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Patellar Tendon Load Progression during Rehabilitation Exercises: Implications for the Treatment of Patellar Tendon Injuries
<h3><strong>Purpose </strong></h3><p>To evaluate patellar tendon loading profiles (loading index, based on loading peak, loading impulse, and loading rate) of rehabilitation exercises to develop clinical guidelines to incrementally increase the rate and magnitude of patellar tendon loading during rehabilitation.</p><h3><strong>Methods </strong></h3><p>Twenty healthy adults (10 females/10 males, 25.9 ± 5.7 years) performed 35 rehabilitation exercises, including different variations of squats, lunge, jumps, hops, landings, running, and sports specific tasks. Kinematic and kinetic data were collected and a patellar tendon loading index was determined for each exercise using a weighted sum of loading peak, loading rate, and cumulative loading impulse. Then, the exercises were ranked, according to the loading index, into tier 1 (loading index≤0.33), tier 2 (0.33 < loading index<0.66), and tier 3 (loading index≥0.66).</p><h3><strong>Results </strong></h3><p>The single-leg decline squat showed the highest loading index (0.747). Other tier 3 exercises included single-leg forward hop (0.666), single-leg countermovement jump (0.711), and running cut (0.725). The Spanish squat was categorized as a tier 2 exercise (0.563), as was running (0.612), double-leg countermovement jump (0.610), single-leg drop vertical jump (0.599), single-leg full squat (0.580), double-leg drop vertical jump (0.563), lunge (0.471), double-leg full squat (0.428), single-leg 60° squat (0.411), and the Bulgarian squat (0.406). Tier 1 exercises included 20 cm step up (0.187), 20 cm step down (0.288), 30 cm step up (0.321), and double-leg 60° squat (0.224).</p><h3><strong>Conclusions </strong></h3><p>Three patellar tendon loading tiers were established based on a combination of loading peak, loading impulse, and loading rate. Clinicians may use these loading tiers as a guide to progressively increase patellar tendon loading during the rehabilitation of patients with patellar tendon disorders and after anterior cruciate ligament reconstruction using the bone patellar tendon bone graft.</p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients
<p><span>A Systematic Review and Meta-Analysis of Mindfulness-Based (Baduanjin) Exercise for the Rehabilitation of Stroke Patients</span></p>
Towards the use of UHPFRC in railway bridges: the rehabilitation of Buna Bridge
<p>Dataset of experiment carried out on Buna bridge, before rehabilitation. Accelerations corresponding to a roving test, with the positions and orientations specified ont the pdf (pass1 vertical, pass 2 vertical, pass1 horizontal, pass2 horizontal. The structure was excited with a shaker in horizontal (confH) and vertical (confV) positions. </p>
REHAB24-6: A multi-modal dataset of physical rehabilitation exercises
<p>To enable the evaluation of HPE models and the development of exercise feedback systems, we produced a new rehabilitation dataset (REHAB24-6). The main focus is on a diverse range of exercises, views, body heights, lighting conditions, and exercise mistakes. With the publicly available RGB videos, skeleton sequences, repetition segmentation, and exercise correctness labels, this dataset offers the most comprehensive testbed for exercise-correctness-related tasks.</p> <h2>Contents</h2> <ul> <li>65 recordings (184,825 frames, 30 FPS): <ul> <li>RGB videos from two cameras (<code>videos.zip</code>, horizontal = Camera17, vertical = Camera18);</li> <li>3D and 2D projected positions of 41 motion capture marker (<code><2/3>d_markers.zip</code>, marker labels in <code>marker_names.txt</code>);</li> <li>3D and 2D projected positions of 26 skeleton joints (<code><2/3>d_joints.zip</code>, joint labels in <code>joint_names.txt</code>);</li> </ul> </li> <li>Annotation of 1,072 exercise repetitions (<code>Segmentation.csv</code>, indexed based <strong>only on</strong> 30 FPS data, described in <code>Segmentation.txt</code>): <ul> <li>Temporal segmentation (start/end frame, most between 2–5 seconds);</li> <li>Binary correctness label (around 90 from each category in each exercise, except Ex3 with around 50);</li> <li>Exercise direction (around 90 from each direction in each exercise);</li> <li>Lighting conditions label.</li> </ul> </li> </ul> <h2>Recording Conditions</h2> <p>Our laboratory setup included 18 synchronized sensors (2 RGB video cameras, 16 ultra-wide motion capture cameras) spread around an 8.2 × 7 m room. The RGB cameras were located in the corners of the room, one in a horizontal position (hor.), providing a larger field of view (FoV), and one in a vertical (ver.), resulting in a narrower FoV. Both types of cameras were synchronized with a sampling frequency of 30 frames per second (FPS).