Find research datasets worth reusing

opencc-by-4.0Dec 2010View details →

Fig. 2 in Spatial and temporal distribution patterns of ichthyoplankton in a region affected by water regulation by dams

Fig. 2. Average egg abundances (rectangles) and standard errors (bars) by period (a), month (b) and sampling area (c) in the Ilha Grande National Park, from October 2001 to March 2005.

Fig. 6 in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 6. Microhyla xodangorum sp. nov., ♀, holotype (IEBR A.4913 (KP-MĐ-2018-21)). A. Dorsolateral view in life. B. Ventral view in life. C. Underside of right hand. D. Underside of right foot. Scale bars = 1 mm.

Fig. 5 in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 5. Map showing the distribution pattern of the Microhyla heymonsi group based on available molecular and morphological data collected from East and Southeast Asia (red area: M. heymonsi s. str. Vogt, 1911 and M. cf. heymonsi; yellow area: Microhyla sp1; blue area: Microhyla sp2; green area: M. ninhthuanensis Hoang et al., 2021; pink area: Microhyla hmongorum sp. nov.).

Fig. 3 in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 3. Bayesian inference matrilineal genealogy of the Microhyla heymonsi group derived from the analysis of 16S rRNA mtDNA sequences. Numbers above and under branches are Bayesian posterior probabilities and ML bootstrap values; the scale bar represents 0.5 nucleotide substitutions per site.

Fig. 7. A in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 7. A. Habitat of Microhyla hmongorum sp. nov. in Tam Duong District, Lai Chau Province, Vietnam. B. Habitat of Microhyla xodangorum sp. nov. in Kon Plong District, Kon Tum Province, Vietnam. Photos by C.V. Hoang.

Fig. 4 in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 4. Microhyla hmongorum sp. nov., ♂, holotype (IEBR A.4905 (TD-LC2020.121)). A. Dorsolateral view in life. B. Ventral view in life. C. Underside of left hand. D. Underside of right foot. E. Lateral view of the head. Scale bars = 1 mm.

Fig. 1 in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 1. Plots of the first principal component (PC1) versus the second (PC2) for the males and the females of Microhyla hmongorum sp. nov. (green), Microhyla xodangorum sp. nov. (pink), M. ninhthuanensis Hoang et al., 2021 (red), M. daklakensis Hoang et al., 2021 (blue), and M. cf. heymonsi Vogt, 1911 (black).

Fig. 2 in Distribution pattern of the Microhyla heymonsi group (Anura, Microhylidae) with descriptions of two new species from Vietnam

Fig. 2. Bayesian inference matrilineal genealogy of the Microhyla heymonsi group derived from the analysis of 12S rRNA–16S rRNA mtDNA sequences. Numbers above and under branches are Bayesian posterior probabilities and ML bootstrap values. The scale bar represents 0.1 nucleotide substitutions per site.

opencc-by-4.0Jan 2023View details →

Dataset for: 'Patterns in the Plankton – Spatial distribution and long-term variability of copepods on the Agulhas Bank'

This dataset contains environmental data (in situ temperature and chlorophyll a) and integrated biomass (mg C m-2) data for a number of copepod taxa, as well as total copepod biomass and abundance, on the Agulhas Bank, South Africa, as predicted by a Generalized Additive Model (GAM), during late austral spring (October-December) from 1988 to 2011. Mean environmental and copepod biomass parameters for each area and year are also provided. Relevant information on sampling and statistical analysis of spatial distributions has been extracted from the paper. Please see paper for full details and figures, including supplementary data; <a href="https://doi.org/10.1016/j.dsr2.2023.105265">https://doi.org/10.1016/j.dsr2.2023.105265</a>. Please see the Word document Huggett_et_al_2023_README.docx for a list of the data files and descriptions of the contents.

opencc-by-4.0May 2023View details →

Dataset for "Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback"

