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.
1,291
datasets available to search
ShareScore release 0.9.0
Dataset results
1,291 results for “gap”
Figure 5 in Filling the knowledge gaps of Paratelmatobius mantiqueira (Anura: Leptodactylidae): tadpole, acoustic repertoire, and life history traits
Figure 5. Call repertoire of Paratelmatobius mantiqueira. (a) The advertisement call is composed of pulsed notes, with varied pulse intervals (voucher specimen ZUEC-AMP 24836). Generally, a shorter pulse interval was observed at the beginning and ending of the notes. (b) While in antiphony, the advertisement call had an increase in call rate due to a reduction in intercall duration, notes per call, and internote duration. (c) The release call was composed of pulsed notes and had a visible harmonic structure. (d) The aggressive call had a high number of pulses per note and variable pulse interval.
Gaps and complex structurally variant loci in phased genome assemblies
<p>Supplementary data including code for an science article 'Gaps and complex structurally variant loci in phased genome assemblies'.</p>
Data-driven gap filling and spatio-temporal filtering of the GRACE and GRACE-FO records
<p>Gravity Recovery And Climate Experiment and Follow On (GRACE/-FO) global monthly measurements of Earth's gravity field have led to significant advances in quantifying mass transfer. However, a significant temporal gap between missions hinders evaluating long-term mass variations. Moreover, instrumental and processing errors translate into large non-physical North-South stripes polluting geophysical signals. We use Multichannel Singular Spectrum Analysis (M-SSA) to overcome both issues by exploiting spatio-temporal information of Level-2 GRACE/-FO solutions, filtered using the DDK7 decorrelation and a new complementary filter, built based on the residual noise between fully processed data and a parametric fit to observations. Using an iterative M-SSA on Equivalent Water Height (EWH) time series processed by CSR, GFZ, GRAZ, and JPL, we replace missing data and outliers to obtain a combined evenly sampled solution. Then, we apply M-SSA to retrieve common signals between each EWH time series and its same-latitude neighbours to further reduce residual spatially uncorrelated noise. Comparing GRACE/-FO M-SSA solution with SLR and SWARM low-degree Earth’s gravity field and hydrological model demonstrates its ability to satisfyingly fill missing observations. Our solution achieves a noise level comparable to mass concentration (mascon) solutions over oceans (3.2 mm EWH), without requiring \textit{a priori} information nor regularisation. While short-wavelength signals are challenging to capture using highly filtered spherical harmonics or mascons solutions, we show that our technique efficiently recovers localized mass variations using well-documented mass transfers associated with reservoir impoundments.</p>
Socio-ecological gap analysis to forecast species range contractions for conservation
Conservation requires both a needs assessment and prioritization scheme for planning and implementation. Range maps are critical for understanding and conserving biodiversity, but current range maps often omit content, negating important metrics of variation in populations and places. Here, we integrate a myriad of conditions that are spatially explicit across distributions of carnivores to identify gaps in capacity necessary for their conservation. Expanding on traditional gap analyses that focus almost exclusively on quantifying discordance in protected area coverage across a species' range, our work aggregates threat layers (e.g., drought, human pressures) with resources layers (e.g., protected areas, cultural diversity) to identify gaps in available conservation capacity (ACC) across ranges for 91 African carnivores. Our model indicated that all species have some portion of their range at risk of contraction, with an average of 15 percentage range loss. We found that the ACC differed based on body size and taxonomy. Results deviated from current perceptions of extinction risks for species with an International Union for Conservation of Nature (IUCN) threat status of Least Concern and yielded insights for species categorized as Data Deficient. Our socio-ecological gap analysis presents a geospatial approach to inform decision-making and resource allocation in conservation. Ultimately, our work advances forecasting dynamics of species' ranges that are increasingly vital in an era of great socio-ecological change to mitigate human–wildlife conflict and promote inclusive carnivore conservation across geographies.
