Find research datasets worth reusing

opencc-by-4.0Jul 2016View details →

East-African Social Sciences and Humanities Journals active in 2008-2009

Research Data of &bdquo;East-African Social Sciences and Humanities Publishing: A Handmade Bibliometrics Approach“, Proceedings of the 21st International Conference on Science and Technology Indicators, València (Spain), September 14-16, 2016. Corrected second version: due to an error, the journal “Chemchemi” was not included in “1 final list merged” and “7 ceased-established”. However, this does not change the conclusions drawn in the paper. Abstract of the paper to be published in the proceedings: For Eastern Africa, very little information about the SSH knowledge production can be found from a European perspective. Adequate indicators like information-rich bibliographic databases that cover East-Africa-based journals and book publishers are lacking. This research in progress explores their indexing situation in detail, their development, which is closely connected to political history, their (non-)usage, and affiliations as well as career-stages of their authors. Furthermore, it also pays attention to East-Africa-based SSH researchers who use other publication venues. Any bibliometric analysis in this field needs to rely on manual data collection, otherwise it would be heavily biased. This study lays out the foundation for citation analyses, qualitative research on the publications' content and the self-description of East-African scholars against the background of an academic environment that is often described as “international”.

opencc-by-4.0Dec 2023View details →

Minute-Level Human Activity and Particulate Matter Exposure Dataset from Ljubljana, Slovenia

This dataset encompasses detailed measurements of human activities and particulate matter exposure at a minute-level resolution, collected in Ljubljana, Slovenia from September 24 to October 31, 2020. Data was gathered from 18 participants using a combination of devices: a personal particulate matter monitor (PPM), a Garmin Vivosmart 3 smart activity tracker (SAT), and the Clockify app. The PPM provided real-time measurements of particulate matter concentrations (PM1, PM2.5, and PM10), as well as environmental parameters like temperature, humidity, and altitude. The SAT tracker offered insights into personal health data, including average heart rate and metabolic equivalent of task (MET). Clockify app was utilized for detailed logging of various activities categorized with minute accuracy. Key variables included in the dataset are: <ol> <li>Participant ID</li> <li>Date of data collection</li> <li>Time of data recording (minute accuracy)</li> <li>Specific task or activity performed</li> <li>Particulate matter - PM1 - concentrations</li> <li>Particulate matter - PM2.5 - concentrations</li> <li>Particulate matter - PM10 - concentrations</li> <li>Environmental temperature</li> <li>Relative humidity</li> <li>Altitude</li> <li>Speed</li> <li>Average heart rate</li> <li>Metabolic equivalent of task</li> </ol> This dataset offers a resource for exploring the interplay between individual behaviors and air pollution exposure, with applications in environmental health research and the development of machine learning models for activity recognition.

opencc-zeroDec 2023View details →

Genomic footprints of (pre) colonialism: Population declines in urban and forest túngara frogs coincident with historical human activity

Urbanisation is rapidly altering ecosystems, leading to profound biodiversity loss. To mitigate these effects, we need a better understanding of how urbanisation impacts dispersal and reproduction. Two contrasting population demographic models have been proposed that predict that urbanisation either promotes (facilitation model) or constrains (fragmentation model) gene flow and genetic diversity. Which of these models prevails likely depends on the strength of selection on specific phenotypic traits that influence dispersal, survival, or reproduction. Here, we a priori examined the genomic impact of urbanisation on the Neotropical túngara frog (Engystomops pustulosus), a species known to adapt its reproductive traits to urban selective pressures. Using whole-genome resequencing for multiple urban and forest populations we examined genomic diversity, population connectivity and demographic history. Contrary to both the fragmentation and facilitation models, urban populations did not exhibit substantial changes in genomic diversity or differentiation compared to forest populations, and genomic variation was best explained by geographic distance rather than environmental factors. Adopting an a posteriori approach, we additionally found both urban and forest populations to have undergone population declines. The timing of these declines appears to coincide with extensive human activity around the Panama Canal during the last few centuries rather than recent urbanisation. Our study highlights the long-lasting legacy of past anthropogenic disturbances in the genome and the importance of considering the historical context in urban evolution studies as anthropogenic effects may be extensive and impact non-urban areas on both recent and older timescales.