</p> <p>The subjects wore motion capture body suits with 41 markers attached to them, which were detected by optical cameras. The OptiTrack Motive 2.3.0 software inferred the 3D positions of the markers in virtual centimeters and converted them into a skeleton with 26 joints, forming our human pose 3D ground truth (GT).</p> <p>To acquire a 2D version of the ground truth in pixel coordinates, we applied a projection of the virtual coordinates into the camera using the simplified pinhole model. We estimated the parameters for this projection as follows. First, the virtual position of the cameras was estimated using measuring tape and knowledge of the virtual origin. Then, the orientation of the cameras was optimized by matching the virtual marker positions with their position in the videos.</p> <p>We also simulated changes in lighting conditions: a few videos were shot in the natural evening light, which resulted in worse visibility, while the rest were under artificial lighting.</p> <h2>Exercises</h2> <p>10 subjects participated in our recording and consented to release the data publicly: 6 males and 4 females of different ages (from 25 to 50) and fitness levels. A physiotherapist instructed the subjects on how to perform the exercises so that at least five repetitions were done in what he deemed the correct way and five more incorrectly. The participants had a certain degree of freedom, e.g., in which leg they used in Ex4 and Ex5. Similarly, the physiotherapist suggested different exercise mistakes for each subject.</p> <ul> <li><strong>Ex1 = Arm abduction</strong>: sideway raising of the straightened right arm;</li> <li><strong>Ex2 = Arm VW</strong>: fluent transition of arms between V (arms straight up) and W (elbows down, hands up) shape;</li> <li><strong>Ex3 = Push-ups</strong>: push-ups with hands on a table;</li> <li><strong>Ex4 = Leg abduction</strong>: sideway raising of the straightened leg;</li> <li><strong>Ex5 = Leg lunge</strong>: pushing a knee of the back leg down while keeping a right angle on the front knee;</li> <li><strong>Ex6 = Squats</strong>.</li> </ul> <p>Every exercise was also executed in two directions, resulting in different views of the subject depending on the camera. Facing the horizontal camera resulted in a front view for that camera and a profile from the other. Facing the wall between the cameras shows the subject from half-profile in both cameras. A rare direction, only used for push-ups due to the use of the table, was facing the vertical camera, with the views being reversed compared to the first orientation.</p> <h2>Citation</h2> <p>Cite the related conference paper:</p> <p>Černek, A., Sedmidubsky, J., Budikova P.: REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods. 17th International Conference on Similarity Search and Applications (SISAP). Springer, 14 pages, 2024.</p> <h2>License</h2> <p>This dataset is for academic or non-profit organization noncomercial research use only. By using you agree to appropriately reference the paper above in any publication making of its use. For comercial purposes contact us at info@visioncraft.ai</p>
Publication rate and consistency of registered trials of motor-based stroke rehabilitation
<p><strong>Table e1 - Eligible records</strong></p> <p>A list of registered randomized controlled trials (RCTs) meeting the following criteria:</p> <ol> <li>RCTs of motor-based interventions in individuals with stroke (including transient ischemic attack);</li> <li>Started on or after 1 July 2005;</li> <li>Completed before 1 April 2017 (actual or expected end date);</li> <li>Included human adult participants (≥18 years old);</li> <li>Included at least one outcome (primary or secondary) related to motor control, mobility, or physical functioning and performance of upper and/or lower extremities or the body as a whole; and</li> <li>Registered in English.