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First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt: scripts and models
<p>In this repository you will find the models and the scripts used to generate the journal article: "First application of artificial neural networks to estimate 21st century Greenland ice sheet surface melt."</p> <p>The model.tar contains the script for making a model, in addition to the models used in the journal artcile.</p> <p>The proc.tar contains the scripts used for processing of the CMIP6 data.</p> <p>The plots.tar contains scripts for generating the plots in the journal article, as well as the supplementary information.</p>
List of documents and patterns identified by multivocal literature review of software engineering patterns for machine learning applications
We performed a multivocal literature review of both academic and gray literature to collect software engineering for machine learning (ML) application systems and software design. For the academic literature, we chose Engineering Village. For the gray literature, we used a Google search on August 16, 2019. We retrieved 32 scholarly documents and 48 gray literature documents. We vetted whether each document should be included in our review using the following criteria: Documents written in English addressing concrete software-engineering patterns or practices to design ML application systems and software should be included. Documents focusing on design of ML techniques and algorithms should be excluded. This process identified 19 scholarly documents and 19 gray documents. Although 69 patterns related to the design of ML application systems were initially identified, 33 remained after the vetting process. Finally, industrial ML developers reviewed the 33 candidates from the viewpoint of practical usefulness. They identified only 15 ML patterns.
The State of Serverless Applications: Collection,Characterization, and Community Consensus - Replication Package
<p>The replication package for our article <em>The State of Serverless Applications: Collection,Characterization, and Community Consensus</em> provides everything required to reproduce all results for the following three studies:</p> <ul> <li>Serverless Application Collection</li> <li>Serverless Application Characterization</li> <li>Comparison Study</li> </ul> <p><strong>Serverless Application Collection</strong></p> <p>We collect descriptions of serverless applications from open-source projects, academic literature, industrial literature, and scientific computing.</p> <p><em>Open-source Applications</em></p> <p>As a starting point, we used an existing data set on open-source serverless projects from <a href="https://gupea.ub.gu.se/bitstream/2077/62544/1/gupea_2077_62544_1.pdf">this study</a>. We removed small and inactive projects based on the number of files, commits, contributors, and watchers. Next, we manually filtered the resulting data set to include only projects that implement serverless applications. We provide <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Collection/Open%20source%20filtering.xlsx">a table</a> containing all projects that remained after the filtering alongside the notes from the manual filtering.</p> <p><em>Academic Literature Applications</em></p> <p>We based our search on an <a href="https://doi.org/10.5281/zenodo.1175423">existing community-curated dataset</a> on literature for serverless computing consisting of over 180 peer-reviewed articles. First, we filtered the articles based on title and abstract. In a second iteration, we filtered out any articles that implement only a single function for evaluation purposes or do not include sufficient detail to enable a review. As the authors were familiar with some additional publications describing serverless applications, we contributed them to the community-curated dataset and included them in this study. We provide <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Collection/Academic%20literature%20filtering.xlsx">a table</a> with our notes from the manual filtering.</p> <p><em>Scientific Computing Applications</em></p> <p>Most of these scientific computing serverless applications are still at an early stage and therefore there is little public data available. One of the authors is employed at the German Aerospace Center (DLR) at the time of writing, which allowed us to collect information about several projects at DLR that are either currently moving to serverless solutions or are planning to do so. Additionally, an application from the German Electron Synchrotron (DESY) could be included. For each of these scientific computing applications, we provide a document containing a description of the project and the names of our contacts that provided information for the characterization of these applications.</p> <ul> <li>SC1 Copernicus Sentinel-1 for near-real-time water monitoring</li> <li>SC2 Reprocessing Sentinel 5 Precursor data with ProEO</li> <li>SC3 High-Performance Data Analytics for Earth Observation</li> <li>SC4 Tandem-L exploitation platform</li> <li>SC5 Global Urban Footprint</li> <li>SC6 DESY - High Throughput Data Taking</li> </ul> <p><em>Collection of serverless applications</em></p> <p>Based on the previously described methodology, we collected a diverse dataset of 89 serverless applications from open-source projects, academic literature, industrial literature, and scientific computing. This dataset is can be found <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Dataset.xlsx">i</a>n Dataset.xlsx.