More Like this

1. Identifying datasets for global wildlife trafficking

   https://doi.org/10.5281/zenodo.7096921  

   Gore, Meredith ; Keskin, Burcu ; Macdonald, John ; Ferber, Aaron ; Griffin, Emily ; Prell, Oakley ; Dilkina, Bistra ; Hilend, Rowan ( January 2022 , Zenodo)

   We describe a novel database on wildlife trafficking that can be used for exploring supply chain coordination via game-theoretic collaboration models, geographic spread of wildlife products trafficked via multi-item knapsack problems, or illicit network interdiction via multi-armed bandit problems.

   A publicly available visualization of this dataset is available at: https://public.tableau.com/views/IWTDataDirectory-Gore/Sheet2?:language=en-US&:display_count=n&:origin=viz_share_link 

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2. Software and Data Resources to Advance Machine Learning Research in Electroencephalography

   https://doi.org/10.1109/SPMB47826.2019.9037851  

   Rahman, S. ; Miranda, M. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, I. ; Selesnick, I. (Ed.)

   The Neural Engineering Data Consortium at Temple University has been providing key data resources to support the development of deep learning technology for electroencephalography (EEG) applications [1-4] since 2012. We currently have over 1,700 subscribers to our resources and have been providing data, software and documentation from our web site [5] since 2012. In this poster, we introduce additions to our resources that have been developed within the past year to facilitate software development and big data machine learning research. Major resources released in 2019 include: ● Data: The most current release of our open source EEG data is v1.2.0 of TUH EEG and includes the addition of 3,874 sessions and 1,960 patients from mid-2015 through 2016. ● Software: We have recently released a package, PyStream, that demonstrates how to correctly read an EDF file and access samples of the signal. This software demonstrates how to properly decode channels based on their labels and how to implement montages. Most existing open source packages to read EDF files do not directly address the problem of channel labels [6]. ● Documentation: We have released two documents that describe our file formats and data representations: (1) electrodes and channels [6]: describes how to map channel labels to physical locations of the electrodes, and includes a description of every channel label appearing in the corpus; (2) annotation standards [7]: describes our annotation file format and how to decode the data structures used to represent the annotations. Additional significant updates to our resources include: ● NEDC TUH EEG Seizure (v1.6.0): This release includes the expansion of the training dataset from 4,597 files to 4,702. Calibration sequences have been manually annotated and added to our existing documentation. Numerous corrections were made to existing annotations based on user feedback. ● IBM TUSZ Pre-Processed Data (v1.0.0): A preprocessed version of the TUH Seizure Detection Corpus using two methods [8], both of which use an FFT sliding window approach (STFT). In the first method, FFT log magnitudes are used. In the second method, the FFT values are normalized across frequency buckets and correlation coefficients are calculated. The eigenvalues are calculated from this correlation matrix. The eigenvalues and correlation matrix's upper triangle are used to generate feature. ● NEDC TUH EEG Artifact Corpus (v1.0.0): This corpus was developed to support modeling of non-seizure signals for problems such as seizure detection. We have been using the data to build better background models. Five artifact events have been labeled: (1) eye movements (EYEM), (2) chewing (CHEW), (3) shivering (SHIV), (4) electrode pop, electrostatic artifacts, and lead artifacts (ELPP), and (5) muscle artifacts (MUSC). The data is cross-referenced to TUH EEG v1.1.0 so you can match patient numbers, sessions, etc. ● NEDC Eval EEG (v1.3.0): In this release of our standardized scoring software, the False Positive Rate (FPR) definition of the Time-Aligned Event Scoring (TAES) metric has been updated [9]. The standard definition is the number of false positives divided by the number of false positives plus the number of true negatives: #FP / (#FP + #TN). We also recently introduced the ability to download our data from an anonymous rsync server. The rsync command [10] effectively synchronizes both a remote directory and a local directory and copies the selected folder from the server to the desktop. It is available as part of most, if not all, Linux and Mac distributions (unfortunately, there is not an acceptable port of this command for Windows). To use the rsync command to download the content from our website, both a username and password are needed. An automated registration process on our website grants both. An example of a typical rsync command to access our data on our website is: rsync -auxv nedc_tuh_eeg@www.isip.piconepress.com:~/data/tuh_eeg/ Rsync is a more robust option for downloading data. We have also experimented with Google Drive and Dropbox, but these types of technology are not suitable for such large amounts of data. All of the resources described in this poster are open source and freely available at https://www.isip.piconepress.com/projects/tuh_eeg/downloads/. We will demonstrate how to access and utilize these resources during the poster presentation and collect community feedback on the most needed additions to enable significant advances in machine learning performance. 