</li> </ol> <p>This list was obtained by searching the following registries between 23 November 2017 and 22 February 2018: the International Clinical Trials Registry Platform, Clinicaltrials.gov (USA), Australian New Zealand Clinical Trial Registry, Chinese Clinical Trial Registry, Clinical Research Information Service (Republic of Korea), Clinical Trial Registry of India, Cuban Public Registry of Clinical Trials, European Union Clinical Trials Register, German Clinical Trials Register, Iranian Registry of Clinical Trials, International Standard Randomised Controlled Trials Number registry (UK), Center for Clinical Trials-Japan Medical Association, University Hospital Medical Information Network-Clinical Trial Registry (Japan), Thai Clinical Trials Registry, Netherlands Trials Registry, Pan African Clinical Trials Registry, Peruvian Clinical Trials Registry, and Sri Lanka Clinical Trials Registry. </p> <p><em>Variable definitions</em></p> <p>UIN: Unified identification number (obtained from the trial registry)</p> <p>Status: whether or not a peer-reviewed publication reporting the trial findings for the primary outcome/outcome was found</p> <ul> <li>Paper available: a publication reporting the trial findings for the primary outcome/objective was found</li> <li>Paper available (not English): a publication, published in a language other than English, reporting the trial findings for the primary outcome/objective was found</li> <li>Secondary paper available: a publication reporting study findings is available, but not for the primary objectives/outcome</li> <li>Results available: trial findings for the primary objective/outcome are available in a non-peer reviewed format (e.g., conference publication, non-peer reviewed journal, or uploaded to the trial registry)</li> <li>Discontinued: the trial registry record indicates that the trial was discontinued, so no publication is expected</li> <li>No paper/results: none of the above apply</li> </ul> <p>Source: database or method we used to find the publication reporting the trial findings for the primary objective/outcome</p> <ul> <li>Pubmed: the publication was found by searching for the UIN in Pubmed</li> <li>EMBASE/OVID: the publication was found by searching for the UIN in Embase or OVID Medline</li> <li>Google Scholar: the publication was found by searching for the UIN in Google Scholar</li> <li>Registry: the publication was listed in the trial registry</li> <li>Internet: the publication was found by a superficial internet search for the UIN</li> <li>Protocol: the publication was found through a cited reference search for the published protocol</li> <li>Author: the publication was found by contacting the trial investigators</li> <li>Other: the publication was found by some other means</li> <li>None: no publication reporting the trial findings for the primary objective/outcome was found</li> </ul> <p><strong>Table e2 - Published papers consistency</strong></p> <p>The subset of trials from Table e1 where an English-language publication reporting the trial findings for the primary objective/outcome was found.</p> <p><em>Variable definitions</em></p> <p>UIN: Unified identification number (obtained from the trial registry)</p> <p>DOI: digital objective identifier of the publication</p> <p>First_author: last name of the first author of the publication</p> <p>Year: year of the publication</p> <p>Journal: journal of the publication (abbreviated journal names are used, where available)</p> <p>Reg_Trial_End_Date: end date of the trial, as stated in the trial registry (format DD-MMM-YY)</p> <p>Date_Submitted: date when the paper was submitted to the journal for publication (where available; format: DD-MMM-YY)</p> <p>Time_To_Submit: difference, in days, between Reg_Trial_End_Date and Date_Submitted</p> <p>Date_Published: date when the paper was published, either online or in print, whichever is earlier (format: DD-MMM-YY)</p> <p>Time_To_Publish: difference, in days, between Reg_Trial_End_Date and Time_To_Publish</p> <p>UIN_Paper_Location: location of the UIN in the publication</p> <p>Reg_Pilot: whether the trial was defined as a pilot or feasibility study in the registry record (0=no, 1=yes)</p> <p>Paper_Pilot: whether the trial was defined as a pilot or feasibility study in the publication (0=no, 1=yes)</p> <p>Consistency_pilot: whether Reg_Pilot = Paper_Pilot (0=no, 1=yes)</p> <p>Consistency_Primary_Objective: whether the trial registry record and publication were consistent in terms of the primary objective (0=no, 1=yes)</p> <p>Consistency_Primary_Outcome: whether the trial registry record and publication were consistent in terms of the primary outcome (0=no, 1=yes)</p> <p>Target_N: target sample size, as indicated in the trial registry record</p> <p>Paper_N: number of participants recruited to the study, as indicated in the publication</p> <p>Consistency_N: whether the trial registry record and publication were consistent in terms of the sample size (i.e., Paper_N is within +/-10% of Target_N; 0=no, 1=yes)</p> <p>N_direction: for those trials that were inconsistent in terms of the sample size, whether Paper_N was less than (Under) or more then (Over) Target_N</p> <p>Eligibility_Consistency: whether the trial registry record and publication were consistent in terms of