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Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p><strong>Context:</strong> Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies shows that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. <strong>Objective:</strong> This qualitative study aims at revealing the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. <strong>Method:</strong> We performed two industry case studies based on online focus group sessions. We asked developers to discuss how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. <strong>Results:</strong> Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns, for instance. Examples of interrelations include large size leads to low cohesion and high complexity leads to high coupling. <strong>Conclusions:</strong> Our findings could be combined with previous results on how refactorings affect quality attributes to assist developers in applying refactorings that may have a practically relevant impact on critical attributes.</p>
Quality of work organization, distress and absenteeism among healthcare workers
<p>Database of the study "Quality of work organization, distress and absenteeism among healthcare workers" to be published.</p>
Asynchronous Workload Balancing through Persistent Work-Stealing and Offloading for a Distributed Actor Model Library
<p>With dynamic imbalances caused by both software and ever more complex hardware, applications and runtime systems must adapt to dynamic load imbalances. We present a diffusion-based, reactive, fully asynchronous, and decentralized dynamic load balancer for a distributed actor library. With the asynchronous execution model, features such as remote procedure calls, and support for serialization of arbitrary types, UPC++ is especially feasible for the implementation of the actor model. While providing a substantial speedup for small- to medium-sized jobs with both predictable and unpredictable workload imbalances, the scalability of the diffusion-based approaches remains below expectations in most presented test cases.</p> <p>Actor-UPCXX is a high-performance computing library based on the actor model to enable the use of the actor model for HPC simulations. The source code can be found at: https://github.com/TUM-I5/Actor-UPCXX</p>
Making Data Work: A Systematic Mapping of Collaborative Data Curation Practices
<p>This file contains dataset associated with the paper Making Data Work: A Systematic Mapping of Collaborative Data Curation Practices</p>
OpenPack: Public multi-modal dataset for packaging work recognition in logistics domain
<p><strong>OpenPack</strong> is an open-access logistics dataset for human activity recognition, which contains human movement and package information from 16 subjects in four scenarios. Human movement information is subdivided into three types of data, acceleration, physiological, and depth-sensing. The package information includes the size and number of items included in each packaging job. </p> <p>In the "Humanware laboratory" at IST Osaka University, with the supervision of industrial engineers, an experiment to mimic logistic center labor was designed. 12 workers with previous packaging experience and 4 without experience performed a set of packaging tasks according to an instruction manual from a real-life logistics center. During the different scenarios, subjects were recorded while performing packing operations using Lidar, Kinect, and Realsense depth sensors while wearing 4 ATR IMU devices and 2 Empatica E4 wearable sensors. Besides sensor data, this dataset contains timestamp information collected from the hand terminal used to register product, packet, and address label codes as well as package details that can be useful to relate operations to specific packages.</p> <p>The 4 different scenarios include; sequential packing, worker-decided sequence changes, pre-ordered item packing, and time-sensitive stressors. Each of the subjects performed 20 packing jobs in 5 work sessions for a total of 100 packing jobs. <strong>53+</strong> hours of packaging operations have been labeled into 10 global operation classes and 16 sub-action classes for this dataset. Action classes are not unique to each operation but may only appear in one or two operations. </p> <p>You can find information on how to use this dataset at: <a href="https://open-pack.github.io/">https://open-pack.github.io/</a>. For details on how this dataset was collected please check the following publication "OpenPack: A Large-Scale Dataset for Recognizing Packaging Works in IoT-Enabled Logistic Environments" <a href="https://doi.ieeecomputersociety.org/10.1109/PerCom59722.2024.10494448">10.1109/PerCom59722.2024.10494448</a>.</p> <p> </p> <p><strong>Full Dataset</strong></p> <p>In this repository, the data and label files are contained in separate files for each worker. Each worker's file contains; IMU, E4, 2d keypoint, 3d keypoint, annotation, and system-related<em> </em>data<em>.