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3. Recent Advances in the Temple University Digital Pathology Corpus

   https://doi.org/10.1109/SPMB47826.2019.9037859  

   Hunt, I. ; Husain, S. ; Simon, J. ; Obeid, I. ; Picone, J. ( December 2019 , IEEE Signal Processing in Medicine and Biology Symposium (SPMB))

   Obeid, Iyad ; Picone, Joseph ; Selesnick, Ivan (Ed.)

   The Neural Engineering Data Consortium (NEDC) is developing a large open source database of high-resolution digital pathology images known as the Temple University Digital Pathology Corpus (TUDP) [1]. Our long-term goal is to release one million images. We expect to release the first 100,000 image corpus by December 2020. The data is being acquired at the Department of Pathology at Temple University Hospital (TUH) using a Leica Biosystems Aperio AT2 scanner [2] and consists entirely of clinical pathology images. More information about the data and the project can be found in Shawki et al. [3]. We currently have a National Science Foundation (NSF) planning grant [4] to explore how best the community can leverage this resource. One goal of this poster presentation is to stimulate community-wide discussions about this project and determine how this valuable resource can best meet the needs of the public. The computing infrastructure required to support this database is extensive [5] and includes two HIPAA-secure computer networks, dual petabyte file servers, and Aperio’s eSlide Manager (eSM) software [6]. We currently have digitized over 50,000 slides from 2,846 patients and 2,942 clinical cases. There is an average of 12.4 slides per patient and 10.5 slides per case with one report per case. The data is organized by tissue type as shown below: Filenames: tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_0a001_00123456_lvl0001_s000.svs tudp/v1.0.0/svs/gastro/000001/00123456/2015_03_05/0s15_12345/0s15_12345_00123456.docx Explanation: tudp: root directory of the corpus v1.0.0: version number of the release svs: the image data type gastro: the type of tissue 000001: six-digit sequence number used to control directory complexity 00123456: 8-digit patient MRN 2015_03_05: the date the specimen was captured 0s15_12345: the clinical case name 0s15_12345_0a001_00123456_lvl0001_s000.svs: the actual image filename consisting of a repeat of the case name, a site code (e.g., 0a001), the type and depth of the cut (e.g., lvl0001) and a token number (e.g., s000) 0s15_12345_00123456.docx: the filename for the corresponding case report We currently recognize fifteen tissue types in the first installment of the corpus. The raw image data is stored in Aperio’s “.svs” format, which is a multi-layered compressed JPEG format [3,7]. Pathology reports containing a summary of how a pathologist interpreted the slide are also provided in a flat text file format. A more complete summary of the demographics of this pilot corpus will be presented at the conference. Another goal of this poster presentation is to share our experiences with the larger community since many of these details have not been adequately documented in scientific publications. There are quite a few obstacles in collecting this data that have slowed down the process and need to be discussed publicly. Our backlog of slides dates back to 1997, meaning there are a lot that need to be sifted through and discarded for peeling or cracking. Additionally, during scanning a slide can get stuck, stalling a scan session for hours, resulting in a significant loss of productivity. Over the past two years, we have accumulated significant experience with how to scan a diverse inventory of slides using the Aperio AT2 high-volume scanner. We have been working closely with the vendor to resolve many problems associated with the use of this scanner for research purposes. This scanning project began in January of 2018 when the scanner was first installed. The scanning process was slow at first since there was a learning curve with how the scanner worked and how to obtain samples from the hospital. From its start date until May of 2019 ~20,000 slides we scanned. In the past 6 months from May to November we have tripled that number and how hold ~60,000 slides in our database. This dramatic increase in productivity was due to additional undergraduate staff members and an emphasis on efficient workflow. The Aperio AT2 scans 400 slides a day, requiring at least eight hours of scan time. The efficiency of these scans can vary greatly. When our team first started, approximately 5% of slides failed the scanning process due to focal point errors. We have been able to reduce that to 1% through a variety of means: (1) best practices regarding daily and monthly recalibrations, (2) tweaking the software such as the tissue finder parameter settings, and (3) experience with how to clean and prep slides so they scan properly. Nevertheless, this is not a completely automated process, making it very difficult to reach our production targets. With a staff of three undergraduate workers spending a total of 30 hours per week, we find it difficult to scan more than 2,000 slides per week using a single scanner (400 slides per night x 5 nights per week). The main limitation in achieving this level of production is the lack of a completely automated scanning process, it takes a couple of hours to sort, clean and load slides. We have streamlined all other aspects of the workflow required to database the scanned slides so that there are no additional bottlenecks. To bridge the gap between hospital operations and research, we are using Aperio’s eSM software. Our goal is to provide pathologists access to high quality digital images of their patients’ slides. eSM is a secure website that holds the images with their metadata labels, patient report, and path to where the image is located on our file server. Although eSM includes significant infrastructure to import slides into the database using barcodes, TUH does not currently support barcode use. Therefore, we manage the data using a mixture of Python scripts and manual import functions available in eSM. The database and associated tools are based on proprietary formats developed by Aperio, making this another important point of community-wide discussion on how best to disseminate such information. Our near-term goal for the TUDP Corpus is to release 100,000 slides by December 2020. We hope to continue data collection over the next decade until we reach one million slides. We are creating two pilot corpora using the first 50,000 slides we have collected. The first corpus consists of 500 slides with a marker stain and another 500 without it. This set was designed to let people debug their basic deep learning processing flow on these high-resolution images. We discuss our preliminary experiments on this corpus and the challenges in processing these high-resolution images using deep learning in [3]. We are able to achieve a mean sensitivity of 99.0% for slides with pen marks, and 98.9% for slides without marks, using a multistage deep learning algorithm. While this dataset was very useful in initial debugging, we are in the midst of creating a new, more challenging pilot corpus using actual tissue samples annotated by experts. The task will be to detect ductal carcinoma (DCIS) or invasive breast cancer tissue. There will be approximately 1,000 images per class in this corpus. Based on the number of features annotated, we can train on a two class problem of DCIS or benign, or increase the difficulty by increasing the classes to include DCIS, benign, stroma, pink tissue, non-neoplastic etc. Those interested in the corpus or in participating in community-wide discussions should join our listserv, nedc_tuh_dpath@googlegroups.com, to be kept informed of the latest developments in this project. You can learn more from our project website: https://www.isip.piconepress.com/projects/nsf_dpath. 