the eligibility criteria</p> <p>UIN_in_paper: the UIN that was included in the paper, exactly as it was published</p> <p>UIN_consistency: whether UIN = UIN_in_paper (0=no, 1=yes)</p> <p>Reg_Type: whether the trial was registered before recruiting the first participant (Prospective), after recruiting the first participant but before the trial was completed (Retro - pre-completion), or after the trial was completed (Retro - post-completion)</p> <p><strong>Table e3 - Inconsistency details</strong></p> <p>The subset of trials from Table e2 where the trial registry record and publication were inconsistent on only one of the criteria examined. This table provides further details of these inconsistencies, and details of any explanations for changes to the protocol since registration, if available.</p>
Dataset for Monitoring and Visualizing Stroke Rehabilitation Progress using Wearable Sensors (IMU)
<div> <p>This dataset is associated with a manuscript that is currently under peer review.</p> <p> </p> <p>Article Abstract:</p> <p>Stroke is one of the leading causes of death and disability worldwide, and recovering mobility is an important goal during post-stroke rehabilitation. In this work, we present a study to verify the feasibility of monitoring and visualizing longitudinal stroke gait rehabilitation progress using wearable sensors. Wearable devices such as inertial measurement units (IMUs) are easy-to-use and cost-effective tools for quantifying mobility. However, there is a need for research on longitudinal monitoring of stroke rehabilitation progress with wearables, as well as generating clinically relevant insights using appropriate visualizations. To this aim, we recruited ten stroke patients in their early rehabilitation stage. We collected and analyzed the IMU-derived gait features across two visits, and presented visualizations of the foot movement trajectories as well as the spatio-temporal gait parameters in the average, symmetry, and variation domains to quantify changes in gait. Our visualization and quantification methods are evaluated and validated by clinical experts, and prove to be promising in aiding clinicians to monitor rehabilitation progression.</p> <p> </p> <p>Data description:</p> <p>The dataset consists data from ten stroke patients who completed both visits. The "raw" data folder contains tri-axial acceleration and angular velocity data from the IMUs. In addition, information about the participants such as demographics (e.g., body height and body weight), FAC scores at both visits, and evaluations of gait improvement are documented in the file "participant_info.csv".</p> <p>The “interim” folder contains IMU data that has been manually segmented to remove irrelevant movements before and after each walking session during a visit, based on visual inspection of raw IMU signals. For quality control, the segmented accelerometer and gyroscope data of each sensor were plotted, and the plots were saved in the same folder as the IMU signals. In addition, during the first execution of gait parameter extraction, calculated 3D feet trajectories were cached in the "interim" folder, so that for future executions, the cached trajectories can be loaded directly, reducing the computational efforts for re-calculation. The file "stance_magnitude_thresholds_manual.csv" documents the angular velocity thresholds used to identify stance phases for the gait analysis algorithm for each participant. The threshold values were determined manually by observing the angular velocity signals. </p> <p>The “processed” folder contains stride-by-stride spatio-temporal gait parameters extracted for each of the four walking conditions, and aggregated gait parameters in terms of coefficients of variation and symmetry for all walking conditions for each participant. </p> <p> </p> </div>
A Dataset for Engagement Prediction in Neuromotor Disorder Patients during Rehabilitation
<p>This dataset is related to the work titled "Artificial Intelligence Tools for Engagement Prediction in Neuromotor Disorder Patients undergoing Robot-Assisted Rehabilitation," whose aim is to methodologically explore the performance of artificial intelligence algorithms applied to structured datasets made of heart rate variability (HRV) and electrodermal activity (EDA) features to predict the level of patient engagement during robot-assisted gait rehabilitation (RAGR).</p> <p>It is composed of three Excel files related to the 3-minute windows data augmentation scenario applied to the bimodal dataset made of 14 HRV and 19 EDA features. Specifically:</p> <ul> <li>ds_bimodal_win_3min.xlsx contains the features extracted from 3-minute windows of HRV and EDA signals, recorded during the RAGR activity, and normalized with respect to the reference (baseline) signals. Features are not z-scored.</li> <li>labels_self_win_3min contains one single column with, for each row, the label related to the self-perceived engagement classification target. </li> <li>labels_therapist_win_3min contains one single column with, for each row, the label related to the therapist-perceived engagement classification target.</li> </ul> <p>The coding of classes for both classification targets is:</p> <ul> <li>0: Underchallenged</li> <li>1: Minimally Challenged</li> <li>2: Challenged</li> </ul>