</p> <p><strong>Serverless Application Characterization</strong></p> <p>As previously described, we collected 89 serverless applications from four different sources. Subsequently, two randomly assigned reviewers out of seven available reviewers characterized each application along 22 characteristics in a structured collaborative review sheet. The characteristics and potential values were defined a priori by the authors and iteratively refined, extended, and generalized during the review process. The initial moderate inter-rater agreement was followed by a discussion and consolidation phase, where all differences between the two reviewers were discussed and resolved. The six scientific applications were not publicly available and therefore characterized by a single domain expert, who is either involved in the development of the applications or in direct contact with the development team.</p> <p><em>Initial Ratings & Interrater Agreement Calculation</em></p> <p>The initial reviews are available as <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Initial%20Characterizations.csv">a table</a>, where every application is characterized along with the 22 characteristics. A single value indicates that both reviewers assigned the same value, whereas a value of the form <code>[Reviewer 2] A | [Reviewer 4] B</code> indicates that for this characteristic, reviewer two assigned the value A, whereas reviewer assigned the value B.</p> <p>Our script for the calculation of the Fleiß-Kappa score based on this data is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/CalculateKappa.py">publically available</a>. It requires the python package <code>pandas</code> and <code>statsmodels</code>. It does not require any input and assumes that the file <code>Initial Characterizations.csv</code> is located in the same folder. It can be executed as follows:</p> <pre><code>python3 CalculateKappa.py </code></pre> <p><em>Results Including Unknown Data</em></p> <p>In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find.</p> <p>For six characteristics, many applications were assigned the ''Unknown'' value, i.e., the reviewers were not able to determine the value of this characteristic. Therefore, we excluded these characteristics from this study. For the remaining characteristics, the percentage of ''Unknowns'' ranges from 0–19% with two outliers at 25% and 30%. These ''Unknowns'' were excluded from the percentage values presented in the article. As part of our replication package, we provide the raw results for each characteristic including the ''Unknown'' percentages in the form of bar charts.</p> <p>The script for the generation of these bar charts is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/GenerateResultsIncludingUnknown.py">part of this replication package</a>). It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>. It does not require any input and assumes that the file <code>Dataset.csv</code> is located in the same folder. It can be executed as follows:</p> <pre><code>python3 GenerateResultsIncludingUnknown.py </code></pre> <p><em>Final Dataset & Figure Generation</em></p> <p>In the following discussion and consolidation phase, the reviewers compared their notes and tried to reach a consensus for the characteristics with conflicting assignments. In a few cases, the two reviewers had different interpretations of a characteristic. These conflicts were discussed among all authors to ensure that characteristic interpretations were consistent. However, for most conflicts, the consolidation was a quick process as the most frequent type of conflict was that one reviewer found additional documentation that the other reviewer did not find. Following this process, we were able to resolve all conflicts, resulting in a collection of 89 applications described by 18 characteristics. This dataset is available here: <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/Dataset.xlsx">link</a></p> <p>The script to generate all figures shown in the chapter "Serverless Application Characterization can be found <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Serverless%20Application%20Characterization/GenerateFigures.py">here</a>. It does not require any input but assumes that the file <code>Dataset.csv</code> is located in the same folder. It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>. It can be executed as follows:</p> <pre><code>python3 GenerateFigures.py </code></pre> <p><em>Comparison Study</em></p> <p>To identify existing surveys and datasets that also investigate one of our characteristics, we conducted a literature search using Google as our search engine, as we were mostly looking for grey literature. We used the following search term:</p> <pre><code>("serverless" OR "faas") AND ("dataset" OR "survey" OR "report") after: 2018-01-01 </code></pre> <p>This search term looks for any combination of either serverless or faas alongside any of the terms dataset, survey, or report. We further limited the search to any articles after 2017, as serverless is a fast-moving field and therefore any older studies are likely outdated already. This search term resulted in a total of 173 search results. In order to validate if using only a single search engine is sufficient, and if the search term is broad enough, we checked if the seven studies the authors were already familiar with are contained in the search results. As all seven studies were contained in the search results, we concluded that the literature search was broad enough. In a first iteration, we filtered out all results that do not either report original data or report on data from another study. Next, we removed all reports on secondary data, where the original study was already contained in the search results. This process resulted in a total of 16 identified studies. Finally, we determined for each identified study if they investigate one of our characteristics. This resulted in a total of ten related studies. The results from the literature search and the notes from the filtering are part of this replication package as <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/ComparisonSearch.xlsx">a table</a>.