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4. MoveApps: a serverless no-code analysis platform for animal tracking data

   https://doi.org/10.1186/s40462-022-00327-4  

   Kölzsch, Andrea ; Davidson, Sarah C. ; Gauggel, Dominik ; Hahn, Clemens ; Hirt, Julian ; Kays, Roland ; Lang, Ilona ; Lohr, Ashley ; Russell, Benedict ; Scharf, Anne K. ; et al ( December 2022 , Movement Ecology)

   Abstract Background Bio-logging and animal tracking datasets continuously grow in volume and complexity, documenting animal behaviour and ecology in unprecedented extent and detail, but greatly increasing the challenge of extracting knowledge from the data obtained. A large variety of analysis methods are being developed, many of which in effect are inaccessible to potential users, because they remain unpublished, depend on proprietary software or require significant coding skills. Results We developed MoveApps, an open analysis platform for animal tracking data, to make sophisticated analytical tools accessible to a global community of movement ecologists and wildlife managers. As part of the Movebank ecosystem, MoveApps allows users to design and share workflows composed of analysis modules (Apps) that access and analyse tracking data. Users browse Apps, build workflows, customise parameters, execute analyses and access results through an intuitive web-based interface. Apps, coded in R or other programming languages, have been developed by the MoveApps team and can be contributed by anyone developing analysis code. They become available to all user of the platform. To allow long-term and cross-system reproducibility, Apps have public source code and are compiled and run in Docker containers that form the basis of a serverless cloud computing system. To support reproducible science and help contributors document and benefit from their efforts, workflows of Apps can be shared, published and archived with DOIs in the Movebank Data Repository. The platform was beta launched in spring 2021 and currently contains 49 Apps that are used by 316 registered users. We illustrate its use through two workflows that (1) provide a daily report on active tag deployments and (2) segment and map migratory movements. Conclusions The MoveApps platform is meant to empower the community to supply, exchange and use analysis code in an intuitive environment that allows fast and traceable results and feedback. By bringing together analytical experts developing movement analysis methods and code with those in need of tools to explore, answer questions and inform decisions based on data they collect, we intend to increase the pace of knowledge generation and integration to match the huge growth rate in bio-logging data acquisition. 

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5. Terrestrial Parasite Tracker indexed biotic interactions and review summary

   https://doi.org/10.5281/zenodo.6761707  

   Poelen, Jorrit H. ; Seltmann, Katja C. ; Campbell, Mariel ; Orlofske, Sarah A. ; Light, Jessica E. ; Tucker, Erika M. ; Demboski, John R ; McElrath, Tommy ; Grinter, Christopher C ; Diaz-Bastin, Rachel ; et al ( January 2022 , Zenodo)

   PLEASE CONTACT AUTHORS IF YOU CONTRIBUTE AND WOULD LIKE TO BE LISTED AS A CO-AUTHOR. (this message will be removed some time weeks/months after the first publication)

   Terrestrial Parasite Tracker indexed biotic interactions and review summary.

   The Terrestrial Parasite Tracker (TPT) project began in 2019 and is funded by the National Science foundation to mobilize data from vector and ectoparasite collections to data aggregators (e.g., iDigBio, GBIF) to help build a comprehensive picture of arthropod host-association evolution, distributions, and the ecological interactions of disease vectors which will assist scientists, educators, land managers, and policy makers. Arthropod parasites often are important to human and wildlife health and safety as vectors of pathogens, and it is critical to digitize these specimens so that they, and their biotic interaction data, will be available to help understand and predict the spread of human and wildlife disease.

   This data publication contains versioned TPT associated datasets and related data products that were tracked, reviewed and indexed by Global Biotic Interactions (GloBI) and associated tools. 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.

   If you have questions or comments about this publication, please open an issue at https://github.com/ParasiteTracker/tpt-reporting or contact the authors by email.

   Funding:
   The creation of this archive was made possible by the National Science Foundation award "Collaborative Research: Digitization TCN: Digitizing collections to trace parasite-host associations and predict the spread of vector-borne disease," Award numbers DBI:1901932 and DBI:1901926

   References:
   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. https://doi.org/10.1016/j.ecoinf.2014.08.005.

