Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
57
datasets available to search
ShareScore release 0.9.0
Dataset results
57 results for “SHERLOCK”
Figure 15 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 15 Chaetae of Apistobranchus jasoni sp. nov. (holotype NHM.2018.12712) a abdominal chaetiger b detail of neuropodial acicular chaeta and accompanying capillary c, d overview of neuropodial chaetal fascicle with long capillaries with frayed (damaged) tips and short falcate chaetae e detail short falcate neurochaetae. Scale bar: 250 µm (a); 25 µm (b); 100 µm (c–e).
Figure 23 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 23 Aphelochaeta falklandica sp. nov. (holotype NHM.2018.21708), Methyl Green Staining patterns a complete specimen b ventral view of thoracic chaetigers showing methyl green stained bands across the thorax c dorsal view of head and thoracic chaetigers d close up of head region e dorsal view of anterior of specimen stained with Shirla stain showing dorsal tentacles f detail of pygidial region showing anal lobes. Scale bars: 1 mm (a).
Figure 3 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 3 Harmothoe anderssoni (voucher, NHM.2018.24629) a mid-body parapodium, insert showing line drawing of supra-acicular neuropodial lobe b bidentate neurochaetae c unidentate neurochaetae d mid-body elytron showing elongated marginal papillae e detail of elytral surface from mid-body elytron showing multifid macrotubercules. Scale bars: 500 µm (a, d); 50 µm (b, c); 200 µm (e).
Figure 8 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 8 Drawing of anterior end of Prosphaerosyllis modinouae sp. nov. in dorsal view (a) and detail of palps (b).
Figure 14 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 14 Apistobranchus jasoni sp. nov. Illustrations of holotype (NHM.2018.12712) a anterior end in dorsal view b anterior end in lateral view and c detail of chaetigers 4 to 10 (numbered). Scale bar: 1 mm.
Figure 22 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 22 Aphelochaeta cf. longisetosa Falkland Islands specimen – MGSP plate a ventrolateral view of anterior end b dorsal view of anterior end.
Figure 1 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 1 Map showing exploratory oil field (Sea Lion) in North Falklands Basin and exploratory oil wells in East Falklands Basin from which samples in this study were collected.
Figure 5 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 5 Prosphaerosyllis modinouae sp. nov. (paratype, NHM.2018.24386) a complete specimen in dorsal view b detail of anterior end in dorsal view c detail of prostomium and palps in dorsal view d detail of eyes e anterior dorsal cirrus f posterior dorsal cirrus g posterior parapodium (arrow marking acicula) h detail of acicula. Scale bars: 1000 µm (a); 100 µm (b, c); 25 µm (e, f); 50 µm (g).
Figure 19 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 19 Comparison of Leitoscoloplos olei sp. nov. and L. kerguelensisaL. olei sp. nov., holotype (NHM.2018.21756) in lateral view b comparison of L. olei sp. nov. and one of the syntypes (BMNH.ZK.1885.12.1.252) of L. kerguelensis, both specimens in lateral view. Scale bars: 1mm.
Figure 6 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 6 Prosphaerosyllis modinouae sp. nov. (holotype, NHM.2018.25100; specimen Shirla-stained) a complete specimen in dorsal view b pattern of dorsal body papillae (= darkly stained dots) in anterior end c pattern of dorsal body papillae (= darkly stained dots) in mid-body segments d pattern of ventral papillae (= darkly stained dots). Scale bars: 500 µm (a); 200 µm (b, d).
Figure 25 from: Neal L, Paterson GLJ, Blockley D, Scott B, Sherlock E, Huque C, Glover AG (2020) Biodiversity data and new species descriptions of polychaetes from offshore waters of the Falkland Islands, an area undergoing hydrocarbon exploration. ZooKeys 938: 1-86. https://doi.org/10.3897/zookeys.938.49349
Figure 25 Dodecaceria saeria sp. nov. (holotype NHM.2018.23622) a general views of anterior and posterior ends of worm b close-up of anterior c pygidium d chaetae of mid-body chaetiger e tube.