</p> <p>As these studies use different answer options than our study, we mapped their answer options to ours. In many cases, this was straightforward, such as mapping HTTP to HTTP Request. If the answer option granularities between the studies differed, we aggregated answer options from the study with lower granularity to match the higher granularity study. In case the lower granularity study allowed multiple answers, we selected only the highest value instead of aggregating them, to avoid counting a single study participant multiple times. As this mapping process is somewhat subjective, we provide a detailed account of the mapping for each characteristic and related study <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/Comparison%20Mappings.xlsx">as a multi-sheet excel table</a>, where each sheet shows our mapping alongside our notes which answer options were mapped.</p> <p>For many studies, not all information required for traditional meta-analysis techniques, such as cohort size, is available, preventing the application of these meta-analysis techniques. Therefore, we came up with an agreement metric that equally weights the agreement of the reported ranking and the agreement of the reported percentage values. It combines the relative difference between the reported percentages of both studies and the order of the reported popularities of the answer options. We categorize scores in the range [0.8, 1] as very high agreement, [0.6,0.6[ as high agreement, [0.4, 0.6[ as medium agreement, [0.2, 0.4[ as low agreement, and [0, 0.2[ as very low agreement. We acknowledge that these categories are somewhat arbitrary, however, based on a manual inspection of the results, they do seem to capture the level of agreement between the individual studies quite well. Our replication package includes <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/Comparison%20Mappings.xlsx">the mapped data</a> alongside the resulting scores to enable a manual inspection of the degree of agreement. The script that implements the calculation of our score is also <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/corroboration_analysis.py">publically available</a>. It uses the python packages <code>pandas</code>, <code>numpy</code>, and <code>scipy</code>. The script does not require any input and assumes that the file <code>Comparison Mappings.xlsx</code> is located in the same folder. The script can be executed as follows:</p> <pre><code>python3 corroboration_analysis.py </code></pre> <p>Further, the script to generate all figures shown in the chapter <em>Comparison Analysis</em> is the final piece of our replication package: <a href="https://github.com/ServerlessApplications/ReplicationPackage/blob/main/Comparison%20Study/barcharts.py">link</a>. It uses the python packages <code>numpy</code> and <code>matplotlib</code>. It does not require any input and can be executed as follows:</p> <pre><code>python3 barcharts.py </code></pre> <p>If you have any questions about our study or require any additional information/data please contact the first author.</p>
Figure 1 in Application of species-richness estimators for the assessment of earthworm diversity
Figure 1. Performance of eight species-richness estimators (dashed lines) for earthworms sampling data set: ACE; ICE; Chao 1; Chao 2; Jack 1; Jack 2; Bootstrap; Michaelis–Menten asymptote, and the species accumulation curve (solid lines).
Supplementary Material for: 'An impedance pneumography signal quality index: design, assessment and application to respiratory rate monitoring'
<p>This supplementary material accompanies:</p> <p>Charlton P.H. <em>et al.</em>, "<a href="https://doi.org/10.1016/j.bspc.2020.102339">An impedance pneumography signal quality index for respiratory rate monitoring: design, assessment and application</a>", <em>Biomedical Signal Processing and Control</em>, 65, 102339, 2021.</p> <p>The Impedance Pneumography Signal Quality Index (SQI) dataset and accompanying scripts (in Matlab format) are provided to facilitate reproduction of the analyses using data from the MIMIC III dataset in this publication.</p> <p><strong>Summary of Publication</strong></p> <p>In this article we developed and assessed the performance of a signal quality index (SQI) for the impedance pneumography signal.<br> The SQI was developed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a>, and assessed using data from the <a href="http://peterhcharlton.github.io/RRest/listen_dataset.html">Listen dataset</a> and MIMIC III datasets.<br> The SQI was found to accurately classify segments of impedance pneumography signal as either high or low quality. Furthermore, when it was coupled with a high performance RR algorithm, highly accurate and precise RRs were estimated from those segments deemed to be high quality. In this study performance was assessed in the critical care environment - further work is required to deteremine whether the SQI is suitable for use with wearable sensors. Both the dataset and code used to perform this study are publicly available.