</em></p> <p><em><strong>Preprocessed Dataset (IMU with operation and action Labels)</strong></em></p> <p>We have received many comments that it was difficult to combine multiple workers' IMU and annotation data. Therefore, we have created several CSV files containing the four IMU's sensor data and the operation labels in a single file. These files are now included as "imu-with-operation-action-labels.zip". </p> <p><em><strong>Preprocessed Dataset (Kinect 2D and 3D keypoint data with operation and action Labels)</strong></em></p> <p>We have received several requests for a preprocessed dataset containing only specific types of keypoint data with its assigned operation and action labels. Two new preprocessed files have been added for 2D and 3D keypoint data extracted from the frontal view Kinect camera. These files are:</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-2d-kpt-with-operation-action-labels.zip</a>", and</p> <p>"<a href="11059235" target="_blank" rel="noopener noreferrer">kinect-3d-kpt-with-operation-action-labels.zip</a>".</p> <p> </p> <p>Work is continuously being done to update and improve this dataset. When downloading and using this dataset please verify that the version is up to date with the latest release. The latest release <strong>[1.1.0]</strong> was uploaded on 24/04/2024. </p> <p><strong><em>Changes LOG:</em></strong></p> <ul> <li>v1.0.0: Add tutorial preprocessed dataset for IMU data with operation labels.</li> <li>v1.1.0: Update preprocessed datasets. (Include Kinect 2d and 3d keypoint data with Operation and action labels)</li> </ul> <p> </p> <p><strong>We hosted an activity recognition competition using this dataset (OpenPack v0.3.x) awarded at a PerCom 2023 Workshop! The task was very simple: Recognize 10 work operations from the OpenPack dataset. You can refer to this website for coding materials relevant to this dataset. </strong><a href="https://open-pack.github.io/challenge2022"><strong>https://open-pack.github.io/challenge2022</strong></a></p>
Translator observed while working and thinking aloud
<p>Translator Toni Aquilina being observed while revising a literary translation and thinking aloud (for details see Borg 2022).</p>
EEG recordings during resting-state and the maintenance periods of a spatial working memory task in humans
<p>Scripts used to analyze data for the manuscript submitted for publication in EJN</p> <p><strong>Script_Curve_Fitting_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com </p> <p><span>We therefore considered this continuous change in power as an extraneous variable </span><em><span>y<sub>k</sub>(x)</span></em><span> impacting the measured power spectrum </span><em><span>Pow(E<sub>k</sub>)</span></em><span>, and modeled it with a binomial equation that best fit the data, where the coefficients in <em>p<sub>i</sub></em> are in descending powers, and the length of <em>p</em> is <em>(n+1), k </em>is trial number (<em>k = 1 to 10</em>):</span></p> <p><strong><em><span>y<sub>k</sub>(x) = p<sub><span>1 </span></sub>. x<sup><span>2</span></sup><span><span> </span></span>+ p<sub><span>2 </span></sub>. x<span> </span>+ p<sub><span>3</span></sub></span></em></strong></p> <p><span>In order to statistically compare the topographies between the trials with perfect recall and the trials with failed recall, we subtracted this variable from the mean spectral topographies of each subject and for each electrode by first producing the mean spectral curves of each maintenance trial in the theta and alpha frequency bands, taking into account the IAF, and then calculating the coefficients (</span><em><span>p<sub>1</sub></span></em><span>, </span><em><span>p<sub>2</sub></span></em><span> and </span><em><span>p<sub>3</sub></span></em><span>) of the binomial equation using the Matlab function <em>polyfit.m.</em> Once the coefficients were determined, this estimate was subtracted from each power spectrum matrix using the following formula:</span></p> <p><strong><em><span>PowFit(E<sub><span>k</span></sub>) = Pow (E<sub><span>k</span></sub>) – </span></em></strong><strong><em><span>y<sub>k</sub>(x)</span></em></strong></p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Power_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Meziane: hbmeziane@gmail.com<br>This script calculates EEG power spectra then compares perf and fail conditions, then plots brain topographies with statical results</p> <p> </p> <p><strong>Script_Perf_Fail_EEG_Sources_Spec_HBM.rtf</strong></p> <p>Dr. Hadj Boumediene Meziane: hbmeziane@gmail.com<br>This script compares EEG source spectra then compares Perf vs. Fail conditions then plot statistical results (significant voxels) on MRI volume</p>
Ridgebelt Modeling Work
<p>This dataset includes 3 products: an excel spreadsheet with the modeling results of each fault (dip, depth, strike), a zip file including all the base inputs (imagery, topography) for the MOVE models, and the MOVE models and .movd files to facilitate the opening/use of those models.</p>