   GloBI Data Review Report

   Datasets under review:
    - University of Michigan Museum of Zoology Insect Division. Full Database Export 2020-11-20 provided by Erika Tucker and Barry Oconner. accessed via https://github.com/EMTuckerLabUMMZ/ummzi/archive/6731357a377e9c2748fc931faa2ff3dc0ce3ea7a.zip on 2022-06-24T14:02:48.801Z
    - Academy of Natural Sciences Entomology Collection for the Parasite Tracker Project accessed via https://github.com/globalbioticinteractions/ansp-para/archive/5e6592ad09ec89ba7958266ad71ec9d5d21d1a44.zip on 2022-06-24T14:04:22.091Z
    - Bernice Pauahi Bishop Museum, J. Linsley Gressitt Center for Research in Entomology accessed via https://github.com/globalbioticinteractions/bpbm-ent/archive/c085398dddd36f8a1169b9cf57de2a572229341b.zip on 2022-06-24T14:04:37.692Z
    - Texas A&M University, Biodiversity Teaching and Research Collections accessed via https://github.com/globalbioticinteractions/brtc-para/archive/f0a718145b05ed484c4d88947ff712d5f6395446.zip on 2022-06-24T14:06:40.154Z
    - Brigham Young University Arthropod Museum accessed via https://github.com/globalbioticinteractions/byu-byuc/archive/4a609ac6a9a03425e2720b6cdebca6438488f029.zip on 2022-06-24T14:06:51.420Z
    - California Academy of Sciences Entomology accessed via https://github.com/globalbioticinteractions/cas-ent/archive/562aea232ec74ab615f771239451e57b057dc7c0.zip on 2022-06-24T14:07:16.371Z
    - Clemson University Arthropod Collection accessed via https://github.com/globalbioticinteractions/cu-cuac/archive/6cdcbbaa4f7cec8e1eac705be3a999bc5259e00f.zip on 2022-06-24T14:07:40.925Z
    - Denver Museum of Nature and Science (DMNS) Parasite specimens (DMNS:Para) accessed via https://github.com/globalbioticinteractions/dmns-para/archive/a037beb816226eb8196533489ee5f98a6dfda452.zip on 2022-06-24T14:08:00.730Z
    - Field Museum of Natural History IPT accessed via https://github.com/globalbioticinteractions/fmnh/archive/6bfc1b7e46140e93f5561c4e837826204adb3c2f.zip on 2022-06-24T14:18:51.995Z
    - Illinois Natural History Survey Insect Collection accessed via https://github.com/globalbioticinteractions/inhs-insects/archive/38692496f590577074c7cecf8ea37f85d0594ae1.zip on 2022-06-24T14:19:37.563Z
    - UMSP / University of Minnesota / University of Minnesota Insect Collection accessed via https://github.com/globalbioticinteractions/min-umsp/archive/3f1b9d32f947dcb80b9aaab50523e097f0e8776e.zip on 2022-06-24T14:20:27.232Z
    - Milwaukee Public Museum Biological Collections Data Portal accessed via https://github.com/globalbioticinteractions/mpm/archive/9f44e99c49ec5aba3f8592cfced07c38d3223dcd.zip on 2022-06-24T14:20:46.185Z
    - Museum for Southern Biology (MSB) Parasite Collection accessed via https://github.com/globalbioticinteractions/msb-para/archive/178a0b7aa0a8e14b3fe953e770703fe331eadacc.zip on 2022-06-24T15:16:07.223Z
    - The Albert J. Cook Arthropod Research Collection accessed via https://github.com/globalbioticinteractions/msu-msuc/archive/38960906380443bd8108c9e44aeff4590d8d0b50.zip on 2022-06-24T16:09:40.702Z
    - Ohio State University Acarology Laboratory accessed via https://github.com/globalbioticinteractions/osal-ar/archive/876269d66a6a94175dbb6b9a604897f8032b93dd.zip on 2022-06-24T16:10:00.281Z
    - Frost Entomological Museum, Pennsylvania State University accessed via https://github.com/globalbioticinteractions/psuc-ento/archive/30b1f96619a6e9f10da18b42fb93ff22cc4f72e2.zip on 2022-06-24T16:10:07.741Z