opencc-by-4.0Jan 2024View details →

HDX-MS dataset for: "Glycan-induced structural activation softens the human papillomavirus capsid for entry through reduction of intercapsomere flexibility"

Hydrogen/deuterium exchange mass spectrometry dataset used in: Glycan-induced structural activation softens the human papillomavirus capsid for entry through reduction of intercapsomere flexibility. Yuzhen Feng*, Dominik van Bodegraven*, Alan Kádek*, Ignacio L.B. Munguira, Laura Soria-Martinez, Sarah Nentwich, Sreedeepa Saha, Florian Chardon, Daniel Kavan, Charlotte Uetrecht#, Mario Schelhaas#, Wouter H. Roos#. Nature Communications 10076 (2024). doi: 10.1038/s41467-024-54373-0 * - authors contributing equally # - corresponding authors Description: Hydrogen/deuterium exchange mass spectrometry (HXMS) analysis of the effect of heparin on the conformational dynamics of human papillomavirus 16 pseudovirus (PsV). Sample processing: HPV16 PsV were prepared according to (Buck & Thompson: Current Protocols in Cell Biology 2007). In short, p16Shell and pClneo-EGFP were transfected into HEK293TT cells. After 48 h, cells were harvested and lysed followed by maturation of the virus particles for 24 h. For purification, the particles were purified using a CsCl step gradient (27 % w/V and 38.8 % w/V CsCl in 10 mM Tris-HCl pH 7.4, 207570 x g, 3 h 50 min, 4 °C) followed by dialysis in Float-A-Lyzer devices (1 mL, Spectra/Por) against a total of 3 L HPV virion buffer (1x PBS, 635 mM NaCl, 0.9 mM CaCl2, 0.5 mM MgCl2, 2.1 mM KCl, pH 7.4). PsV were pre-incubated for 1 h either with or without heparin (H4784, Sigma-Aldrich) at room temperature. To initiate deuterium labelling the samples were 6-fold diluted with the virion buffer they were obtained in, only made of 99.9% D2O (150 mM NaCl, 4.8 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, 0.9 mM CaCl2, 0.5 mM MgCl2, pD 7.2). This resulted in a final concentration of 0.5 µM L1 monomer in the form of PsV with or without 1 mg/ml heparin during deuterium labelling. The exchange reaction was left to proceed at room temperature until aliquots of 45 µl were removed at predetermined time points (1 min, 5 min, 15 min, 1 h and 4 h). In the aliquots, the exchange was immediately stopped by twofold dilution with ice-cold quench buffer (0.25 M glycine, 100 mM TCEP, 8 M urea, indicated pH 2.7), resulting in final pH 2.5. For samples with heparin, the quench buffer additionally contained 1 mg/ml protamine sulphate (P4020, Sigma-Aldrich). After 30 s incubation on ice, the samples were centrifuged at 10.000 x g for 1 min at 0 °C. Each supernatant was transferred to a fresh tube and flash frozen in liquid nitrogen. Low binding microtubes and low binding pipette tips (both Axygen) were used throughout for all handling of viral particles. The frozen samples were quickly thawed and injected into a refrigerated (1°C) HPLC system (Infinity 1260, Agilent Technologies), through a porcine pepsin column (≥ 3200 units/mg, Sigma-Aldrich) in-house immobilized onto POROS-20AL perfusion resin (Thermo Scientific) as described previously (Wang et al.: Molecular & Cellular Proteomics 2002), which was kept at 4°C. Pepsin digestion was performed at isocratic 200 µl/min flow rate (0.4 % formic acid in water). After the digestion, peptides were online desalted for 3 min on a peptide microtrap (OPTI-TRAP, Optimize Technologies) and then eluted on a reversed-phase analytical column (ZORBAX 300SB-C18, 0.5 x 35 mm, 3.5 µm, 300Å, Agilent Technologies). There LC separation proceeded at 25 µl/min flow rate through an 8 min gradient of 8–30% solvent B, followed by a 3 min gradient of 30-90 % solvent B (solvent A: 0.4 % formic acid in water, solvent B: 0.4 % formic acid in acetonitrile). The outlet of the HPLC system was connected to an electrospray ionization (ESI) source of an Orbitrap Fusion Tribrid Mass Spectrometer (Thermo Scientific). The instrument was operated in positive ESI MS-only mode for deuterated samples, scan range 300-2000 m/z, using 4 microscans at resolving power setting 120,000. In a separate measurement on non-deuterated sample, the instrument was used in positive data-dependent ESI MS/MS mode with 30% HCD dissociation, 1 microscan and 240,000 resolving power setting for the identification of all peptides produced by non-specific pepsin cleavage. In total 22 pmol and 50 pmol L1 protein were injected per MS and MS/MS analysis, respectively. To minimize sample carry-over on the protease column, two washing solutions were always injected between sample injections modified from Majumdar et al. 69 (wash solution 1: 5% acetonitrile, 5% isopropanol, 20% acetic acid; wash solution 2: 4 M Urea, 1 M glycine, pH 2.5). All HDX samples were analysed in technical triplicates, except for the 15 min time point for PsV without heparin, which was measured in duplicate. Data processing: Peptides were identified from the MS/MS data by the Andromeda search algorithm implemented in MaxQuant (version 1.6.5.0) using a custom protein database containing the sequences of HPV16 L1 and L2 proteins. Deuterium uptake for the identified peptides was calculated with DeutEx (in-house developed), manually inspected and the statistical significance of the observed differences in deuteration was evaluated by applying an unpaired two-tailed Student’s T-test with single pooled variance evaluated with alpha ≤ 0.05 using the Holm-&Scaron;idák correction for multiple comparisons in Prism 8.0.1 (GraphPad Software). The processed data were visualized using MSTools (https://peterslab.org/MSTools/, Kavan & Man: International Journal of Mass Spectrometry 2011) and open-source PyMol 2.6.0a0 (Schrödinger, Inc). For ZENODO the datafiles were deposited as native Thermo .raw files (including instrumental parameters metadata) while all peaks in the spectra were additionally exported into plain m/z vs intensity .txt files per each scan in the LC-MS analysis as also used for the DeutEx HDX-MS processing.