Job-Related Health Issues That Affect Employees Working in Pharmaceutical Marketing
<p><strong><span>Abstract</span></strong></p> <p><span>Marketing representatives play a critical role in a pharmaceutical organization in the development and sustainability of their business through helping in selling of products and services. It is not an easy job. The Medical Marketing representative is a high-risk job with immense stress and negative consequences for individuals. It requires more skills, wider knowledge and emotional stability than the other profession. Due to extensive traveling, wandering and waiting time, target issues, work-life balancing problems and lack of job security the medical representative feels exhausted.</span></p> <p><span>In India, the pharmaceutical industry is growing tremendously for the past few years. This industry is highly competitive in nature. It increases the need for the marketing representatives and also their roles and responsibilities simultaneously. The cut-throat competitive scenario in the market increases the pressure of achieving targets to pharmaceutical sales which ultimately induced their job stress and other health issues. So, the pharmacy institution must provide more attention to prevent and reduce the burnout of the marketing representatives, otherwise the institution will lose its reputation.</span></p> <p><strong><span>Keywords: </span></strong><span>occupational risks; pharmaceutical marketing force; road traffic accidents; violence; workplace stress; Burnout; Frustrating; Medical Representatives; Stress and Work-Life Balancing Problems; stress management Violence; Well-being.</span></p>
Plate XXIV, Figs L.1–L.3 – Perla bipunctata in the works of Pictet (1833, 1842). L.1, nymph of Perla bipunctata (Pictet 1833: his plate 5, Fig. 12); L.2, nymph of Perla bipunctata (Pictet 1842: his plate 11, Fig. 1); L.3, adult ♂ of Perla bipunctata (Pictet 1842: his plate 12, Fig. 3). in Steps towards a revision of the Perla bipunctata Pictet, 1833 species complex (Plecoptera: Perlidae)
Plate XXIV, Figs L.1–L.3 – Perla bipunctata in the works of Pictet (1833, 1842). L.1, nymph of Perla bipunctata (Pictet 1833: his plate 5, Fig. 12); L.2, nymph of Perla bipunctata (Pictet 1842: his plate 11, Fig. 1); L.3, adult ♂ of Perla bipunctata (Pictet 1842: his plate 12, Fig. 3).
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019) in Variability of the gene cyt b in the Korean field mouse Apodemus peninsulae Thomas, 1906 - a reservoir host of AMRV in the Khasansky District of Primorsky Krai
Рис. 1. ФиΛогенетические Αеревья хантавируса AMRV и его прироΑного носитеΛя восточноазиатской мыши Apodemus peninsulae Thomas, 1906. А. ФиΛогенетическое Αерево восточноазиатской мыши Apodemus peninsulae, построенное метоΑом «максимаΛьного правΑопоΑобия» (ML) и поΛученное на основе анаΛиза участка гена цитохрома b мтΔНК (744 п.н.). В узΛах ветвΛения указаны бутстреп-поΑΑержки, рассчитанные ΑΛя 1000 повторов. Цветными Λиниями обозначены фиΛогенетические Λинии: Αве Китайские (зеΛеный), Корейская «Korea» (синий), Амурская «Amur» (красный). ПоΛужирным шрифтом выΑеΛены собственные образцы. Названия образцов из GenBank/NCBI быΛи сокращены; B. ФиΛогенетическое Αерево из работы Α. Н. Яшиной с ΑопоΛнениями, построенное метоΑом «бΛижайшего сосеΑа» (NJ) на основе посΛеΑоватеΛьностей фрагмента М-сегмента (2737–2980 н.п.) генома хантавирусов. В узΛах ветвΛения указаны бутстреппоΑΑержки, рассчитанные ΑΛя 1000 повторов. Жирным выΑеΛены иссΛеΑованные РНК изоΛяты (Яшина 2012; Яшина и Αр. 2019) Fig. 1. Phylogenetic trees of AMRV and its natural reservoir host — the Korean field mouse Apodemus peninsulae Thomas, 1906. A. Phylogenetic tree of the Korean field mouse Apodemus peninsulae constructed by the "maximum likelihood" method (ML). The data are obtained from the analysis of the cytochrome b mtDNA gene fragments (744 bp). Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. Colored lines indicate phylogenetic lines: two Chinese (green), Korea (blue), and Amur (red). Own samples are highlighted in bold. The names of the samples from GenBank/NCBI have been shortened; B. Phylogenetic tree from L. N. Yashina's work with additions constructed by the neighbour joining method (NJ). It is based on the sequences of an M-segment fragment (2737–2980 bp) of the hantavirus genome. Bootstrap supports calculated for 1,000 repeats are indicated in the branching nodes. The researched RNA isolates are highlighted in bold (Yashina 2012; Yashina et al. 2019)
How does parenthood affect an ICT practitioner's work? A survey study with fathers
