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Figure 19. Allende nigrohumeralis. A, female habitus. B, female frontal view. C–G, epigynum. C, caudal view. D, lateral view. E, ventral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–P, male pedipalp. J, expanded. K, ventral view. L, dorsal view. M, expanded. N, conductor detail. P, ectal view. Scale bars 0.2 in Systematics of the spider genus Metabus O. P.-Cambridge, 1899 (Araneoidea: Tetragnathidae) with additions to the tetragnathid fauna of Chile and comments on the phylogeny of Tetragnathidae
Figure 19. Allende nigrohumeralis. A, female habitus. B, female frontal view. C–G, epigynum. C, caudal view. D, lateral view. E, ventral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–P, male pedipalp. J, expanded. K, ventral view. L, dorsal view. M, expanded. N, conductor detail. P, ectal view. Scale bars 0.2 mm, except in habitus 1 mm.
Figure 17. Allende patagiatus. A, female habitus. B, female frontal view. C–G, epigynum. C, ventral view. D, caudal view. E, lateral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–M, male pedipalp. J, ectal view. K, conductor detail. L, dorsal view. M, ventral view. Scale bars 0.2 in Systematics of the spider genus Metabus O. P.-Cambridge, 1899 (Araneoidea: Tetragnathidae) with additions to the tetragnathid fauna of Chile and comments on the phylogeny of Tetragnathidae
Figure 17. Allende patagiatus. A, female habitus. B, female frontal view. C–G, epigynum. C, ventral view. D, caudal view. E, lateral view. F, caudal view cleared. G, dorsal view cleared. H, male habitus. I, male frontal view. J–M, male pedipalp. J, ectal view. K, conductor detail. L, dorsal view. M, ventral view. Scale bars 0.2 mm, except in habitus 1 mm.
Figure 12. Placoid scales. A–D, morphotype 1. A–B, A, occlusal view. B, lateral view. C–D, C, occlusal view. D, lateral view. E–H, morphotype 2. E, occlusal view. F, lateral view. G, posterior view. H, anterior view. I–L, morphotype 3. I, occlusal view. J, lateral view. K, posterior. L, anterior. M–P, morphotype 4. M, occlusal view. N, lateral view. O, posterior view. P, anterior view. Q–U, morphotype 5. Q, occlusal view. R, lateral view. S, lateral view. T, posterior view. U, anterior view. V–Y, morphotype 6. V, anterior view. W, lateral view. X, occlusal view. Y, posterior view. All scale bars equal 0.5 in Neoselachians (Chondrichthyes, Elasmobranchii) from the Lower and lower Upper Cretaceous of north-eastern Spain
Figure 12. Placoid scales. A–D, morphotype 1. A–B, A, occlusal view. B, lateral view. C–D, C, occlusal view. D, lateral view. E–H, morphotype 2. E, occlusal view. F, lateral view. G, posterior view. H, anterior view. I–L, morphotype 3. I, occlusal view. J, lateral view. K, posterior. L, anterior. M–P, morphotype 4. M, occlusal view. N, lateral view. O, posterior view. P, anterior view. Q–U, morphotype 5. Q, occlusal view. R, lateral view. S, lateral view. T, posterior view. U, anterior view. V–Y, morphotype 6. V, anterior view. W, lateral view. X, occlusal view. Y, posterior view. All scale bars equal 0.5 mm.