opencc-zeroNov 2021View details →

Raw microscopy data from: Endoplasmic reticulum stress activates human IRE1α through reversible assembly of inactive dimers into small oligomers

Protein folding homeostasis in the endoplasmic reticulum (ER) is regulated by a signaling network, termed the unfolded protein response (UPR). Inositol-requiring enzyme 1 (IRE1) is an ER membrane-resident kinase/RNase that mediates signal transmission in the most evolutionarily conserved branch of the UPR. Dimerization and/or higher-order oligomerization of IRE1 are thought to be important for its activation mechanism, yet the actual oligomeric states of inactive, active, and attenuated mammalian IRE1 complexes remained unknown. We developed an automated two-color single-molecule tracking approach to dissect the oligomerization of tagged endogenous human IRE1 in live cells. In contrast to previous models, our data indicate that IRE1 exists as a constitutive homodimer at baseline and assembles into small oligomers upon ER stress. We demonstrate that the formation of inactive dimers and stress-dependent oligomers is fully governed by IRE1's lumenal domain. Phosphorylation of IRE1's kinase domain occurs more slowly than oligomerization and is retained after oligomers disassemble back into dimers. Our findings suggest that assembly of IRE1 dimers into larger oligomers specifically enables trans- autophosphorylation, which in turn drives IRE1's RNase activity.

opencc-by-4.0Dec 2021View details →

Preliminary neutron data for cryotrapping peroxide in the active site of human mitochondrial manganese superoxide dismutase crystals for neutron diffraction

The files are preliminary refined neutron coordinates and data on a cryotrapped peroxo species at the active site of human manganese superoxide dismutase crystals.

opencc-by-4.0Feb 2022View details →

UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells

Source data for the publication: UPF3A and UPF3B are redundant and modular activators of nonsense-mediated mRNA decay in human cells. Includes raw image data (e.g. agarose gels, western blots, northern blots), quantifications, qPCR raw Ct values and other supporting material.

opencc-by-4.0Feb 2022View details →

Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall

These are the data and code for the article 'Widespread ripples synchronize human cortical activity during sleep, waking, and memory recall.' Please cite this article when using these data or code.