    - Purdue Entomological Research Collection accessed via https://github.com/globalbioticinteractions/pu-perc/archive/e0909a7ca0a8df5effccb288ba64b28141e388ba.zip on 2022-06-24T16:10:26.654Z
    - Texas A&M University Insect Collection accessed via https://github.com/globalbioticinteractions/tamuic-ent/archive/f261a8c192021408da67c39626a4aac56e3bac41.zip on 2022-06-24T16:10:58.496Z
    - University of California Santa Barbara Invertebrate Zoology Collection accessed via https://github.com/globalbioticinteractions/ucsb-izc/archive/825678ad02df93f6d4469f9d8b7cc30151b9aa45.zip on 2022-06-24T16:12:29.854Z
    - University of Hawaii Insect Museum accessed via https://github.com/globalbioticinteractions/uhim/archive/53fa790309e48f25685e41ded78ce6a51bafde76.zip on 2022-06-24T16:12:41.408Z
    - University of New Hampshire Collection of Insects and other Arthropods UNHC-UNHC accessed via https://github.com/globalbioticinteractions/unhc/archive/f72575a72edda8a4e6126de79b4681b25593d434.zip on 2022-06-24T16:12:59.500Z
    - Scott L. Gardner and Gabor R. Racz (2021). University of Nebraska State Museum - Parasitology. Harold W. Manter Laboratory of Parasitology. University of Nebraska State Museum. accessed via https://github.com/globalbioticinteractions/unl-nsm/archive/6bcd8aec22e4309b7f4e8be1afe8191d391e73c6.zip on 2022-06-24T16:13:06.914Z
    - Data were obtained from specimens belonging to the United States National Museum of Natural History (USNM), Smithsonian Institution, Washington DC and digitized by the Walter Reed Biosystematics Unit (WRBU). accessed via https://github.com/globalbioticinteractions/usnmentflea/archive/ce5cb1ed2bbc13ee10062b6f75a158fd465ce9bb.zip on 2022-06-24T16:13:38.013Z
    - US National Museum of Natural History Ixodes Records accessed via https://github.com/globalbioticinteractions/usnm-ixodes/archive/c5fcd5f34ce412002783544afb628a33db7f47a6.zip on 2022-06-24T16:13:45.666Z
    - Price Institute of Parasite Research, School of Biological Sciences, University of Utah accessed via https://github.com/globalbioticinteractions/utah-piper/archive/43da8db550b5776c1e3d17803831c696fe9b8285.zip on 2022-06-24T16:13:54.724Z
    - University of Wisconsin Stevens Point, Stephen J. Taft Parasitological Collection accessed via https://github.com/globalbioticinteractions/uwsp-para/archive/f9d0d52cd671731c7f002325e84187979bca4a5b.zip on 2022-06-24T16:14:04.745Z
    - Giraldo-Calderón, G. I., Emrich, S. J., MacCallum, R. M., Maslen, G., Dialynas, E., Topalis, P., … Lawson, D. (2015). VectorBase: an updated bioinformatics resource for invertebrate vectors and other organisms related with human diseases. Nucleic acids research, 43(Database issue), D707–D713. doi:10.1093/nar/gku1117. accessed via https://github.com/globalbioticinteractions/vectorbase/archive/00d6285cd4e9f4edd18cb2778624ab31b34b23b8.zip on 2022-06-24T16:14:11.965Z
    - WIRC / University of Wisconsin Madison WIS-IH / Wisconsin Insect Research Collection accessed via https://github.com/globalbioticinteractions/wis-ih-wirc/archive/34162b86c0ade4b493471543231ae017cc84816e.zip on 2022-06-24T16:14:29.743Z
    - Yale University Peabody Museum Collections Data Portal accessed via https://github.com/globalbioticinteractions/yale-peabody/archive/43be869f17749d71d26fc820c8bd931d6149fe8e.zip on 2022-06-24T16:23:29.289Z

