24T22:08:13.753Z<br> - University of California Berkeley, Essig Museum of Entomology accessed via https://github.com/globalbioticinteractions/emec/archive/93b17a3db566baa001ce9190e6fbdb60fa99dda4.zip on 2023-07-24T22:08:24.495Z<br> - Florida State Collection of Arthropods accessed via https://github.com/globalbioticinteractions/fsca/archive/2cdcf9475b7e0ef2a728a96535608bc0ce2ac5ca.zip on 2023-07-24T22:08:49.972Z<br> - University of Kansas Natural History Museum accessed via https://github.com/globalbioticinteractions/ku-semc/archive/a9c7cb81050eef68b4428667206a219da458f517.zip on 2023-07-24T22:09:17.016Z<br> - Natural History Museum of Los Angeles County accessed via https://github.com/globalbioticinteractions/lacm-lacmec/archive/dafbf532c53fbadba126c81186c26d52677aa781.zip on 2023-07-24T22:11:11.442Z<br> - Harvard University M, Morris P J (2021). Museum of Comparative Zoology, Harvard University. Museum of Comparative Zoology, Harvard University. accessed via https://github.com/globalbioticinteractions/mcz/archive/b33635a9fc75fd7931ad968cbc11180e6467bfd7.zip on 2023-07-24T22:21:32.961Z<br> - San Diego Natural History Museum accessed via https://github.com/globalbioticinteractions/sdnhm-sdmc/archive/7238d8b804f543250eb487b43144e1125fb3688a.zip on 2023-07-24T22:26:25.503Z<br> - University of Colorado Museum of Natural History Entomology Collection accessed via https://github.com/globalbioticinteractions/ucm-ucmc/archive/60530dcc82d33c9675a4026ad60dc40bea8f2a91.zip on 2023-07-24T22:26:50.178Z<br> - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/66a4e39589d1dfa299d07985546c4be522ff60d8.zip on 2023-07-24T22:27:13.801Z<br> - University of New Hampshire Donald S. Chandler Entomological Collection accessed via https://github.com/globalbioticinteractions/unhc-unhc/archive/d7668a6bb4545dc4da0645ecc383169ba547b0f5.zip on 2023-07-24T22:27:28.670Z</p> <p>Generated on:<br> 2023-07-24</p> <p>by:<br> GloBI's Elton 0.12.6 <br> (see https://github.com/globalbioticinteractions/elton).</p> <p>Note that all files ending with .tsv are files formatted <br> as UTF8 encoded tab-separated values files.</p> <p>https://www.iana.org/assignments/media-types/text/tab-separated-values</p> <p><br> Included in this review archive are:</p> <p>README:<br> This file.</p> <p>review_summary.tsv:<br> Summary across all reviewed collections of total number of distinct review comments.</p> <p>review_summary_by_collection.tsv:<br> Summary by reviewed collection of total number of distinct review comments.</p> <p>indexed_interactions_by_collection.tsv: <br> Summary of number of indexed interaction records by institutionCode and collectionCode.</p> <p>review_comments.tsv.gz:<br> All review comments by collection.</p> <p>indexed_interactions_full.tsv.gz:<br> All indexed interactions for all reviewed collections.</p> <p>indexed_interactions_simple.tsv.gz:<br> All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.</p> <p>datasets_under_review.tsv:<br> Details on the datasets under review.</p> <p>elton.jar: <br> Program used to update datasets and generate the review reports and associated indexed interactions.</p> <p>indexed_interactions_bees.tsv:<br> All indexed bee interactions <br> </p> <p>datasets.zip:<br> All datasets reviewed for this publication</p> <p> Big Bee Metrics from the Bee Library and GloBI - July 24, 2023.pdf:<br> Summary statistics from the Bee Library and GloBI about data partners</p> <p>If you have questions or comments about this publication, please open an issue at https://github.com/Big-Bee-Network/issues-observations-and-questions/discussions or contact the authors by email.</p> <p><strong>Funding:</strong><br> The creation of this archive was made possible by the National Science Foundation award Collaborative Research: Digitization TCN: Extending Anthophila research through image and trait digitization (Big-Bee). Award numbers: <a href="https://www.nsf.gov/awardsearch/showAward?AWD_ID=2102006">DBI:2102006</a>, DBI:2101929, DBI:2101908, DBI:2101876, DBI:2101875, DBI:2101851, DBI:2101345, DBI:2101913, DBI:2101891 and DBI:2101850.</p> <p>References:<br> Poelen JH, Simons JD and Mungall CH. (2014). Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics. <a href="https://doi.org/10.1016/j.ecoinf.2014.08.005">https://doi.org/10.1016/j.ecoinf.2014.08.005</a>.</p> <p>Seltmann KC, Allen J, Brown BV, Carper A, Engel MS, Franz N, Gilbert E, Grinter C, Gonzalez VH, Horsley P, Lee S, Maier C, Miko I, Morris P, Oboyski P, Pierce NE, Poelen J, Scott VL, Smith M, Talamas EJ, Tsutsui ND, Tucker E (2021) Announcing Big-Bee: An initiative to promote understanding of bees through image and trait digitization. Biodiversity Information Science and Standards 5: e74037. <a href="https://doi.org/10.3897/biss.5.74037">https://doi.org/10.3897/biss.5.74037</a></p> <p>Jorrit Poelen, Tobias Kuhn, & Katrin Leinweber. (2022). globalbioticinteractions/elton: 0.12.5 (0.12.5). Zenodo. https://doi.org/10.5281/zenodo.7267926</p>