Dataset for the manuscript entitled: Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback. DOI of the manuscript: <a href="https://doi.org/10.1126/sciadv.adh1328">10.1126/sciadv.adh1328</a> Abstract of the manuscript: Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics in order to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal new properties of sensorimotor cortical integration and set new constraints on the design of neuroprosthetics. Description of the variables in the data storage dictionary: <ol> <li>cursor_positions: sequence of the virtual cursor position over a session</li> <li>range_of_rewardable_cursor_position: range of cursor positions that can be rewarded. Upper threshold excluded. Lower threshold included.  </li> <li>cursor_times: timing of the cursor positions provided by the cursor_positions data, in the same clock as spike and lick times. </li> <li>lick_times: timing of all recorded licks.  </li> <li>reward_times:timing of the opening of the valve that releases the water reward. </li> <li>master_spike_times: time of the spikes of the master neurons.</li> <li>master_spike_shape: spike shape of each spike stored in master_spike_times. The shipe shapes are shown for 3s (30kHz sampling rate), for each 4 electrode of the corresponding tetrode.</li> <li>neighbor_spike_times': same as master_spike_times for neighbor neurons.</li> <li>neighbor_spike_shape': same as master_spike_shape for neighbor neurons.   </li> </ol> General structure of the data set: Each variable is a hierarchical tree of lists: [Protocol][Mouse][Session].  Protocol takes one of the following values: 0: Bar feedback | 1: Full shuffle | 2: No Feedback | 5: Playback Structured Feedback | 7: Spontaneous activity | 8: Barrel Shuffle | 9: Frame shuffle. Mouse and Session iterate through respectively the mice that were involved in the protocole, and the sessions, generally 5 in total. Python code to load the hdf5 File The code below relies on libraries available on a standard, mac anaconda install of the jupyter notebook system on the 25/03/2024. It provides with several nested lists of Numpy arrays. For instance, to access the series of cursor positions of Mouse M during session number S of Protocole P, index as follows:  CP = cursor_positions_DATA[P][M][S] # 1 - Load the libraries     import h5py     import numpy as np     import pylab as pl     filename = "./data.h5"     f = h5py.File(filename, "r") # 2 - Extraction of the cursor position, time, lick and reward time, as well as the activity of the master and neighbor neurons.      cursor_positions = f['cursor_positions']     cursor_positions_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[cursor_positions[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         cursor_positions_DATA.append(DATA_protocole)       cursor_times = f['cursor_times']     cursor_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[cursor_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         cursor_times_DATA.append(DATA_protocole)       lick_times = f['lick_times']     lick_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[lick_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         lick_times_DATA.append(DATA_protocole)       reward_times = f['reward_times']     reward_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[reward_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     bfr = np.array(S)                     DATA_mouse.append(bfr)                  DATA_protocole.append(DATA_mouse)         reward_times_DATA.append(DATA_protocole)       master_spike_times = f['master_spike_times']     master_spike_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[master_spike_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     DATA_unit = []                     for Unit in range(len(S)):                         if not(list(S) == [0,1]):                             N = f[S[Unit][0]]                             bfr = np.array(N)                             DATA_unit.append(bfr)                      DATA_mouse.append(DATA_unit)                 DATA_protocole.append(DATA_mouse)         master_spike_times_DATA.append(DATA_protocole)       neighbor_spike_times = f['neighbor_spike_times']     neighbor_spike_times_DATA = []     for Protocole in range(9):         DATA_protocole = []         P = f[neighbor_spike_times[Protocole][0]]         for Mouse in range(16):                 # Loop through the mice, and collect              M = f[P[Mouse][0]]             if not(list(M) == [0,1]) and (len(M) == 5):                 DATA_mouse = []                 for Session in range(5):                     S = f[M[Session][0]]                     DATA_unit = []                     for Unit in range(len(S)):                         if not(list(S) == [0,1]):                             N = f[S[Unit][0]]                             bfr = np.array(N)                             DATA_unit.append(bfr)                      DATA_mouse.append(DATA_unit)                 DATA_protocole.append(DATA_mouse)         neighbor_spike_times_DATA.append(DATA_protocole)

opencc-by-4.0May 2023View details →

Plant Community Formation and Species Distribution Patterns in relation to environmental variables in Endiras Natural Forest, Fogera District, South Gondar Zone, Ethiopia

We need to deposit the data for the manuscript entitled plant community formation and species distribution patterns in relation to environmental variables in Endiras forest, Fogera District, South Gondar Zone, Ethiopia so as to be cited easily

dryad40/100

Niche suitability and spatial distribution patterns of anurans in a unique Ecoregion mosaic of Northern Pakistan