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree STEM</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gender</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree gap</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Word tree rights</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Empirical conceptual map</p>
Factors associated with the gender gap in the STEM sector: Comparison of theoretical and empirical concept maps and qualitative SWOT analysis
<p>Theoretical concept map</p>
FIGURE 4 in Filling gaps in the knowledge of the Colombian Darwin wasps (Hymenoptera: Ichneumonidae): new records, description of unknown males, and taxonomical notes
FIGURE 4. Species of Pimplinae (Ichneumonidae) from Mesenia-Paramillo Nature Reserve, Colombia: A) Neotheronia lineata (Fabricius) (in vivo; ♁); B) Pimpla albomarginata Cameron (in vivo; ♀); C) Pimpla punicepis Cresson (in vivo; ♀); D) Pimpla pyramis (Porter) (♀). Scale bar: 1 mm.
FIGURE 3 in Filling gaps in the knowledge of the Colombian Darwin wasps (Hymenoptera: Ichneumonidae): new records, description of unknown males, and taxonomical notes
FIGURE 3. Species of Dolichomitus Smith from Mesenia-Paramillo Nature Reserve, Colombia. A–C. Dolichomitus mariajoseae Araujo & Pádua: A) Habitus (in vivo; ♀); B) Habitus (♁); C) Mid coxa, lateral view (♁). D–F. Dolichomitus menai Araujo & Pádua: D) Habitus (in vivo; ♀); E) Habitus (♁); F) Mid coxa, lateral view (♁). G–I. Dolichomitus orejuelai Araujo & Pádua: G) Habitus (in vivo; ♀); H) Habitus (in vivo; ♁); I) Mid coxa, lateral view (♁). Scale bar: 1 mm.
FIGURE 2 in Filling gaps in the knowledge of the Colombian Darwin wasps (Hymenoptera: Ichneumonidae): new records, description of unknown males, and taxonomical notes
FIGURE 2. Species of Darwin wasps (Ichneumonidae) from Mesenia-Paramillo Nature Reserve, Colombia: A) Aphanistes silviae Alvarado (♀) (Anomaloninae); B) Woldstedius paulus Dasch (♀) (Diplazontinae); C) Forestopius auguratricis Alvarado & Palacio (♀) (Metopiinae); D) Acrotaphus pseudoamazonicus Pádua & Sääksjärvi (in vivo; ♀) (Pimplinae); E) Anastelgis garciai Gauld (in vivo; ♀) (Pimplinae). Scale bar: 1 mm.
Experimental and Computational Dataset for ""Direct monitoring of the thermodynamics and kinetics of DNA and RNA dinucleotide hybridization onto gaps and overhangs"
<p>Binding of short nucleic acid segments (<4 nucleotides) to single-strand templates occurs as a critical intermediate in processes such as non-enzymatic nucleic acid replication and toehold-mediated strand displacement. The templates in these reactions often contain adjacent duplex segments that stabilize base pair formation to single-strand gaps or overhangs, but the thermodynamics and kinetics of hybridization in such contexts are poorly understood due to the<br> experimental challenges of probing weak binding and rapid structural dynamics. Here we develop an approach to directly measure the thermodynamics and kinetics of DNA and RNA dinucleotide<br> hybridization using steady-state and temperature-jump infrared spectroscopy. Thermodynamic results indicate that duplex segments adjacent to overhangs and gaps stabilize dinucleotide binding<br> through a combination of coaxial stacking interactions and additional factors that remain unclear. All-atom molecular dynamics simulations indicate potential structural differences between the<br> dinucleotide-gap complexes and canonical duplexes that may contribute to binding stability. We demonstrate time-resolved measurements of dinucleotide dehybridization and observe timescales ranging from 200 ns to 40 µs depending on the template and temperature. Together, our work provides an initial step for predicting the binding stability and kinetics of short DNA and RNA duplex segments onto various type of nucleic acid templates</p>
Data underlying "Hard Superconducting Gap in InSb Nanowires"
<p>This repository contains an extended dataset of raw data underlying <a href="https://doi.org/10.1021/acs.nanolett.7b00540">Ö. Gül et al., <em>Hard Superconducting Gap in InSb Nanowires</em>, Nano Lett.<strong> 17</strong>, 2690 (2017)</a></p> <p>It also contains python notebooks for recreating the plots in the paper, developed in 2022/23.</p>
Legacy effects of canopy gaps on liana abundance 25 years later in a seasonal tropical evergreen forest in northeastern Thailand