</p> <p><strong>Reproducing this Publication</strong></p> <p>The work relating to the MIMIC dataset in this publication can be reproduced as follows:</p> <p><strong> - Reproducing the analysis</strong><br> These steps can be used to quickly reproduce the analysis using the curated and annotated dataset.</p> <p>* Download the curated and annotated dataset from <a href="https://doi.org/10.5281/zenodo.3973770">Zenodo</a> using this <a href="https://zenodo.org/record/3973771/files/mimic_imp_sqi_data.mat?download=1">direct download link</a>.<br> * Run the analysis using the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.</p> <p><strong> - Full reproduction</strong><br> These steps include downloading the raw data files, extracting data from these files, collating the dataset, manually annotating the data, and performing the analysis.</p> <p>* Use the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script to download raw MIMIC data files from PhysioNet, and collate them into a single Matlab file.<br> * Prepare the dataset for manual annotation by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> script.<br> * Manually annotate the signals by running the <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_mimic_imp_annotation.m</em></a> script - the annotations are stored in separate files (the original annotation files are available <a href="https://zenodo.org/record/3974113/files/2019_annotations.zip?download=1">here</a>).<br> * Import the manual annotations into the collated data file by re-running the <a href="https://zenodo.org/record/3973771/files/ImP_SQI_mimic_data_importer.m?download=1"><em>ImP_SQI_mimic_data_importer.m</em></a> script.<br> * Run <a href="https://zenodo.org/record/3973771/files/run_imp_sqi_mimic.m?download=1"><em>run_imp_sqi_mimic.m</em></a> to perform the analysis described in the publication.</p> <p><strong> - Submitted manuscript</strong></p> <p>The submitted manuscript is available <a href="https://zenodo.org/record/5211463/files/Impedance%20SQI%20manuscript%20-%20Oct%202020%20revision.docx?download=1">here</a>.</p> <p>The scripts are also stored (alongside details of how to use them) are available in the <a href="http://peterhcharlton.github.io/RRest/">RRest GitHub repository</a> at: <a href="https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI">https://github.com/peterhcharlton/RRest/tree/master/RRest_v3.0/Publication_Specific_Scripts/ImP_SQI</a></p> <p>License: The dataset (<a href="https://zenodo.org/record/3974113/files/mimic_imp_sqi_data.mat?download=1"><em>mimic_imp_sqi_data.mat</em></a>) is distributed under the terms specified in the accompanying LICENSE file. The scripts are distributed under the GNU General Public Licence (as specified towards the start of each file).</p> <p>Version 1.0: This version includes the submitted manuscript.</p> <p> </p>
FIGURES 31–35 in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 31–35. Three specimens of Apis mellifera L. (Apidae) from the same collecting event, stored in alcohol for 4 years. 31 & 33, Specimen 1, processed with the air pump, using nozzle #2 (see Fig. 2), for one minute. 32 & 34, Specimen 2, processed with the hair dryer for one minute. 35, Specimen 3, immersed in absolute alcohol and then in ethyl acetate, for one hour in each, then bathed in ethyl acetate again after pinned, to loosen the pilosity, then immediately processed with the hair dryer for one minute.
FIGURES 22–30 in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 22–30. Processing of Diptera specimens. All specimens from the same collecting event, stored in alcohol for 3 years. First column: specimens as removed from alcohol. Second column: immediately after air blow session. Third column: two days later, fully dried. 22–24, A soft, delicate Micropezidae, submitted to a 60 seconds session. 25–27, Muscidae, 7 mm long, submitted to a 40 seconds session. 28–30, Sarcophagidae, 15 mm long, submitted to a 120 seconds session.
FIGURES 19–21 in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 19–21. Three specimens of the densely pilose Melipona rufiventris Lepeletier (Apidae); all specimens from the same collecting event; stored in alcohol for 9 years. 19, Specimen mounted manually (technique by Noyes 1982; see also Table 1). 20, Specimen submitted to air blow for 40 seconds, using air pump without any nozzle. 21, A third specimen, photographed immediately after a 40 seconds session of air blow using nozzle #2 (Fig. 2).
FIGURES 11–13. A 4.5 in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 11–13. A 4.5 mm long specimen of Bracon sp. (Hymenoptera, Braconidae) under different stages of mounting, after stored in alcohol for 7 years. 11, Pinned with a minuten, in alcohol. 12, Double mount, while still soaked in alcohol. 13, A few moments after a 30 seconds session of air blow.
FIGURES 3–10. A in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 3–10. A specimen of Liris sp. (Hymenoptera, Crabronidae) under different stages of mounting. 3, Stored in 85% alcohol, after one year. 4, Condition of specimen immediately after removed from alcohol and briefly dried on an absorbent card. 5, Detail of right wings on glass surface, after receiving a drop of alcohol; note folding of hind wings. 6, Pinned specimen, still soaked in alcohol; left side. 7, Same specimen, immediately after it started receiving an air blow – note raised metasoma, and left hind wing still wet, applied over the body. 8, Same as previous, 15 seconds later; all wings now vibrating; note golden pilosity already drying on gena, mesosoma, and part of metasoma. 9, Same, a few moments after receiving a 20 seconds session of air blow; specimen not manually repositioned in any way. 10, Same, dorsal view.