Early Virtual-Reality-Based Home Rehabilitation after Total Hip Arthroplasty: A Randomized Controlled Trial
<p>The benefits of early virtual-reality-based home rehabilitation following total hip arthroplasty (THA) have not yet been assessed. The aim of this randomized controlled study was to compare the efficacy of early rehabilitation via the Virtual Reality Rehabilitation System (VRRS) versus traditional rehabilitation in improving functional outcomes after THA. Subjects were randomized either to an experimental (VRRS; n = 21) or a control group (control; n = 22). All participants were invited to perform a daily home exercise program for rehabilitation after THA with different administration methods—namely, an illustrated booklet for the control group and a tablet with wearable sensors for the VRRS group. The primary outcome was the hip disability (HOOS JR). Secondary outcomes were the level of independence and the degree of global perceived effect of the rehabilitation program (GPE). Outcomes were measured before surgery (T0) and at the 4th (T1), 7th (T2), and 15th (T3) day after surgery. Mixed-model ANOVA showed no significant group effect but a significant effect of time for all variables (<em>p </em>< 0.001); no differences were observed in HOOS JR between VRRS and the control at T0, T1, T2, or T3. Further, no differences in the level of independence were found between VRRS and the control, whereas the GPE was higher at T3 in VRSS compared to the control (4.76 ± 0.43 vs. 3.96 ± 0.65; <em>p </em>< 0.001). Virtual-reality-based home rehabilitation resulted in similar improvements in functional outcomes with a better GPE compared to the traditional rehabilitation program following THA. The application of new technologies could offer novel possibilities for service delivery in rehabilitation.</p>
Dataset from Experimental and Nonlinear Finite Element Modeling Investigating an Innovative Buckling Restrained Bracing System for Rehabilitation of Seismic Deficient Structures
<p>The data presented in this paper were collected experimentally and modeled using the finite element method. A total of six BRBs (i.e., duplicates of three types of BRB core bars) specimens were tested experimentally and verified numerically using the finite element method employing the commercial Software ABAQUS. Specific labeling was used to designate each BRB type. Three core bars were used in the tested BRBs: fully-threaded, threaded-notched, and smooth-shaved. The specimens are labeled according to their core bar type and diameter. i.e., BRB-12-Th stands for a full threaded core bar diameter of 12 mm, the threaded notched type was labeled BRB-12-Th-Nd, and the smooth shave one was labeled BRB-12-Sh.</p> <p>Further details of the tested BRBs are included in the excel file called dimensions and properties of BRBs. The worksheet provides details of the BRB components (i.e., core bar, restraining unit, and innovative end units). The dimensions and strength of the materials were obtained from coupon tests. The experimental data are presented in the second excel file labeled hysteresis with three embedded worksheets, one for each type of BRB. The excel sheets provide the cyclic loading data and plots showing the hysteresis behavior of tested BRBs. A sample of the loading protocol included in the second excel file is presented in Fig.1. The third excel file presents the analytical data extracted from experimental data that has two sheets: stiffness and energy dissipation. The sheet labeled stiffness has the secant stiffness versus deformation plot for the push-pull cycles (compression-tension). The second sheet labeled energy dissipation shows the cumulative energy dissipated.</p>
Fig 2 in Bivalve Spat (Anadara granosa) recruitment in rehabilitation mangrove ecosystem of Rangsang Island, Riau province
Fig 2: Distribution of Mangrove Density in the Mangrove Ecosystem Rehabilitation of Rangsang Island, Riau
MOTU data. FHIR-standardized data collection on the clinical rehabilitation pathway of trans-femoral amputation patients.