<p>Context: Many studies have investigated the perception of software development teams about gender bias, inclusion policies, and the impact of remote work on productivity. The studies indicate that mothers and fathers working in the software industry had to reconcile homework, work activities, and child care. </p> <p>Goal: This study investigates the impact of parenthood on Information and Communications Technology (ICT) and how the fathers perceive the mothers' challenges. Recognizing their difficulties and knowing the mothers' challenges can be the first step towards making the work environment friendlier for everyone.</p> <p>Method: We surveyed 155 fathers from industry and academia from 10 different countries, however, most of the respondents are from Brazil (92.3%). Data was analyzed quantitatively and qualitatively. We employed Grounded Theory to identify factors related to (i) paternity leave, (ii) working time after paternity, (iii) childcare-related activities, (iv) prejudice at work after paternity, (v) fathers' perception of prejudice against mothers, (vi) challenges and difficulties in paternity. </p> <p>Results: In general, fathers do not suffer harassment or prejudice at work for being fathers. However, they perceive that mothers suffer distrust in the workplace and live with work overload because they have to dedicate themselves to many activities. They also suggested actions to mitigate parents' difficulties. </p> <p>Conclusions: Despite some fathers wanting to participate more in taking care of their children, others do not even recognize the difficulties that mothers can face in the work. Therefore, it is important to explore the problems and implement actions to build a more parent-friendly work environment.</p> <p>Keywords: Information and Communications Technology; Paternity Challenges; Paternity Suggestions.</p>
BRAIN Journal-Brain-Like Artificial Intelligence for Automation-Figure 20. Overview about which Binding Mechanisms Work at what Hierarchical Levels and Development Stages of the Brain
<p>As a result of our research, in [60], a solution to the binding problem for perception was suggested by<br> combining the already existing binding hypotheses in a conclusive way, supplementing them with<br> other insights about the perceptual system of the brain, and translating them into a technically<br> implementable model. It was demonstrated via computational simulations that different binding<br> mechanisms proposed in literature are not mutually inclusive. On the contrary! At different<br> hierarchical levels and in different development stages, different binding mechanisms are acting in<br> perception. An overview about these circumstances is given in Figure 20. A detailed description can<br> be found in.</p>
BRAIN Journal-On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex-Figure 1. Working mechanism of the VOA.
<p>As it can be seen from the algorithm steps, the VOA employs simple equations. It is an<br> advantage that the algorithm can be formed and applied within optimization problems whereas<br> alternative algorithms may contain some complex solution steps (This situation may be also an<br> disadvantage for the VOA when it is applied in more difficult optimization problems but while the<br> world is transformed into a ‘strong simplicity’, the VOA may be a practical solution approach).<br> The working mechanism of the VOA can be visualized briefly as like in Figure 1.</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 5. Fiber artist Alexandra Rusu (NUA) working at a Roman vertical loom (video movie)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea Hasnaş. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages</p>
Data_MathyChekafCowan_JOC2018_Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression
<p>Original data files for the article Mathy, Fabien, Chekaf, Mustapha, & Cowan Nelson (2018). Simple and Complex Working Memory Tasks Allow Similar Benefits of Information Compression. Journal of Cognition.</p> <p>Abstract : Complex working memory span tasks were designed to engage multiple aspects of working memory and impose interleaved processing demands that limit the use of mnemonic strategies, such as chunking. Consequently, the average span is usually lower (4 ± 1 items) than in simple span tasks (7 ± 2 items). One possible reason for the higher span of simple span tasks is that participants can take advantage of the spare time to chunk multiple items together to form fewer independent units, approximating 4 ± 1 chunks. It follows that the respective spans of these two types of tasks could be equal (at around 4 ± 1) if stimulus lists exclusively used nonchunkable stimulus items. To manipulate the chunkability of the stimulus lists, our method involved a measure of their compressibility, i.e., the extent to which a pattern exists that can be detected and used as a basis of chunk formation. We predicted an interaction between the types of tasks and chunkability/compressibility, supporting a single higher span for the condition in which a simple span task was combined with chunkable items. The three other conditions were predicted to prevent chunking processes, either because the interleaved processing task did not allow any chunking process to occur or because the noncompressible material inherently limited the chunkability of information. The prediction that chunking is important solely in simple spans was not confirmed: Effects of information compression contributed to performance levels to a similar extent in both tasks according to a theoretically-based metric. This result suggests that i) complex span tasks might overestimate storage capacity in general, and ii) the difference between simple and complex span performance levels must rest in some mechanism other than prevention of a chunking strategy by the interleaved processing task in complex span tasks.</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 12. Diagram of the proposed working methodology
