Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
512
datasets available to search
ShareScore release 0.9.0
Dataset results
512 results for “Activity pattern”
Fig. 1 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 1. Mean activity density of Carabus scheidleri (a) and C. coriaceus (b) per sampling plot in different for- estry treatments (C = control, CC = clear-cutting, P = preparation cut- ting) between 2014 and 2018. Verti- cal lines represent a 95% confidence interval and capital letters above bars indicate significant differences based on Tukey's multiple compari- sons of means
Fig. 2 in Individual Movement Of Large Carabids As A Link For Activity Density Patterns In Various Forestry Treatments
Fig. 2. Movements of Carabus scheidleri (a) within and between forestry treatments (C = control, CC = clear-cutting, P = preparation cutting) based on CMR. The number next to the arrow corresponds with the number of recorded movements. Individual trajectories of radio-tracked C. coriaceus (b) in the experimental area, black dots represent the first release point for each trajectory
Additional data; Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities
<p>This dataset contains additional data for the research article <em>Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities</em> by Sruthi Sreekumar, Jasper Wattjes, Anna Niehues, Tamara Mengoni, Ana C. Mendes, Edwin R. Morris, Francisco M. Goycoolea, and Bruno M. Moerschbacher.</p> <p>The directory <em>Fig1ab_SuppFig1ab</em> contains data, code, and results of enzymatic mass-spectrometric fingerprinting experiments. The directory <em>cosms</em> contains source code and instructions to create a conda environment in which the code to create Figures 1 A+B and Supplementary Figures 1 A+B can be executed.</p> <p>The directory <em>Fig23456_SuppFig_2567</em> contains Excel Worksheets (.xlsx files) with data underlying Figures 2, 3, 4, 5 and 6, and Supplementary Figures 2, 5, 6 and 7.</p> <p>The directories <em>Fig5b</em> and <em>SuppFig7d</em> contain raw MS data (Bruker .d files) of oligomeric hydrolysis products produced by incubating different chitosans with different chitinolytic enzymes. </p>
Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults
<p>There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age.</p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with one row for each gait cycle and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.</p>
Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion
<p>The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.</p> <p> </p> <p>In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG; e) the muscle synergies extracted via NMF; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set.</p> <p>The file “metadata.dat” is available in ASCII and RData format and contains:</p> <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> <p>The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p><strong>Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a></strong></p> <p>The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20<sup>th</sup>), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively.</p> <p>The files containing the raw, filtered, and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with 30000 rows (one for each recorded data point) and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3<sup>rd</sup>), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”.</p> <p>The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules M), one for each synergy and for each muscle. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives P), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12<sup>th</sup>), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.</p> <p>All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.</p>
No relationship between chronotype and timing of breeding when variation in daily activity patterns across the breeding season is taken into account
<p>There is increasing evidence that individuals are consistent in the timing of their daily activities, and that individual variation in temporal behaviour is related to the timing of reproduction. However, it remains unclear whether observed patterns relate to the timing of the onset of activity or whether an early onset of activity extends the time that is available for foraging. This may then again facilitate reproduction. Furthermore, the timing of activity onset and offset may vary across the breeding season, which may complicate studying the above mentioned relationships. Here, we examined in a wild population of great tits (Parus major) whether an early clutch initiation date may be related to an early onset of activity and/or to longer active daylengths. We also investigated how these parameters are affected by the date of measurement. In order to test these hypotheses we measured emergence and entry time from/into the nest box as proxies for activity onset and offset in females during the egg laying phase. We then determined active daylength. Both emergence time and active daylength were related to clutch initiation date. However, a more detailed analysis showed that the timing of activities with respect to sunrise and sunset varied throughout the breeding season both within and among individuals. The observed positive relationships are hence potentially statistical artifacts. After methodologically correcting for this date effect, by using data from the pre-egg laying phase, where all individuals were measured on the same days, neither of the relationships remained significant. Taking methodological pitfalls and temporal variation into account may hence be crucial for understanding the significance of chronotypes.</p>
Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012). in Lithofacies And Ichnofacies Of Turbidite Deposits, West Java, Indonesia
Text-fig. 6. Shallowing pattern during the Middle Miocene to Late Miocene/Pliocene due to increasing magmatic activity as an external parameter. a: palaeobathymetry map during the Middle Miocene to Pliocene; b: sea level change curve indicating a shallowing pattern; c: relative changes of sea level and magmatic activity curve (Haq et al. 1987, Soeria-Atmadja et al. 1998, Muljana 2012).