<p><span>Liana</span><span>s </span><span>require host trees to reach and stay in the forest canopy, but as seedlings and juveniles, they benefit from canopy gaps created by treefalls</span><span>.</span><span> Here, we evaluated the relative importance of these two aspects, i.e., the availability of potential</span><span> hosts</span><span> vs. the legacy effect of past treefall gaps, on the local abundance of liana stems in a seasonal tropical evergreen forest in the Sakaerat Biosphere Reserve in northeastern Thailand. Within a 2.5-ha plot for forest dynamics monitoring, canopy height was measured in 1993 and 2018 at 5-m intervals to distinguish areas of mature (canopy height </span><span>≥</span><span> 20 m), building (10-20 m), and gap phases (< 10 m). In 2017–2018, we surveyed all liana stems </span><span>≥</span><span> 1 cm in diameter at breast height within 50 subplots (10 m × 10 m each) and recorded their diameter and the diameter of the host tree. Of a total of 445 liana individuals, 242 could be identified at least to the family level, while the others had clear morphological traits of climbing mechanisms. The number of liana stems was higher in areas that had been at the building/gap phase than those at the mature phase in 1993. When this 25-year-old legacy of past gap locations was considered, there was a positive association of local abundance between lianas and trees in areas at the mature phase in 2018. In conclusion, liana abundance reflected a long-term legacy of past treefall gaps more than 25 years earlier in this seasonal evergreen forest.</span></p>
Dataset for "Gap junctions desynchronize a neural circuit to stabilize insect flight"
<p><strong>Data guide</strong></p> <p>For each figure, we provide one data folder. Each folder contains one overview Excel file that provides all source data that is shown in the respective figure. For some figure panels additional subfolders with original data files are provided. Please note that the data for figures 3A-J (except F and G) have been uploaded together with the analyses and simulation code and are accessible under the doi: 10.5281/zenodo.7740678</p> <p> </p> <p><strong>Data Figure 1: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 1.xlsx</li> <li>Folder Panel A: <ul> <li>Recordings shown in A and G with sorted spikes: figure 1a_original recording.smr</li> <li>Data matrix of waveforms shown in A as text file: figure 1a_waveforms_200818_561.5-566.5sec.txt</li> </ul> </li> <li>Folder Panels B+F: all original recordings with sorted spikes that have been analyzed for panels B and F: <ul> <li>171106_m1.SMR</li> <li>181029_m1.smr</li> <li>190522_m1.SMR</li> <li>200305_m1.smr</li> <li>200818_m1.smr</li> <li>200824_m1.smr</li> <li>200827_m1.SMR</li> <li>201005_m1.smr</li> </ul> </li> <li>Folder Panel C: all original recordings as .smr (Spike2) files that went into the analyses shown in figure 1c. <ul> <li>100 recordings in folders “Fly 1 to Fly 100”</li> <li>1 recording in folder “single animal”</li> <li>the waveforms of these 101 animals are also as text in file Figure 1.xlsx</li> </ul> </li> <li>Folder Panel D <ul> <li>The original data file of the recording shown in figure 1D as axon file: 20128002_OUT1_firing.abf</li> <li>The selected waveforms shown in figure 1D are also as text in file Figure 1.xlsx</li> </ul> </li> <li>All recordings that underlie the analysis in figure 1E are as number matrices in the file Figure 1.xlsx</li> <li>The spike events shown in figure 1G are as number matrices in the file Figure 1.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 2: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 2.xlsx</li> <li>The number matrices for all traces shown in figure 2A are as number matrices in the file Figure 2.xlsx</li> <li>Panel B: all number matrices of the event times that underlie the analyses shown in figure 2B: <ul> <li>Folder “control”: .txt of event times of the 7 control animals</li> <li>Folder “ShakB-RNAi”: .txt of event times of the 7 ShakB-RNAi-kd animals</li> <li>Folder “ShakB-oe”: .txt of event times of the 7 ShakB-oe (overexpression) animals</li> </ul> </li> <li>Panel C original confocal image stack for image data shown in figure 2C: <ul> <li>original Leica file: figure 2c_image stack.lif</li> <li>Folder “tiff” with same image stack saved as tiff</li> </ul> </li> </ul> <p> </p> <ul> <li>Panel D: The number matrices underlying the waveforms of the traces shown in Figure 2D are contained in the file Figure 2.xlsx</li> <li>Panel E: The number matrices underlying the waveforms of the traces shown in Figure 2E are contained in the file Figure 2.xlsx</li> <li>Panel F: The number matrices of all pre- and postsynaptic voltage areas of paired MN recordings and the analyses thereof for Figure 2F (left) are contained in the file Figure 2.xlsx. The number matrices of all pre- and postsynaptic voltage amplitudes of paired MN recordings and the analyses thereof for Figure 2F (right) are contained in the