The lack of information regarding biodiversity states hampers designing and implementation conservation strategies and future targets. Northern Pakistan consists of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity to flourish in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, overlap and distribution patterns in Pakistan, we collected observational data for nine amphibian species across several distinct ecoregions by surveying 87 randomly selected locations from 2016 to 2018 in District Rawalpindi and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between species occurred at significantly higher density in tropical and subtropical coniferous forests than in other ecoregion types. We found species such as Minervarya spp., Hoplobatrachus tigerinus and Euphlyctis spp. showed preference for the lowlands in proximal, central and southern parts of the study area proximal to urban settlements, little vegetation and higher average temperatures. The toads Duttaphrynus bengalensis and D. stomaticus had scattered distributions throughout the study area with no clear preference for elevation. Sphaerotheca pashchima showed a patchy distribution in the midwestern extent of the study area as well as the foothills to the north. Microhyla nilphamariensis also showed a wide distribution throughout the study area with a preference for both lowlands and montane terrain. Endemic frogs (Nanorana vicina and Allopaa hazarensis) were observed only in locations with higher elevations, higher density of streams and lower average temperatures as compared to the other seven species sampled.

opencc-zeroJun 2023View details →

FIGURE 34 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 34 Photos of localities. A) the habitat of M. flavitibius, Arailer (Armenia), rich mesophilous mountain grasslands, 2100 m asl., B) the joint habitat of M. brevis and M. flavitibius, Boghaqar (Armenia), edges of deciduous mountain forest with shrubs and grasslands, 1340–1400 m asl, C) The habitat of M. brevis, Tsghuk (Armenia). Subalpine meadows, ruderal vegetation (herbaceous legumes, Carduus) on roadsides. 2100 m asl, D) the habitat of M. aberrans, Vršac Mountains, 300 m asl., Serbia. PHOTO BY GRIGORY POPOV (A-C) AND TAMARA TOT (D).

FIGURE 33 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 33 Maximum Likelihood tree based on the General Time Reversible model constructed by MEGA version 7.0. A discrete Gamma distribution was used to model evolutionary rate differences among sites (5 categories (+G, parameter = 1.9228)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 63.3530 % sites). Bootstrap support values (≥50) are shown near nodes.

FIGURE 32 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 32 Holotype of Merodon brevis, male. A) metafemur and metatibia of the left leg, lateral view, B) male genitalia, lateral view, C) metafemur and metatibia of the right leg, lateral view, D) male genitalia, ventral view.

FIGURE 30 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 30 Body parts of Merodon brevis. A) metaleg of male, lateral view, B) metaleg of female, lateral view, C) thorax of male, lateral view.

FIGURE 29 A in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 29 A) Merodon brevis, head of male, lateral view, B) M. brevis, head of female, lateral view, C) M. aberrans, antenna of male, lateral view, D) M. brevis, antenna of male, lateral view, E) M. retectus sp. nov., antenna of male, lateral view, F) M. aberrans, antenna of female, lateral view, G) M. brevis, antenna of female, lateral view, H) M. retectus sp. nov., antenna of female, lateral view. Abbreviations: x, distance from top of basoflagellomere and most prominent point of pedicel; y, width of basoflagellomere at level of base of arista.

FIGURE 26 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 26 Male genitalia of Merodon retectus sp. nov. A) epandrium, lateral view, B) epandrium, ventral view, C) hypandrium, lateral view. Abbreviations: al, anterior surstyle lobe; c, cercus; il, interior accessory lobe of posterior surstyle lobe; l, lingula; pl, posterior surstyle lobe.

FIGURE 31 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 31 Male genitalia of Merodon brevis. A) epandrium, lateral view, B) epandrium, ventral view, C) hypandrium, lateral view. Abbreviations: al, anterior surstyle lobe; c, cercus; il, interior accessory lobe of posterior surstyle lobe; l, lingula; pl, posterior surstyle lobe.

FIGURE 24 in Integrative taxonomy of the Merodon aberrans (Diptera, Syrphidae) species group: distribution patterns and description of three new species

FIGURE 24 Body parts of Merodon retectus sp. nov., male. A) abdomen, dorsal view, B) abdomen, lateral view, C) thorax, lateral view.