opencc-by-4.0Jul 2020View details →

Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion

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.   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. The file “metadata.dat” is available in ASCII and RData format and contains: <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> 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 3rd), 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”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> 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 20th), 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. 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 3rd), 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”. 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 12th), 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. 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”.

opencc-by-4.0May 2022View details →

Vibration and IMU Sensing Human Activity Dataset

This dataset contains fine-grained human daily activity data collected by infrastructure vibration sensors and one on-wrist IMU sensor. This dataset is collected from six persons from two domestic homes, in total, there are 12 sub-datasets. For the naming, "p" means person and "l" means location. Each dataset has 11 columns, 1o of them stands for sensors' reading. * Due to the uploading platform, please ignore all files in the folder '__MACOSX', and files whose names start with '._'. These are computer system files, not parts of the shared dataset.  ** If you are going to use this dataset for any publications, we will appreciate you to cite this dataset properly. ************************************************************ The following content is copied from README.txt in the compressed folder: ----------------------- Labels: Keyboard typing 1 Using mouse 2 Handwriting 3 Cutting vegetables 4 Stir-frying vegetables 5 Wiping the table 6 Sweeping floor 7 Using vacuum to vacuum floor: 8 Open and close drawer: 9 None Activity: 10 ----------------------- 11 Columns: 1: Activity label 2: Vibration sensor put on the Living Area floor 3: Vibration sensor put on the Living Area table 4: Vibration sensor put on the Studying Area floor 5: Vibration sensor put on the Studying Area desk 6, 7, 8: Accelerometer X,Y,Z 9, 10, 11: Gyroscope X,Y,Z ----------------------- All signals are zero-meaned. The vibration sensors' sampling rate is roughly around 6500Hz, and the IMU sensors' original sampling rate is roughly around 235Hz. ************************************************************ New in Version 2: - Added extracted features from IMU data and vibration data for reference. - IMU signal is applied with a sliding window of 1.5 seconds with 0.75 seconds overlapping, then the feature is extracted in each window. The feature's description can be found here: https://dl.acm.org/doi/abs/10.1145/3410530.3414320 - The vibration signal is applied with event detection to extract events in the vibration signal. For each event, we normalize it by its energy, then extract 10~490 Hz frequency amplitude as the feature.   Disclaimer: Both event detection and feature extraction are empirical, we don't guarantee it is an optimal one.

opencc-by-4.0Dec 2021View details →

Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies (datasets and R scripts)

This digital archive is an outcome of the paper Kolář J., Macek M., Tkáč P., Novák D. & V.Abraham: Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies. Quaternary Science Reviews, 2022

opencc-zeroApr 2024View details →

Data from: Human avoidance, selection for darkness and prey activity explain wolf diel activity in a highly cultivated landscape

Wildlife that share habitats with humans with limited options for spatial avoidance must either tolerate frequent human encounters or concentrate their activity on those periods with the least risk of encountering people. Based on 5,259 camera trap images of adult wolves from eight territories, we analyzed the extent to which diel activity patterns in a highly cultivated landscape with extensive public access (Denmark) could be explained by diel variation in darkness, human activity, and prey (deer) activity. A resource selection function that contrasted every camera observation (use) with 24 alternative hourly observations from the same day (availability), revealed that diel activity correlated with all three factors simultaneously with human activity having the strongest effect (negative), followed by darkness (positive) and deer activity (positive). A model incorporating these three effects had lower parsimony and classified use and availability observations just as well as a 'circadian' model that smoothed the use-availability ratio as a function of time of the day. Most of the selection for darkness was explained by variation in human activity, supporting the notion that nocturnality (proportion of observations registered at night vs. day at the equinox) is a proxy for temporal human avoidance. Contrary to our expectations, wolves were no more nocturnal in territories with unrestricted public access than in territories where public access was restricted to roads, possibly because wolves in all territories had few possibilities to walk more than a few hundred meters without crossing roads. Overall, Danish wolf packs were 6.5 (95% CI: 4.6-9.6) times more active at night than at daylight, which makes them amongst the most nocturnally active wolves reported so far. These results confirm the prediction that wolves in habitats with limited options for spatial human avoidance, invest more in temporal avoidance.