   Generated on:
   2022-06-24

   by:
   GloBI's Elton 0.12.4 
   (see https://github.com/globalbioticinteractions/elton).

   Note that all files ending with .tsv are files formatted 
   as UTF8 encoded tab-separated values files.

   https://www.iana.org/assignments/media-types/text/tab-separated-values

   
   Included in this review archive are:

   README:
     This file.

   review_summary.tsv:
     Summary across all reviewed collections of total number of distinct review comments.

   review_summary_by_collection.tsv:
     Summary by reviewed collection of total number of distinct review comments.

   indexed_interactions_by_collection.tsv: 
     Summary of number of indexed interaction records by institutionCode and collectionCode.

   review_comments.tsv.gz:
     All review comments by collection.

   indexed_interactions_full.tsv.gz:
     All indexed interactions for all reviewed collections.

   indexed_interactions_simple.tsv.gz:
     All indexed interactions for all reviewed collections selecting only sourceInstitutionCode, sourceCollectionCode, sourceCatalogNumber, sourceTaxonName, interactionTypeName and targetTaxonName.

   datasets_under_review.tsv:
     Details on the datasets under review.

   elton.jar: 
     Program used to update datasets and generate the review reports and associated indexed interactions.

   datasets.zip:
     Source datasets used by elton.jar in process of executing the generate_report.sh script.

   generate_report.sh:
     Program used to generate the report

   generate_report.log:
     Log file generated as part of running the generate_report.sh script
    

    

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