Soil biota counts from the Biotic Effects Experiment (BEE), McMurdo Dry Valleys, Antarctica (1999-2024, ongoing)
Increases in soil temperature and moisture may change the bioavailability of essential elements by altering solubility and diffusion rates in soils, or by changing the amounts of organic compounds. Long-term experiments in the Bonney, Hoare and Fryxell basins have been established with three treatments: 1) increased moisture, 2) soil warming (ITEX chambers), and 3) soil warming + increased moisture. The identification and abundance of soil biota are reported. Only control treatments have been measured since the 2014-2015 austral summer.
Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology
<p>What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators.</p> <p>Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories.</p> <p>Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography.</p> <p><strong>The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting. </strong></p>
Social networks predict the life and death of honey bees - Data
<p><strong>Interaction matrices and metadata used in "Social networks predict the life and death of honey bees"</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2020.05.06.076943v2">Preprint: Social networks predict the life and death of honey bees</a></p> <p>See the README file in <a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition</a> for example code.</p> <p><strong>The following files are included:</strong></p> <p><strong>interaction_networks_20160729to20160827.h5</strong></p> <p>The social interaction networks as a dense tensor and metadata.</p> <p>Keys:</p> <ul> <li>interactions: Tensor of shape (29, 2010, 2010, 9) (days x individuals x individuals x interaction_types). I_{d,i,j,t} = log(1 + x), where x is the number of interactions of type t between individuals i and j at recording day d. See the methods section of paper of the interaction types.</li> <li>labels: Names of the 9 interaction types in the order they are stored in the interactions tensor.</li> <li>bee_ids: List of length 2010, mapping from sequential index used in the interaction tensor to the original BeesBook tag ID of the individual</li> </ul> <p><strong>alive_bees_bayesian.csv </strong></p> <p>This file contains the results of the bayesian lifetime model with one row for each bee.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>days_alive: Number of bees the bees was determined to be alive. If the individual was still alive at the end of the recording, the number of days from the day she hatched until the end of the recording.</li> <li>death_observed: Boolean indicator whether the death occurred during the recording period.</li> <li>annotated_tagged_date: Hatch date of the individual, i.e. the date she was tagged.</li> <li>inferred_death_date: The death date as determined by the model.</li> </ul> <p><strong>bee_daily_data.csv</strong></p> <p>This file contains one row per bee per day that she was alive for the focal period.</p> <p>Columns:</p> <ul> <li>bee_id: Numerical unique identifier for each individual.</li> <li>date: Date in year-month-day format.</li> <li>age: Age in days. Can be NaN if the bee has no associated death_date.</li> <li>network_age, network_age_1, network_age_2: The first three dimensions of network age.</li> <li>dance_floor, honey_storage, near_exit, brood_area_total: Normalized (sum to 1). Can be NaN if a bee had no high confidence detections (>0.9) for a given day. Can be 0 if a bee was only seen outside of the annotated areas.</li> <li>location_descriptor_count: The number of minutes the bee was seen in one of the location labels during that day. I.e., dance_floor * location_descriptor_count calculates the number of minutes, the bee was seen on the dance floor on the given day.</li> <li>death_date: Date the bee was last seen in the colony in year-month-day format. Can be NaN for individuals that did not die until the end of the recording period.</li> <li>circadian_rhythm: R² value of a sine with a period of one day fitted to the velocity data of the individual over three days. Can be NaN if the fit did not converge due to a lack of data points.</li> <li>velocity_peak_time: Phase of the circadian sine fit in hours as an offset to 12:00 UTC. Can be NaN if circadian_rhythm is NaN.</li> <li>velocity_day, velocity_night: Mean velocity of the individual between 09:00-18:00 UTC and 21:00-06:00 UTC, respectively. Can be NaN if no velocity data was available for that interval.</li> <li>days_left: Difference in days between date and death_date. Can be NaN if death_date is NaN.</li> </ul> <p><strong>location_data.csv</strong></p> <p>This file contains subsampled position information for all bees during the focal period. The data contains one row for every individual for every minute of the recording if that individual was seen at least once during that minute with a tag confidence of at least 0.9. The first matching detection for each individual is used.