file Figure 2.xlsx</li> <li>Panel G: The number matrices underlying the waveforms of the traces shown in Figure 2G are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2H are contained in the file Figure 2.xlsx</li> <li>The number matrices underlying the waveforms of the traces shown in Figure 2I are contained in the file Figure 2.xlsx</li> </ul> <p> </p> <p><strong>Data Figure 3: </strong>(all data for Fig. 3 except f and g are at doi: 10.5281/zenodo.7740678)</p> <ul> <li>Overview Excel file with all source data for Figures 3G and F: Figure 3.xlsx</li> <li>Panel F: The number matrices for all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel F: The number matrices for the waveforms of all traces shown in figure 3F (command voltage +50mV) and for 14 additional command voltage steps are contained in the file Figure 3.xlsx</li> <li>Panel G: The number matrices for the waveforms of all traces shown in figure 3G are contained in the file Figure 3.xlsx <ul> <li>original data file from which the traces in figure 3G (top) were taken: control MN4_3_0s-87s.SMR</li> <li>original data file from which the traces in figure 3G (bottom) were taken: UAS shab81 2022_10_07_0s-87s.SMR</li> </ul> </li> </ul> <p> </p> <p><strong>Data Figure 4: </strong></p> <ul> <li>Overview Excel file with all source data: Figure 4.xlsx</li> <li>Panel A: The number matrices for all event signals and all waveform traces shown in figure 4A are as number matrices in the file Figure 4.xlsx</li> <li>Figure 4B (top): <ul> <li>The number matrices for the waveforms (voltage recording and fit) shown in Figure 4b (top) are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms of the voltage recordings that were fitted to obtain the averages shown in Figure 4B top are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (top) are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4B (middle): <ul> <li>The number matrices for the waveforms (calcium imaging and fit) shown in Figure 4b (middle) are contained in the file Figure 4.xlsx</li> <li>The fitting parameters and results for Figure 4B (middle) are contained in the file Figure 4.xlsx</li> <li>The original imaging data for this analysis is contained in movie S4</li> </ul> </li> <li>Figure 4B (bottom): <ul> <li>The number matrices for the waveforms (wingbeat frequency from events and fit) shown in Figure 4b (bottom) are contained in the file Figure 4.xlsx</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4C: <ul> <li>The number matrices for the waveform shown in Figure 4C are contained in the file Figure 4.xlsx</li> <li>The original calcium imaging movie as .avi file: DLM6_Imaging.avi</li> <li>The original calcium imaging movie as tiff stack: file DLM6_imaging_tiff</li> <li>The fitting results are contained in the file Figure 4.xlsx</li> </ul> </li> <li>Figure 4E: <ul> <li>The number matrices for the waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The number matrices for the waveforms that resulted in the average waveforms shown in Figure 4E are contained in the file Figure 4.xlsx</li> <li>The original data files for analyses in Figure 4E are contained as text files in the folders ShakB_oe (7 text files) and control (7 text files)</li> </ul> </li> </ul>
Data source and projections of maintenance energy gaps for "Caloric reductions needed to achieve obesity goals by 2030 and 2040: A modeling study"
<p><strong>Variables in "data_ENSANUT_waves.xlsx"</strong></p> <table> <thead> <tr> <th scope="col">Name</th> <th scope="col">Variable</th> </tr> </thead> <tbody> <tr> <td><em>id</em></td> <td>Identifier for each individual in the data.</td> </tr> <tr> <td><em>est_var</em></td> <td>Strata for the estimation of variances, accounting for survey design.</td> </tr> <tr> <td><em>svy_weights</em></td> <td>Complex survey weight.</td> </tr> <tr> <td>code_upm</td> <td>Identifier of the primary sampling unit.</td> </tr> <tr> <td>sex</td> <td>Sex of the individual (``male'' or ``female'').</td> </tr> <tr> <td>age</td> <td>Age (yrs).</td> </tr> <tr> <td>body_weight</td> <td>Measured body weight (kg).</td> </tr> <tr> <td>height</td> <td>Measured height (cm).</td> </tr> <tr> <td>bmi</td> <td>Body mass index, estimated before the simulation process (kg/m<sup>2</sup>).</td> </tr> <tr> <td>SES</td> <td>Socioeconomic level, divided in tertiles. This variable was constructed using Principal Components Analysis.</td> </tr> <tr> <td>year</td> <td>Indicator for each ENSANUT wave (2000, 2006, 2012, 2016, 2018).</td> </tr> <tr> <td>svy_weights_raking_2030</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2030.