opencc-zeroMay 2024View details →

Data from: Dissecting gene activation and chromatin remodeling dynamics in single human cells undergoing reprogramming

During cell fate transitions, cells remodel their transcriptome, chromatin, and epigenome; however, it has been difficult to determine the temporal dynamics and cause-effect relationship between these changes at the single-cell level. Here, we employ the heterokaryon-mediated reprogramming system as a single-cell model to dissect key temporal events during early stages of pluripotency conversion using super-resolution imaging. We reveal that, following heterokaryon formation, the somatic nucleus undergoes global chromatin decompaction and removal of repressive histone modifications H3K9me3 and H3K27me3 without acquisition of active modifications H3K4me3 and H3K9ac. The pluripotency gene OCT4 (POU5F1) shows nascent and mature RNA transcription within the first 24 h after cell fusion without requiring an initial open chromatin configuration at its locus. NANOG, conversely, has significant nascent RNA transcription only at 48 h after cell fusion but, strikingly, exhibits genomic reopening early on. These findings suggest that the temporal relationship between chromatin compaction and gene activation during cellular reprogramming is gene context dependent.

opencc-by-4.0Jan 2023View details →

Dataset for "Guidelines for radiation-safe human activities on the Moon"

Dataset for figures in "Guidelines for radiation-safe human activities on the Moon" publication in Nature Astronomy

opencc-by-4.0Nov 2022View details →

Figure 1 in A review of nonlethal and lethal control tools for managing the damage of invasive birds to human assets and economic activities

Figure 1. The number of studies (i.e., field, lab, and modeling) using A) lethal methods to control populations at nesting, foraging, loafing, and roosting sites, B) nonlethal methods to control damage at urban nesting, foraging, loafing, and roosting sites, and C) nonlethal methods to control damage at agricultural foraging sites, including those conducted in the native or introduced ranges of the following birds considered invasive in the United States: rock doves (Columba livia; RODO), Eurasian collared doves (Streptopelia decaocto; EUCD), rose-ringed parakeets (Psittacula krameri; RRPA), monk parakeets (Myiopsitta monachus; MOPA), common mynas (Acridotheres tristis; COMY), European starlings (Sturnus vulgaris; EUST), and house sparrows (Passer domesticus; HOSP). Above each column on the left-hand side is the number of studies that were conducted in the field (i.e., not laboratory or modeling studies; if a study used multiple tools it was counted for each tool). The right-hand side is the subset of field studies for each category (D–F) that included damage assessments in the results.

opencc-by-4.0Jul 2019View details →

Automatic and feature-specific prediction related neural activity in the human auditory system

Downsampled (to 100Hz) data of the paper "Automatic and feature specific prediction related neural activity in the human auditory system"

Figure 3 in Human activity mediates reciprocal distribution and niche separation of two sympatric mongoose species on the Pothwar Plateau, Pakistan

Figure 3. Photomicrographs of whole mounts of hair structure of three rodent species (recovered from fecal samples and reference hairs) consumed by the small Indian mongoose on the Pothwar Plateau. A) Whole mount of recovered hair of Rattus rattus, B) Whole mount of reference hair of Rattus rattus, C) Whole mount of recovered hair of Nesokia indica, D) Whole mount of reference hair of Nesokia indica, E) Whole mount of recovered hair of Mus musculus, F) Whole mount of reference hair of Mus musculus.

Figure 2 in Human activity mediates reciprocal distribution and niche separation of two sympatric mongoose species on the Pothwar Plateau, Pakistan

Figure 2. Average length (cm), mass (g), and diameter (cm) of SIM and GM fecal samples collected from study sites on the Pothwar Plateau.

Figure 5 in Human activity mediates reciprocal distribution and niche separation of two sympatric mongoose species on the Pothwar Plateau, Pakistan

Figure 5. Prey species richness (S), diversity index (H'), and evenness index (E) of the prey species of the small Indian mongoose (Herpestes javanicus) on the Pothwar Plateau during the current study period.

Figure 1 in Human activity mediates reciprocal distribution and niche separation of two sympatric mongoose species on the Pothwar Plateau, Pakistan

Figure 1. GIS-based map showing distribution of the two mongoose species (Herpestes javanicus and H. edwardsii)