<h3>Dataset presented in the article "MOTU on FHIR: A 10-year data collection on the clinical rehabilitation pathway of 1006 trans-femoral amputees".</h3> <p>Data has been anonymised prior the publication. The data are in Comma-separated-values (CSV) format. </p> <p>This work has been conducted within the framework of the MOTU++ project (PR19-PAI-P2).</p> <p><span>This research was co-funded by the Complementary National Plan PNC-I.1 "Research initiatives for innovative technologies and pathways in the health and welfare sector” D.D. 931 of 06/06/2022, DARE - DigitAl lifelong pRevEntion initiative, code PNC0000002, CUP: (B53C22006450001) and by the Italian National Institute for Insurance against Accidents at Work (INAIL) within the MOTU++ project (PR19-PAI-P2). </span></p> <p><span>Authors express their gratitude to all the AlmaHealthDB Team.</span></p>
Fig 4 in Bivalve Spat (Anadara granosa) recruitment in rehabilitation mangrove ecosystem of Rangsang Island, Riau province
Fig 4: Relationship between Spat Abundance of Bivalve A. granosa and Mangrove Density in Rangsang Island, Riau (A) November 2020 (B) December 2020 (C) January 2021
COMMENTS.— Although not breeding in the Mediterranean, the species forages in Libyan waters (van Dijk et al. 2014). In addition to the single beached record, an individual was pulled from nearshore waters of the Tajura coast in 1996 and died in the rehabilitation facility of the Marine Biology Research Centre (MBRC) at Tajura, where it was subsequently taxidermied at the MBRC Museum (Hamza 2010). Capra's (1949) records were based on a report in "L'Idea Coloniale" for 2 May 1927 (Mongàr) and an unspecified specimen in the Museo Civico di Storia Naturale di Trieste (Sella). IUCN THREAT STATUS.— Vulnerable A2bd. MAP 3. Distribution of Dermochelys coriacea in Libya showing stranding site records. in Atlas of the Reptiles of Libya
COMMENTS.— Although not breeding in the Mediterranean, the species forages in Libyan waters (van Dijk et al. 2014). In addition to the single beached record, an individual was pulled from nearshore waters of the Tajura coast in 1996 and died in the rehabilitation facility of the Marine Biology Research Centre (MBRC) at Tajura, where it was subsequently taxidermied at the MBRC Museum (Hamza 2010). Capra's (1949) records were based on a report in "L'Idea Coloniale" for 2 May 1927 (Mongàr) and an unspecified specimen in the Museo Civico di Storia Naturale di Trieste (Sella). IUCN THREAT STATUS.— Vulnerable A2bd. MAP 3. Distribution of Dermochelys coriacea in Libya showing stranding site records.
Fig. 4 in Molecular prevalence and phylogenetic relationship of Haemoproteus and Plasmodium parasites of owls in Thailand: Data from a rehabilitation centre
Fig. 4. Colour heatmap of pairwise genetic distances estimated from nucleotide sequences of the cytochrome b gene (479 bp) of Haemoproteus spp. based on the Jukes-Canter model. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Molecular prevalence and phylogenetic relationship of Haemoproteus and Plasmodium parasites of owls in Thailand: Data from a rehabilitation centre
Fig. 3. Bayesian phylogeny based on partial cytochrome b gene (479 base pairs) of Haemoproteus species lineages. The lineages reported in this study are given in bold. MalAvi lineage codes and GenBank accession numbers are given after species names. Node values (in percentages) indicate posterior clade probabilities. Vertical bars indicate clades of Haemoproteus subgenus (A), Parahaemoproteus (B). Almost all of the Parahaemoproteus lineages recovered from owls were grouped together (clade B-1, grey box). * indicates lineages infecting Strigiformes.
Fig. 5 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 5. Heatmap of pairwise genetic distances estimated from nucleotide sequences of the cytochrome b gene (479 nucleotides) of Plasmodium spp. using the JukesCanter model.
Fig. 4 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 4. Bayesian phylogeny based on the partial cytochrome b gene (479 base pairs) of Plasmodium lineages. The lineages isolated in this study are given in red bold. MalAvi lineage codes and GenBank accession numbers are given after species names. Node values indicate percentages of posterior probabilities. Plasmodium isolated from this study are clustered into three clades (clade I, II and II). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
Fig. 6 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 6. Haemoproteus spp. infected in Blyth's hawk-eagles (Spizaetus alboniger), KU549 (A-C) and KU589 (D-F). Young gametocytes (A&D), microgametocytes (B&E) and macrogametocytes (C&F). Giemsa staining.
Fig. 2 in Prevalence and genetic diversity of Haemoproteus and Plasmodium in raptors from Thailand: Data from rehabilitation center
Fig. 2. Bayesian phylogeny based on partial cytochrome b gene (479 nucleotides) of Haemoproteus lineages. The lineages isolated in this study are given in red bold. MalAvi lineage codes and GenBank accession numbers are given after species names. Node values indicate percentages of posterior probabilities. Vertical bars indicate clades of subgenus Haemoproteus (A) and Parahaemoproteus (B) Haemoproteus isolated from this study are clustered into two clades (clade I and II). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)
ScienceDex guides
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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.