</td> </tr> <tr> <td>svy_weights_raking_2040</td> <td>Complex survey weight, constructed for the baseline sample (ENSANUT 2018) to replicate the expected age and sex distribution in 10-year age groups for 2040.</td> </tr> <tr> <td>body_weight_final_2030_Nordpred</td> <td>Simulated body weight by 2030 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Nordpred</td> <td>Simulated body weight by 2040 based on MEGs projections of the Nordpred-based fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Nordpred</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Nordpred</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Nordpred-based fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Nordpred</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Nordpred</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Nordpred-based fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_Gompertz</td> <td>Simulated body weight by 2030 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_Gompertz</td> <td>Simulated body weight by 2040 based on MEGs projections of the Gompertz model (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_Gompertz</td> <td>Simulated body mass index by 2030 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_Gompertz</td> <td>Simulated body mass index by 2040 based on MEGs projections of the Gompertz model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_Gompertz</td> <td>Indicator of obesity by 2030, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_Gompertz</td> <td>Indicator of obesity by 2040, based on MEGs projections of the Gompertz model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_linear</td> <td>Simulated body weight by 2030 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_linear</td> <td>Simulated body weight by 2040 based on MEGs projections of the linear fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_linear</td> <td>Simulated body mass index by 2030 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_linear</td> <td>Simulated body mass index by 2040 based on MEGs projections of the linear model (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_linear</td> <td>Indicator of obesity by 2030, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_linear</td> <td>Indicator of obesity by 2040, based on MEGs projections of the linear model (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2030_rootSquare</td> <td>Simulated body weight by 2030 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>body_weight_final_2040_rootSquare</td> <td>Simulated body weight by 2040 based on MEGs projections of the root square fit (kg). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2030_rootSquare</td> <td>Simulated body mass index by 2030 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>BMI_final_2040_rootSquare</td> <td>Simulated body mass index by 2040 based on MEGs projections of the root square fit (kg/m<sup>2</sup>). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2030_rootSquare</td> <td>Indicator of obesity by 2030, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> <tr> <td>obes_final_2040_rootSquare</td> <td>Indicator of obesity by 2040, based on MEGs projections of the root square fit (1 = yes, 0 = no). This variable is defined only for the baseline sample (ENSANUT 2018). </td> </tr> </tbody> </table>
Figure 1 in Male homophily in South American herpetology: one of the major processes underlying the gender gap in publications
Figure 1. Co-authorship networks of two South American herpetological journals, considering multiple-authored articles published. Each point represents an author colored by gender, and each line represents a co-authorship connection in a publication. The enlightened areas are the most connected clusters in each network. Pie-charts show the distribution of connections by gender. Barplots show the gender distribution of the first 40 higher betweenness scores. (A-C) Cuadernos de Herpetología's network of papers published between 1985 and 2019. (D-F) South American Journal of Herpetology's network of papers published between 2006 and 2019. Abbreviations: MM = man–man connections; WM = woman–man connections; WW = woman–woman connections.
Figure 2 in Male homophily in South American herpetology: one of the major processes underlying the gender gap in publications
Figure 2. Bibliometric data of nine herpetological journals. (A-C) Gender ratio in papers published during 2018 and 2019, considering different taxonomic groups and sub-areas. Multi authored papers considering the journal's qualies: (D) gender composition within publishing groups, and (E-J) gender participation in protagonic roles and its effect on the proportion
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.