T-Evos: A Large-Scale Longitudinal Study on CI Test Execution and Coverage Evolution
<p><strong>Description</strong></p> <p>T-Evos is a dataset on test results and coverage evolution, covering 6,495 consecutive commits across 12 open-source Java projects.</p> <p> </p> <p><strong>Version 0.1.0</strong></p> <p>We have included the test results and code coverage data. The test results data has been combined into the `test_status.json` for ease of access at the root level of the repository. Each of the node zip files contains the code coverage data. In each of the zip files, the code coverage data folder is structured as followed:</p> <ul> <li>project_name <ul> <li>commit_id <ul> <li>details <ul> <li>test_name.json (individual test coverage data)</li> <li>...</li> </ul> </li> <li>coverage.json (full test coverage data on commit_id)</li> </ul> </li> <li>surefire_test_method_dict.json (available test cases)</li> </ul> </li> <li>project_name.csv (commit build status [success/fail])</li> </ul> <p>Note: the current version of the dataset includes temporary `project_cluster` folders that users might kindly ignore.</p>
Comparing adaptive capacity index across scales: The case of Italy
<p>Measuring adaptive capacity as a key component of vulnerability assessments has become one of the mostchallenging topics in the climate change adaptation context. Numerous approaches, methodologies and con-ceptualizations have been proposed for analyzing adaptive capacity at different scales. Indicator-based assess-ments are usually applied to assess and quantify the adaptive capacity for the use of policy makers. Nevertheless,they encompass various implications regarding scale specificity and the robustness issues embedded in thechoice of indicators selection, normalization and aggregation methods. We describe an adaptive capacity indexdeveloped for Italy's regional and sub-regional administrative levels, as a part of the National Climate ChangeAdaptation Plan, and that is further elaborated in this article. The index is built around four dimensions and tenindicators, analysed and processed by means of a principal component analysis and fuzzy logic techniques. As aninnovative feature of our analysis, the sub-regional variability of the index feeds back into the regional levelassessment. The results show that composite indices estimated at higher administrative or statistical levels ne-glect the inherent variability of performance at lower levels which may lead to suboptimal adaptation policies.By considering the intra-regional variability, different patterns of adaptive capacity can be observed at regionallevel as a result of the aggregation choices. Trade-offs should be made explicit for choosing aggregators thatreflect the intended degree of compensation. Multiple scale assessments using a range of aggregators with dif-ferent compensability are preferable. Our results show that within-region variability can be better demonstratedby bottom-up aggregation methods.</p>
Data from: Fractal triads efficiently sample ecological diversity and processes across spatial scales
<p>The relative influence of ecological assembly processes, such as environmental filtering, competition, and dispersal, vary across spatial scales. Changes in phylogenetic and taxonomic diversity across environments provide insight into these processes, however, it is challenging to assess the effect of spatial scale on these metrics. Here, we outline a nested sampling design that fractally spaces sampling locations to concentrate statistical power across spatial scales in a study area. We test this design in northeast Utah, at a study site with distinct vegetation types (including sagebrush steppe and mixed conifer forest), that vary across environmental gradients. We demonstrate the power of this design to detect changes in community phylogenetic diversity across environmental gradients and assess the spatial scale at which the sampling design captures the most variation in empirical data. We find clear evidence of broad-scale changes in multiple features of phylogenetic and taxonomic diversity across aspect. At finer scales, we find additional variation in phylodiversity, highlighting the power of our fractal sampling design to efficiently detect patterns across multiple spatial scales. Thus, our fractal sampling design and analysis effectively identify important environmental gradients and spatial scales that drive community phylogenetic structure. We discuss the insights this gives us into the ecological assembly processes that differentiate plant communities found in northeast Utah.</p>
Data and scripts associated with "Rock weathering controls soil carbon storage potential at a continental scale"
<p>The zipped folder contains the following files:</p> <p>CONUS_mineral_grid.csv [gridded soil mineralogy maps, with coordinates representing cell centers]</p> <p>CONUS_grid.csv [blank grid, used for reproducing the analysis]</p> <p>NCSS_datamerge_071321.R [core script used for running the analyses presented in the associated publication]</p> <p>apply_depthwtavg.R [wrapper function for depth weighted averaging]</p> <p>attach_ENV.R [function to attach climate data]</p> <p>bootstrap_functions.R [functions for spatial bootstrap statistics]</p> <p>depthweightavg.R [function for calculating depth weighted averages]</p> <p>get_MWBM.R [function for compiling climate data into averages]</p> <p>get_SLP.R [function for reading and pre-processing the NASGLP dataset</p> <p>SLP_IDW.R [inverse distance weighting function interpolating the NASGLP data at query points]</p> <p>weather_calcs.R [secondary calculations partitioning Al and Fe; weathering rate estimation]</p>
Source: Photos courtesy of M.M. Le Roux (a–d) and K.S. Mashego (e–f) FIGURE 1: Morphological characters of Thesium confine (a, d, e) and T. durum (b, c, f) showing (a) the scale-like leaves found in both species; (b) involucral bracts that are invariably present in T. durum; (c) longitudinal section of a flower showing the dense hairs on the perianth lobes, stigma opposite the anthers and the twisted placental column; the habit of (d) T. confine and typical herbarium specimens of (e) T. confine and (f) T. durum. in A taxonomic evaluation of the Thesium confine species complex (Santalaceae)
Source: Photos courtesy of M.M. Le Roux (a–d) and K.S. Mashego (e–f) FIGURE 1: Morphological characters of Thesium confine (a, d, e) and T. durum (b, c, f) showing (a) the scale-like leaves found in both species; (b) involucral bracts that are invariably present in T. durum; (c) longitudinal section of a flower showing the dense hairs on the perianth lobes, stigma opposite the anthers and the twisted placental column; the habit of (d) T. confine and typical herbarium specimens of (e) T. confine and (f) T. durum.