Sherlock Camera Trap Dataset
<h2>Data</h2><p>Base dataset containing the camera trap images used for the 'small' and 'paired' tests of Sherlock, andSherlock code.</p><p>Camera trap images used for the larger 'main' test can be found in the following datasets:</p><p>https://zenodo.org/uploads/10023354</p><p>https://zenodo.org/uploads/10026321</p><p>https://zenodo.org/uploads/10036325</p><p>https://zenodo.org/uploads/10039718</p><p>https://zenodo.org/uploads/10044242</p><p>https://zenodo.org/uploads/10047672</p><p>https://zenodo.org/uploads/10048102</p><p>https://zenodo.org/uploads/10048378</p><h2><a href="https://github.com/mpenn114/Sherlock#sherlock">Sherlock</a></h2><p>This repository contains a code package, Sherlock, which provides an easy-to-use tool for processing camera trapping images.</p><p>Its aim is to remove false positive images - that is, principally, images where the camera has been triggered by a very small disturbances, such as a plant blowing in the wind. If images containing a specific species is being targeted, it also allows for the user to easily customise parameters to set the colour of the deisred animals. This can help to filter out images containing other animals, and thus increase the accuracy of the code.</p><p>This code is aimed to be usable by someone with no prior coding experience. To help this, a guide to installing Python (the language which Sherlock is written in) is provided at the end of this readme.</p><h3><a href="https://github.com/mpenn114/Sherlock#-input-format-">Input Format</a></h3><p>This code can process JPG images (NB: it should be possible to edit the code to accept any image format). These images should be stored in folders/subfolders, with a single "master folder" containing all of these folders. Examples of acceptable folder structures are below:</p><p>[Master Folder] -> Images (that is, an image would have the path .../MasterFolder/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> Images (e.g. .../MasterFolder/Aberystwyth/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> [Sub-location Folders] -> Images (e.g. .../MasterFolder/Aberystwyth/Constitution Hill/0001.JPG)</p><p>[Master Folder] -> [Location Folders] -> [Sub-location Folders] > [Camera Number] -> Images (e.g. .../MasterFolder/Aberystwyth/Constitution Hill/Camera1/0001.JPG)</p><p>In all of these cases, it is simply necessary to specify the master folder. Note that it is also possible to have a mixture of these cases (e.g. some locations may not have sub-locations)</p><p>It is important that the JPG images in each image folder are named consecutively as 0001.JPG, 0002.JPG, ... (note, the number of leading zeros is not important).</p><h3><a href="https://github.com/mpenn114/Sherlock#-output-format-">Output Format</a></h3><p>The code can produce different kinds of outputs, depending on the needs of the user.</p><p>The primary output is, for each folder of images, a folder containing CSV files, labelled as "CSV_Outputs[Runcode]". These files are "Potential_Animals" (a list of all images that the code believes may contain animals); "Unlikely_Animals" (a list of all images that the code believes do not contain an animal); "Errors" (a list of images that could not be processed); "Close to Animals" (a list of images such that images close to them - in terms of image number and time - were identified as potential animals); "Overall Animals" (the combination of the lists in "Potential_Animals" and "Close_to_Animals") and "False_Negatives" (if the code is in "testing mode", explained below, a list of all the false negatives)</p><p>It is also possible to get the code to write any potential animal images into a new folder, called "PotentialAnimals[Runcode]" where any of the images that are identified as animals, along with those that are close to them, are re-written with red boxes indicating the locations in which animals were thought to be. Note that this does not edit the original images in any way. However, it can be turned off if desired (as these images will be reasonably large files)</p><p>Finally, the code can be put into "testing mode". This can be done by changing one of the parameters (explained at the start of the code file), and seeks to compare the results of the code with human-inputted results. A list of image numbers containing animals should be created, called "Animaldata.csv", and a list of image numbers not containing animals should be created, called "nonAnimaldata.csv". These should then be saved in the same folder as the images, and their inclusion will allow the code to create CSV outputs that compare the two sets of results. The code can also compare results according to a number of different characteristics of the image, such as time and location, provided that the format matches that of the example CSV which is included in this Github.