Figure 3 in The impact of predation by the myrmecophagous spider Zodarion elegans (Araneae: Zodariidae) on the activity pattern of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 3 Foraging activity of ants leaving the nest in correlation with absence/presence of Z. elegans individuals. Activity per day was measured as the number of leaving plus returning worker ants per min in a half-hour intervals; counts were summarized per day (0 = absence of Z. elegans, 1 = presence of Z. elegans).
Figure 5 in The impact of predation by the myrmecophagous spider Zodarion elegans (Araneae: Zodariidae) on the activity pattern of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 5. Tasks performed by marked foragers inside the nest when nest entrances were closed due to predation pressure. The number of workers per activity was summarized during the duration of three perturbation experiments. Per experiment 30 workers – 10 Minor-workers, 10 Medium-workers and 10 Major-workers – were marked.
Figure. 4 in The impact of predation by the myrmecophagous spider Zodarion elegans (Araneae: Zodariidae) on the activity pattern of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure. 4 Foraging activity of ants returning to the nest in correlation with absence/presence of Z. elegans individuals. Scores were taken during half-hour intervals (0 = absence of Z. elegans, 1 = presence of Z. elegans).
Figure 1 in The impact of predation by the myrmecophagous spider Zodarion elegans (Araneae: Zodariidae) on the activity pattern of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 1. Frequency of Z. elegans individuals found within a distance of max. 0.5 m to active/inactive ant colonies in a) spring 2009, b) summer 2009 and c) autumn 2009 (1 = active colonies, 0 = inactive colonies).
Figure 2 in The impact of predation by the myrmecophagous spider Zodarion elegans (Araneae: Zodariidae) on the activity pattern of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 2 Flow diagram of nest entrance closure in response to prey capture by the spider Z. elegans. In total, four experiments were performed with an overall duration of 100 days (Trial 1: n = 20, Trial 2: n = 30, Trial 3: n = 30, Trial 4: n = 20, n(total) = 100). The capture of M. wasmanni workers by Z. elegans is necessary to prompt ants to end aboveground activity and close nest entrances. By contrast, the presence of spiders in the formicarium alone was not sufficient to prompt ants to close nest entrances after aboveground foraging activity ceased.
Fig. 4 in Diel flight activity patterns of the red palm weevil (Coleoptera: Curculionidae) as monitored by smart traps
Fig. 4. Diel pattern for the mean total number of red palm weevils (RPW) captured per trap during the 62 d trapping period. Columns headed by the same letter are not significantly different from each other.
Fig. 3 in Diel flight activity patterns of the red palm weevil (Coleoptera: Curculionidae) as monitored by smart traps
Fig. 3. Mean temporal distributions of adult male (A), female (B), and total (C) red palm weevils captured in STs over the 24 h diel cycle. Each circle represents 4, 16, and 19 weevils in A, B, and C, respectively.