Figures 1–6 in Parasitoids (Hymenoptera: Chalcidoidea) of the white peach scale, Pseudaulacaspis pentagona (Targioni-Tozzetti) (Hemiptera: Diaspididae) on Prunus salicina Lindl. (Rosales: Rosaceae) in South Korea
Figures 1–6. Five species of parasitoids associated with Pseudaulacaspis pentagona (Targioni-Tozzetti) on Japanese plum trees in South Korea. 1) Parasitoid emergence holes in scale covers of P. pentagona (white peach scale). 2) Aphytis proclia (Walker), female. 3) Encarsia berlesei (Howard), female. 4) Marietta carnesi (Howard), female. 5) Pteroptrix orientalis (Silvestri), female. 6) Arrhenophagus chionaspidis Aurivillius, female.
Multi-scale Fractures and Water Invasion Dynamics in Buried-Hill Bedrock Gas Reservoirs of the Jianbei Slope, Qaidam Basin, China
<p>In this study, efforts are made to comprehensively analyze the multi-scale fracture development, the dynamic water intrusion, and the favorable remaining gas areas in the ancient buried-hill bedrock gas reservoir in the Jianbei slope of the Qaidam Basin, based on integrated analyses of thin sections, FMI imaging logging, three-dimensional seismic data, and dynamic production data. Fractures in the bedrock are demonstrated to be controlled by tectonic stress, magma intrusion, hydrothermal activity, and weathering and leaching. The complex fracture genesis leads to differences in the scale and type of bedrock fractures with multiple scales and high angles. The dip angle of the fracture gradually increases from the unconformity on the top of the bedrock downward inside the bedrock. Meanwhile, the dip angle may decrease from the center to both sides due to the difference in the fracture strength of the fault zone. Fractures are mostly near-east-west oriented and they normally intersect with the ground stress at an acute angle. Effective fractures can provide advantageous seepage channels for fluids. Formation water is demonstrated to rapidly channel through large-scale fractures while laterally invading through small-scale fractures in the gas reservoir. The western area of the reservoir is featured by intensive water invasion, rapid pressure drop, and fast production decline, while the eastern area is characterized by weak water invasion as well as relatively stable pressure and productivity. Three relatively independent water-seal gas systems with JB1-3, JBH1-3 and J3 Wells as the center are formed, and the remaining gas is enriched.</p>
FIG. 2. — Souzalopesmyia polleti n in Souzalopesmyia Albuquerque, 1951 (Diptera: Muscidae): new species from South America with an updated phylogeny based on morphological evidence, in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 2. — Souzalopesmyia polleti n. sp.: A-D, ♂: sternite 5, dorsal view (A); epandrium, cercal plate and surstyli, dorsal view (B); epandrium, cercal plate and surstyli, lateral view (C); hypandrium and associated structures, lateral view (D); E-H, ♀: ovipositor, dorsal view (E); ovipositor, ventral view (F); spermatheca (G). Scale bars: 0.5 mm.
Data for - Tracking one-in-a-million: Large-scale benchmark for microbial single-cell tracking with experiment-aware robustness metrics
<p><strong>Large-scale Corynebacterium glutamicum data set with Segmentation and Tracking Annotation</strong></p> <p>We provide five time-lapse sequences with manually corrected segmentation and tracking annotations of growing <strong><em>C. glutamicum</em></strong> cultivations. The dataset contains more than 1.4 million cell observations in 29k cell tracks and 14k cell divisions. We provide videos of the annotations (videos.zip) and the dataset in <a href="http://celltrackingchallenge.net/datasets/">Cell Tracking Challenge</a> format (ctc_format.zip). In the videos, cell contours are rendered in yellow, cell links between frames are colored red and cell divisions, and their links are colored in blue.</p> <p><strong>Data Acquisition</strong></p> <p><strong><em>Corynebacterium glutamicum</em></strong> ATCC 13032 was cultivated in BHI-medium at 30°C in this study. From and overnight preculture, the main culture was inoculated the next day with a starting OD600 of 0.05 and grown at 120 rpm to a OD600 of 0.25. A chip was fabricated, according to <a href="https://doi.org/10.1039/D0LC00711K">(Täuber et al., 2020)</a>, and fixed to the microscope’s holder. The main culture cells were transferred to monolayer growth chambers (height = 720 nm) on the microfluidic chip. Flow through the microfluidic device was mediated by pressure driven pumps with a pressure of 100 mbar on the medium reservoir.</p> <p>The time-lapse phase contrast images of five monolayer growth chambers were taken every minute using an inverted microscope (Nikon Eclipse Ti2) with a 100x oil emersion objective and a DS-QI2 camera (Nikon) at 15 % relative DIA-illumination intensity and 100 ms exposure time. The spatial image resolution is 0.072 μm/px.</p>