FIGURES 1–2 in A technique to dry mount Hymenoptera (Hexapoda) from alcohol in a few seconds, and its application to other insect orders
FIGURES 1–2. Air pump of 600 L/min, used in this work. Frontal air exit measures 32 mm in diameter. 1, Air pump. 2, Nozzles; from top to bottom: #1 (20 mm dia.), #2 (12 mm dia.), #3 (5 mm dia.).
Effect of a single application of Scenar for pain relief in patients with low back and neck pain
<p class="MDPI17abstract">We aimed to demonstrate the antalgic effectiveness of ScenarTM (Self Controlled Electro Neuro Adaptative Regulation) in patients experiencing low back and neck pain. Sixty patients were included and equally assigned by randomization to a Scenar-On group and to a Scenar-Off group (sham group). All patients received a 20 minutes application of ScenarTM of the painful area. Pain at rest and at movement, and sensation of stiffness were assessed using a numeric rating scale at baseline, right after the session and 24 hours following the session. Patients' characteristics at entry were similar between groups. Pain at rest decreased after the session in both groups (from 7.5 [4.2] to 5.0 [3.5]) in the Scenar-Off group, p= 0.0001, from 6.5 [3.0] to 4.0 [4.0]) in the Scenar-On group p<0.0001). Similar results were observed at mobilization but sensation of stiffness was not modified. Such beneficial results do not last until the next day. No undesirables major effects were noticed. Our study does not support the fact that one Scenar session improves better low back and neck pain than a sham session.</p>
FIGURES 37–39 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 37–39. Pseudosinella guanhaensis sp.nov.; 37, Abd IV chaetotaxy, left side; 38, tibiotarsi I; 39 empodial complex leg III.
FIGURES 30–34 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 30–34. Pseudosinella guanhaensis sp.nov.; 30, Details of sense organ of Ant. III with two type c, three type e and one type h sensilla, and surrounded by macrochaetae; 31, Details of sensilla at the ventro-lateral distal half Ant. II; 32, details of Ant. I dorso-laterally with 3 type h, 1 type i and 1 type a sensilla, and 2 h and 3 l; 33, ventro-lateral sensilla; 34, dorsal cephalic chaetotaxy.
FIGURES 35–36 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 35–36. Pseudosinella guanhaensis sp.nov.; 35, labial triangle chaetotaxy, left side; 36, trunk chaetotaxy, left side.
FIGURES 26–29 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 26–29. Pseudosinella guanhaensis sp.nov.; 26, antennal segment IV dorsal view, letters as in Fig. 17; 27, antennal segment III dorsal view, letter as in Fig. 17; 28, antennal segments I and II dorsal view, letters as in Fig. 17; 29, antennal segments I and II ventral view, letters as in Fig. 17.
FIGURES 21–25 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 21–25. Pseudosinella ambigua sp. nov.: 21, Abd IV dorsal chaetotaxy; 22, empodial complex leg III; 23, ventral tube anterior side (left); 24, Ventral tube posterior side (rigth); 25, mucro.
FIGURES 19–20 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 19–20. Pseudosinella ambigua sp. nov.: 19, labial triangle chaetotaxy, right side; 20, trunk chaetotaxy, right side.
FIGURES 11–12 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 11–12. Arrhopalites glabrofasciatus sp. nov.: 11, chaetotaxy of manubrium and dens; 12, mucro.
FIGURES 17–18 in Three new species of Collembola (Arthropoda: Hexapoda) from Central Brazilian shallow caves: side effects of long term application of environmental law on conservation
FIGURES 17–18. Pseudosinella ambigua sp. nov.: 17, antennal and body chaetae: a) subapical micro sense rod. b) sense type I. c) sense type II. d) sense type III. e) sense type IV. f) bristle-like. g) ciliated chaeta. h) microsensillum. i) micro sense organ. j) sense type V. l) sense type VI. m) fan-shaped chaeta type I. n) fan-shaped chaeta type II. o) micro or mesochaeta symbol. p) macrochaeta symbols. q) pseudopore symbols. r) bothriotrix. s) stout ciliated macrochaetae; 18, dorsal cephalic chaetotaxy.
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.