Extinction Rebellion Finland (Elokapina) Post-Repression Twitter Activity and Network Patterns
<p><strong>Overview</strong></p> <p>This repository contains two time series data sets of activity levels and network patterns on Finnish climate Twitter for the period of July 18, 2020 to February 11, 2022. First, there is a day-level data set (574 days) containing Twitter activity levels of climate activists and non-activists. Second, there is a week-level data set (81 weeks) containing estimated coefficients for various network effects from exponential random graph models (ERGM) fit to retweet networks.</p> <p>For details, including definitions of activist/non-activist and the ERGM specifications, please see the referenced work: "Social Media Affordances Sustain Social Movements Facing Repression: Evidence from Climate Activism", detailed below.</p> <p> </p> <p><strong>Data Set Details</strong></p> <p>`raw_daily_counts.csv` contains the following variables at the day level.</p> <ul> <li><em>date</em>: from 2020-07-25 to 2022-02-11</li> <li><em>activity_activist</em>: the daily activity count of activist users on Finnish climate Twitter</li> <li><em>activity_nonactivist</em>: the daily activity count of non-activist users on Finnish climate Twitter</li> <li><em>users_activist</em>: the daily count of activist users on Finnish climate Twitter</li> <li><em>users_nonactivist</em>: the daily count of non-activist users on Finnish climate Twitter</li> </ul> <p>`weekly_ERGM_coefficients.csv` contains a subset of estimated coefficients from ERGMs of weekly retweet networks from Finnish climate Twitter. The data set contains the following variables (coefficients), not all of which are from the same model specification.</p> <ul> <li><em>week</em>: starting with week 2 (2020-07-25 to 2020-07-31) and ending with week 82 (2022-02-05 to 2022-02-11)</li> <li><em>political_activity_activist</em>: activists retweeting politically relevant users</li> <li><em>political_activity_nonactivist</em>: non-activists retweeting politically relevant users</li> <li><em>gwidegree0_activist</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork</li> <li><em>gwidegree0_nonactivist</em>: geometrically weighted in-degree (decay = 0) for the non-activist subnetwork</li> <li><em>gwidegree0_activistsub_thresh3</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh4</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh5</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh6</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh7</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh8</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> <li><em>gwidegree0_activistsub_thresh9</em>: geometrically weighted in-degree (decay = 0) for the activist subnetwork with an activity level requirement for activists</li> </ul> <p> </p> <p><strong>References</strong></p> <p>Please reference the original study when using this data set.<br>Savolainen, Sonja, Ville P. Saarinen, and Ted Hsuan Yun Chen. 2024. “Social Media Affordances Sustain Social Movements Facing Repression: Evidence from Climate Activism.” <a href="https://doi.org/10.31235/osf.io/p4yvk" target="_blank" rel="noopener">doi:10.31235/osf.io/p4yvk</a>.</p>
Fig. 3 in Circadian activity patterns of the Red fox (Vulpes vulpes) and the Stone marten (Martes foina) in agricultural landscape of Northwestern Bulgaria during autumn-winter period
Fig. 3. Stone marten (Martes foina) and Red fox (Vulpes vulpes) daily activity patterns in protected area "Zlatiyata", Northwestern Bulgaria.
Fig. 2 in Circadian activity patterns of the Red fox (Vulpes vulpes) and the Stone marten (Martes foina) in agricultural landscape of Northwestern Bulgaria during autumn-winter period
Fig. 2. Stone marten, Martes foina (left) and Red fox, Vulpes vulpes (right) captured in protected area "Zlatiyata", Northwestern Bulgaria.
Fig.1 in Home range, movements and activity patterns of an exceptionally large male Brown Bear (Ursus arctos L.) in the area of the Bulgarian-Greek border (Western Rhodope Mts.)
Fig.1. Dominant male (supposed age of at least 15 years old) with scars of battles with rivals. Hunting Forestry Adjilarska (photo from a kamera-trap, D. Bukovsky, 17.03.2014 at a game-feeding station).
Fig 2. A in Home range, movements and activity patterns of an exceptionally large male Brown Bear (Ursus arctos L.) in the area of the Bulgarian-Greek border (Western Rhodope Mts.)
Fig 2. A century-old bear marking tree just on the Bulgarian/Greek border. The tree is marked also with white paint as a border pillar (photo N. Spassov).
Figure2. Generation of negative feedbacks gets tuned once a TCR completes stimulation beyond the threshold l. A TCell generates activation signal to BCell once it gets stimulation of its k-TCRs.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
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.