</p><h3><a href="https://github.com/mpenn114/Sherlock#-installing-python-anaconda-and-jupyter-lab-">Installing Python, Anaconda and Jupyter Lab</a></h3><p>Anaconda (and thus, Python) can be installed by visiting:</p><p>Windows: <a href="https://docs.anaconda.com/anaconda/install/windows/">https://docs.anaconda.com/anaconda/install/windows/</a></p><p>Mac: <a href="https://docs.anaconda.com/anaconda/install/mac-os/">https://docs.anaconda.com/anaconda/install/mac-os/</a></p><p>Linux: <a href="https://docs.anaconda.com/anaconda/install/linux/">https://docs.anaconda.com/anaconda/install/linux/</a></p><p>Once Anaconda has been installed, you should be able to find "Anaconda Prompt", and open it to get a command window. Type</p><p>conda install -c conda-forge jupyterlab</p><p>into this window and press enter to install Jupyter Lab</p><h3><a href="https://github.com/mpenn114/Sherlock#-opening-jupyter-lab-">Opening Jupyter Lab</a></h3><p>To open Jupyter Lab, open Anaconda Prompt, type in</p><p>jupyter lab</p><p>and then press enter. It should open in your web browser (note: you do not need an Internet connection to do this, or to run any of this code, except the section immediately following)</p><h3><a href="https://github.com/mpenn114/Sherlock#-opening-sherlock-">Opening Sherlock</a></h3><p>You can copy the code for Sherlock onto your computer by opening a new notebook (by clicking the "Python 3" button below "Notebook" in the right hand window. Then, there should be a textbox with your cursor inside it. If you have Git installed on your computer (which you can install from here <a href="https://github.com/git-guides/install-git">https://github.com/git-guides/install-git</a>), you can copy the code for Sherlock by copying</p><p>!git clone <a href="https://github.com/mpenn114/Sherlock">https://github.com/mpenn114/Sherlock</a></p><p>into this textbox, and then pressing the run button (which is a button in the row of buttons above the textbox - it looks like a "play" button). You should then be able to see a folder called Sherlock on the left hand side of the screen. Double-click on this folder to open it, and then double-click on the file "Sherlock.ipynb" to open the notebook for Sherlock. This should then appear on the right hand window.</p><p>Otherwise, you can download the code from this repository as a zip file. After extracting this code (right click on the zip file and click "Extract All"), you then will need to copy the notebook "Sherlock.ipynb" to the folder that Python opens with when you start Jupyter Lab. On Windows, this will generally be "C:/Users/[Your username]".</p><p>This file then contains all the information needed to run the code at the top. The actual code is below this initial text and, once you are happy with the inputs and parameters, you can run it by pressing the clicking somewhere on it, and then pressing the "run" button.</p><p>Note: If you are using an old Mac operating system (iOS 13 or earlier) then you may have an error when running the code. This can be fixed by removing the line</p><p>!pip install opencv-python</p><p>from the code and replacing it with the two lines</p><p>!pip uninstall opencv-python --y</p><p>!pip install opencv-python==4.4.0.46</p><p>The code should then run without any errors.</p><p>If you are using a camera with an unusual metadata format, then you may need to use the Sherlock_legacy.ipynb file instead. This manually detects whether an image was taken during the day or at night, rather than using the image metadata. Do feel free to log this as an issue if the main version doesn't work and we will seek to add a fix!</p>
Figs. 796–799. Paralampona sherlock, new species. 796. Left male palp, ventral view. 797. Same, retrolateral view. 798. Epigynum, ventral view. 799 in A Relimitation And Revision Of The Australasian Ground Spider Family Lamponidae (Araneae: Gnaphosoidea)
Figs. 796–799. Paralampona sherlock, new species. 796. Left male palp, ventral view. 797. Same, retrolateral view. 798. Epigynum, ventral view. 799. Same, dorsal view.
Assessment of the Precision of the Sherlock 3CG Tip Positioning System
ClinicalTrials.gov study NCT01275430. IPD Sharing: NO. Countries: 1. Publications: 0.
Study to Assess Electrocardiogram Waveforms Produced Through the Utilization of the Sherlock 3CG™ Tip Confirmation System
ClinicalTrials.gov study NCT03028090. IPD Sharing: Not stated. Countries: 1. Publications: 0.
SHERLOCK 3CG™ Diamond Tip Confirmation System
ClinicalTrials.gov study NCT03288766. IPD Sharing: NO. Countries: 2. Publications: 0.
RSS intermediate files at Sherlock
Open the record for dataset details and reuse information.
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.