</p> <p>Columns:</p> <p>In addition to the bee_id and date columns as in the bee_daily_data.csv, the file contains these additional columns:</p> <ul> <li>cam_id, cams: The cam_id is a numerical identifier from {0, 1, 2, 3}. Each side of the hive is filmed by two cameras where {0, 1} and {2, 3} record the same side respectively. The cams column contains values either “(0, 1)” or “(2, 3)” and indicates to which sides of the hive this detection belongs.</li> <li>x_pos_hive, y_pos_hive: The spatial positions in millimeters on the hive. The two cameras from one side share a common coordinate system.</li> <li>location: The label that was assigned to the comb at (x_pos_hive, y_pos_hive) on the given date. The label “other” indicates detections that were outside of any annotated region. The label “not_comb” indicates the wooden frame or empty space around the comb.</li> <li>timestamp, date: The timestamp indicates the beginning of each one-minute sampling interval and is given in UTC, as indicated (example: “2016-08-13 00:00:00+00:00”). The date part of the timestamp is repeated in the “date” column. Both are given in year-month-day format.</li> </ul> <p><strong>Software used to acquire and analyze the data:</strong></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.4435058">bb_network_decomposition: Network age calculation and regression analyses</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline/releases/tag/2016">bb_pipeline: Tag localization and decoding pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_pipeline_models/releases/tag/2016">bb_pipeline_models: Pretrained localizer and decoder models for bb_pipeline</a></li> <li><a href="https://github.com/BioroboticsLab/bb_binary/releases/tag/2016">bb_binary: Raw detection data storage format</a></li> <li><a href="https://doi.org/10.5281/zenodo.4436419">bb_irflash: IR flash system schematics and arduino code</a></li> <li><a href="https://github.com/BioroboticsLab/bb_imgacquisition/releases/tag/2016">bb_imgacquisition: Recording and network storage </a></li> <li><a href="https://github.com/BioroboticsLab/bb_behavior/releases/tag/2016">bb_behavior: Database interaction and data (pre)processing, velocity calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_circadian/releases/tag/2016">bb_circadian: Circadian rhythm calculations</a></li> <li><a href="https://github.com/BioroboticsLab/bb_tracking_2016/releases/tag/2016">bb_tracking: Tracking of bee detections over time</a></li> <li><a href="https://github.com/BioroboticsLab/bb_wdd/releases/tag/2016">bb_wdd: Automatic detection and decoding of honey bee waggle dances</a></li> <li><a href="https://github.com/BioroboticsLab/bb_interval_determination/releases/tag/2016">bb_interval_determination: Homography calculation</a></li> <li><a href="https://github.com/BioroboticsLab/bb_stitcher/releases/tag/2016">bb_stitcher: Image stitching</a></li> </ul> <p> </p>
iNaturalist Wild Bees
<p>The dataset contains <strong>726</strong> images of wild bees scraped from the <a href="http://inaturalist.org/">iNaturalist</a> database. There are <strong>25 species</strong> with 30 images each (except for <em>Bombus magnus</em>, that only has 6 images). All photos were marked with <em>Research Grade</em> on iNaturalist and were released under the <em>CC-BY-NC</em> license. Along with the images, a json file is also provided; there you can find the <strong>part annotations for Head, Thorax and Abdomen</strong> for all the photos. The segmentations were drawn in <a href="https://labelstud.io/">Label Studio</a>. For more information on the data, please visit our <a href="https://github.com/TeodorChiaburu/beexplainable">GitHub repository</a> .</p>
Intraspecific variation in the sensitivity of bees to pesticides: a comparative analysis in Bombus terrestris and Osmia bicornis
<p>These files describe the archived CSV files associated with the publication "Intra-specific variation in sensitivity of Bombus terrestris and Osmia bicornis to three pesticides"</p> <p>By Alberto Linguadoca, Margret Jürison, Sara Hellström, Edward A. Straw1, Peter Šima, Reet Karise, Cecilia Costa, Giorgia Serra, Roberto Colombo, Robert J. Paxton, Marika Mänd, Mark J. F. Brown<br> </p>
Occasional and constant exposure to dietary ethanol shortens the lifespan of worker honey bees