WHU-OHS: A benchmark dataset for large-scale Hyperspectral Image classification
<p>The WHU-OHS dataset is made up of 42 OHS satellite images acquired from more than 40 different locations in China. The imagery has a spatial resolution of 10 m (nadir) and a swath width of 60 km (nadir). There are 32 spectral channels ranging from the visible to near-infrared range, with an average spectral resolution of 15 nm. We cropped each image into 512 × 512 pixels with a stride of 32. There are 4822, 513, and 2460 sub-images in the training, validation, and test sets, respectively.</p> <p>For transferability test, we choose eight pairs of OHS images, and each pair contains one source image (S) and one target image (T):</p> <p>S1: Changchun</p> <p>T1: Jilin</p> <p>S2: Wuxi</p> <p>T2: Shanghai</p> <p>S3: Guangzhou</p> <p>T3: Zhongshan</p> <p>S4: Xining</p> <p>T4: Lanzhou</p> <p>S5: Hetian</p> <p>T5: Kelamayi</p> <p>S6: Anyi</p> <p>T6: Nanchang</p> <p>S7: Changde</p> <p>T7: Changsha</p> <p>S8: Tianjin</p> <p>T8: Tangshan</p> <p>The 26 OHS images except for the eight pairs:</p> <p>O1: Baoding</p> <p>O2: Chongqing</p> <p>O3: Fujin</p> <p>O4: Huainan</p> <p>O5: Huhehaote</p> <p>O6: Jinzhong</p> <p>O7: Luliang</p> <p>O8: Manasi_1</p> <p>O9: Manasi_2</p> <p>O10: Nanmulin</p> <p>O11: Neimenggu</p> <p>O12: Qingdao</p> <p>O13: Qinghuangdao</p> <p>O14: Shawan</p> <p>O15: Shenyang</p> <p>O16: Shuozhou</p> <p>O17: Songpan</p> <p>O18: Taian</p> <p>O19: Tongjiang_1</p> <p>O20: Tongjiang_2</p> <p>O21: Wuzhong</p> <p>O22: Xundian</p> <p>O23: Xuzhou</p> <p>O24: Yidu</p> <p>O25: Zangzu</p> <p>O26: Zhongshan</p> <p>The image patches have been normalized and scaled by 10000 to reduce storage cost. Divide the pixel values by 10000 and then the image patches can be used directly.</p>
Electronic correlations and universal long-range scaling in kagome metals
<p>Dataset for the constrained Random Phase Approximation calculations shown in the manuscript. The VASP OUTCAR (ascii plain texy file) contains the whole cRPA U_iijj, U_ijij and U_ijji tensors, from which the numbers in the table of the paper (Supplementary Material) are extracted. Input files necessary to reproduce the results are also in the repository.</p>
Datasets for ``Batchelor, Saffman, and Kazantsev spectra in the small-scale dynamo''
<pre>This directory contains Supplemental Material and an index.html file with links to the run directories and idl plotting routines with secondary data for the other figures for the paper "Batchelor, Saffman, and Kazantsev spectra in the small-scale dynamo" by A. Brandenburg, H. Zhou and R. Sharma. If anything turns out to be incomplete, please email brandenb@nordita.org. </pre>
Scaled and Translated Image Recognition (STIR)
<p><strong>Paper:</strong> <a href="https://arxiv.org/abs/2211.10288">[2211.10288] Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks (arxiv.org)</a><br> <strong>Code:</strong> <a href="https://github.com/taltstidl/scale-equivariant-cnn">taltstidl/scale-equivariant-cnn: Official code for "Just a Matter of Scale? Reevaluating Scale Equivariance in Convolutional Neural Networks" (github.com)</a></p> <p>While convolutions are known to be invariant to (discrete) translations, scaling continues to be a challenge and most image recognition networks are not invariant to them. To explore these effects, we have created the Scaled and Translated Image Recognition (STIR) dataset. This dataset contains objects of size <span class="math-tex">\(s \in [17,64]\)</span>, each randomly placed in a <span class="math-tex">\(64 \times 64\)</span> pixel image.</p> <p><strong>Using the dataset</strong></p> <p>Depending on which data you are planning to use, download one or more of the following files. Data is stored in compressed <code>.npz</code> format and can be loaded as documented <a href="https://numpy.org/doc/stable/reference/generated/numpy.load.html">here</a>.