<p>The chart below (Figure 12) summarizes the workflow recommended for the implementation of a prototype of a 3D online campus.</p>
CoHERE Work Package 1 Museum Visitor Survey Interviews
<p>The CoHERE project seeks to identify, understand and valorise European heritages, engaging with their <strong>socio-political and cultural significance </strong>and their potential for developing <strong>communitarian identities</strong>. CoHERE addresses an intensifying EU Crisis through a study of relations between identities and representations and performances of history. </p> <p>The data was produced as part of CoHERE Work Package 1 - Productions and omissions of European heritage. This provides a critical foundation for CoHERE as a whole, interrogating different meanings of heritage, historical constructions and representations of Europe, formative histories for European identities that are neglected or hidden because of political circumstances, and non-official heritage.</p> <p>The interviews were carried out at selected museums, heritage sites and heritage events across Europe. The interview schedule posed questions about heritage consumption, identity, Europe and their experience of the site/event where the interview took place. Interviews have been transcribed and de-identified.</p>
Most popular scholarly works in the English Wikipedia and their transition to open access
<p>Following the release of "The future of OA" by Piwowar, Priem, Orr (2019), interest has grown on how to accelerate the share of scholarly works consultations which meet an open access record.</p> <p>Based on download patterns for over 23 million DOIs in 2017, released by Elbakyan (2018), we found that the 1 million most downloaded DOIs accounted for over 30 % of the total downloads. Of these 1 million DOIs, over 50 thousands (5 %) were previously identified as cited on the English Wikipedia and not open access (Leva 2018). Of these, 2440 DOIs are now open access according to the Unpaywall API as of 2019-10-25: a list of the corresponding OA URL and host type is enclosed, showing that 34 % became OA at the publisher while 66 % were made OA by a repository. The newly OA works were hosted at over 400 domains of which over 300 repositories, but the top 10 repositories accounted for a large portion of the works, with the top 3 repositories accounting for over 40 % of the newly found green open access DOIs.</p> <p>Part of the newly OA works were just false negatives in Unpaywall in 2018, but a small manual sample shows that most are truly new deposits. Works from 2017 can be expected to be over-represented in the sample given that they were probably the most popular downloads of 2017 and could have been under embargo in 2018 when the previous measure of open access status was made.</p>
Video: Setting up your own Wikibase reconciliation service (e.g. for OpenRefine) - Wikibase Working Hours, 07-11-2023
<p> Video tutorial on how to install a reconciliation service for your own Wikibase instance, how to connect it to OpenRefine and what settings in the Wikibase itself need to be checked to allow data from OpenRefine to be written to the Wikibase.</p> <p>Given during the <a title="d:Wikidata:WikiProject LD4 Wikidata Affinity Group/Wikibase Working Hours" href="https://www.wikidata.org/wiki/Wikidata:WikiProject_LD4_Wikidata_Affinity_Group/Wikibase_Working_Hours">Wikibase Working Hours</a> on 7 November 2023.</p> <p>Notes: <a href="https://docs.google.com/document/d/1MMvyaAe2l63MRZdkW-dg07oYLfXIly_SS3TBR_G8R2k/edit" rel="nofollow">https://docs.google.com/document/d/1MMvyaAe2l63MRZdkW-dg07oYLfXIly_SS3TBR_G8R2k/edit</a></p> <div> <p>Presentation shown in this video:</p> <ul> <li><a title="File:Setting up your own Wikibase reconciliation service (e.g. for OpenRefine) - Wikibase Working Hours, 07-11-2023.pdf" href="https://commons.wikimedia.org/wiki/File:Setting_up_your_own_Wikibase_reconciliation_service_(e.g._for_OpenRefine)_-_Wikibase_Working_Hours,_07-11-2023.pdf">Setting up your own Wikibase reconciliation service (e.g. for OpenRefine) - Wikibase Working Hours, 07-11-2023</a></li> <li><a title="Opens in new tab" href="https://doi.org/10.5281/zenodo.10078805" target="_blank" rel="noopener">10.5281/zenodo.10078805 </a></li> </ul> </div>
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.