<p><span>Honey bees (<em>Apis mellifera</em>) are one of the most crucial pollinators, providing vital ecosystem services. Their development and functioning depend on essential nutrients and substances found in the environment. While collecting nectar as a vital carbohydrate source, bees routinely encounter low doses of ethanol from yeast fermentation. Yet, the effects of repeated ethanol exposure on bees' survival and physiology remain poorly understood. Here, we investigate the impacts of constant and occasional consumption of food spiked with 1% ethanol on honey bee mortality and alcohol dehydrogenase (ADH) activity. This ethanol concentration might be tentatively judged close to that in natural conditions. We conducted an experiment in which bees were exposed to three types of long-term diets: constant sugar solution (control group that simulated conditions of no access to ethanol), sugar solution spiked with ethanol every third day (that simulated occasional, infrequent exposure to ethanol) and daily ethanol consumption (simulating constant, routine exposure to ethanol). The results revealed that both constant and occasional ethanol consumption increased the mortality of bees, but only after several days. These mortality rates rose with the frequency of ethanol intake. The ADH activity remained similar in bees from all groups. Our findings indicate that exposure of bees to ethanol carries harmful effects that accumulate over time. Further research is needed to pinpoint the exact ethanol doses ingested with food and exposure frequency in bees in natural conditions.</span></p>
Individual-based body sizes of wild bees along elevational gradients on Mt. Kilimanjaro
<p><span>This dataset contains body size measurements of wild bees that were captured along elevational gradients on the southern slopes of Mt. Kilimanjaro (Tanzania) using standardized sampling methods (pan traps, transect walks). The dataset includes bee species identified at the species level as well as morphospecies. The bees were measured individually, meaning that intraspecific differences in body size are also represented. The intertegular distance (ITD) in millimeters was measured as a surrogate for body size.</span></p> <p><span>The KiLi project (2010-2018) is a German Science Foundation (DFG) funded research unit (DFG research unit FOR1246) that focuses on biodiversity and ecosystem processes along altitudinal and disturbance gradients on Mt. Kilimanjaro (Tanzania, Africa), capitalizing on its world-wide unique range of climatic and vegetation zones. The research unit comprises 2 central projects and 7 subprojects from various disciplines. On a total of 60 study sites in both natural and human-disturbed ecosystems biodiversity (e.g. plants, soil arthropods, ants, bees, frogs, lizards, bats, birds), related ecosystem processes (decomposition, seed dispersal, pollination, herbivory, predation), and biogeochemical processes and properties of ecosystems (climate, soil properties and nutrient status, regulation of water and carbon fluxes, trace gas emissions, primary productivity, functional diversity) are analyzed.</span></p>
Bioinformatic pipeline: Genomic diversity landscape of the honey bee gut microbiota
<p>This data-set describes the full bioinformatic pipeline used to analyze 54 metagenomic samples of the honey bee gut microbiota. Each sample was isolated from an individual honey bee, and all samples originate from two colonies of the Engel laboratory at the University of Lausanne, Switzerland. The full raw data-set is available from the sequence-read archive: SRP150166.</p> <p>A publication based on this analysis is currently under review, with the title: "Genomic diversity landscape of the honey bee gut microbiota", and an upload to Biorxiv is also underway.</p> <p>The data-set contains tar-balls for the different main workflows of the analysis. Dowload and unpack to view the contents (tar -zxvf filename.tar.gz). For each workflow, all directories contain README.txt files, describing the contents of the directory. Due to size constraints, some intermediate files have been omitted, and some workflows are demonstrated for a subset of the data. However, the full analysis can be reproduced from the raw data, using the provided scripts.</p> <p>Scripts are included within workflow directories, and are also provided as a separate tar-ball for convenience. All perl-scripts come with documentation, which can be viewed by typing: "perl script_name.pl -h". For R scripts, the usage is indicated as a comment in the top lines of each script. Note that many of the scripts require specific input-files to be present in the run-directory. Their usage is demonstrated within the workflow directories in bash-scripts (*.sh). Commands used for generating plots and some statistics are given within workflow directories in text-files "R.commands" when applicable.</p> <p>Aside from custom code, the pipeline also utilizes various open-source Software packages, which are detailed in the file "software_dependencies.txt". Note, while many of the scripts will run fast on any computer, some steps of the pipeline are computationally demanding, and will require significant computing time, as well as storage space. When scripts are known to be time-consuming, this is indicated in the script help message.</p> <p> </p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.