</p> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>emoji.npz</code></td> <td>Emoji vector icons rendered as white icon on black background</td> </tr> <tr> <td><code>mnist.npz</code></td> <td>Classic MNIST handwritten digits rescaled to varying sizes</td> </tr> <tr> <td><code>trafficsign.npz</code></td> <td>Traffic signs from street imagery downscaled to varying sizes</td> </tr> <tr> <td><code>aerial.npz</code></td> <td>Objects in aerial imagery downscaled to varying sizes</td> </tr> </tbody> </table> <p>Each file contains multiple arrays that can be accessed in a dictionary-like fashion. The keys are documented below, where <code>n</code> is the number of classes for a given file and <code>m</code> is the number of instances for each class. Both <code>emoji.npz</code> (36 classes, 1 instance) and <code>mnist.npz</code> (10 classes, 50 instances) are in black & white while <code>trafficsign.npz</code> (16 classes, 25 instances) and <code>aerial.npz</code> (9 classes, 25 instances) are in color.</p> <table> <thead> <tr> <th>Key</th> <th>Shape</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td><code>imgs</code></td> <td><code>(3, 48, n, m, 64, 64)</code> black & white, <code>(3, 48, n, 64, 64, 3)</code> color</td> <td>Images grouped into 3 sets (training, validation, testing) and 48 different scales. Values will be in range <code>0</code> to <code>255</code>.</td> </tr> <tr> <td><code>lbls</code></td> <td><code>(3, 48, n, m)</code></td> <td>Indices referencing ground truth labels. See <code>lbldata</code> for descriptive names. Values will be in range <code>0</code> to <code>n - 1</code>.</td> </tr> <tr> <td><code>scls</code></td> <td><code>(3, 48, n, m)</code></td> <td>Known scales as given by bounding box size. Values will be in range <code>17</code> to <code>64</code>.</td> </tr> <tr> <td><code>psts</code></td> <td><code>(3, 48, n, m, 2)</code></td> <td>Known position of bounding box. First value is distance to left edge, second value distance to top edge.</td> </tr> <tr> <td><code>metadata</code></td> <td><code>(6, 2)</code></td> <td>Metadata on title, description, author, license, version and date.</td> </tr> <tr> <td><code>lbldata</code></td> <td><code>(n,)</code></td> <td>Descriptive names for each ground truth labels.</td> </tr> </tbody> </table> <p>For use in Python a dataset class is provided that implements the basic functionality for loading a certain split and scale selection, as illustrated in the code below. It ensures shuffling is done in a consistent manner such that ground truth scales and positions can be retrieved. Metadata and label descriptions can be retrieved via <code>metadata</code> and <code>labeldata</code>, respectively.</p> <pre><code class="language-python">from data.dataset import STIRDataset dataset = STIRDataset('data/emoji.npz') # Obtain images and labels for training images, labels = dataset.to_torch(split='train', scales=[32, 64], shuffle=True) # Obtain known scales and positions for above scales, positions = dataset.get_latents(split='train', scales=[32, 64], shuffle=True) # Get metadata and label descriptions metadata = dataset.metadata label_descriptions = dataset.labeldata</code></pre> <p><strong>License and Attribution</strong></p> <p>When using this dataset for your own research, please respect the individual licenses of the original data. These are distributed within the data files' metadata. For attribution in papers, we recommend the following citations.</p> <ol> <li>D. Gandy, J. Otero, E. Emanuel, F. Botsford, J. Lundien, K. Jackson, M. Wilkerson, R. Madole, J. Raphael, T. Chase, G. Taglialatela, B. Talbot, and T. Chase. Font Awesome. https://fontawesome.com/v5/download, Nov. 2022.</li> <li>Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. <em>Proc. IEEE</em>, 86(11):2278–2324, Nov. 1998.</li> <li> C. Ertler, J. Mislej, T. Ollmann, L. Porzi, G. Neuhold, and Y. Kuang. The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale. In <em>2020 16th Eur. Conf. Comput. Vision (ECCV)</em>, Glasgow, UK, Aug. 2020.</li> <li>G.-S. Xia, X. Bai, J. Ding, Z. Zhu, S. Belongie, J. Luo, M. Datcu, M. Pelillo, and L. Zhang. DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. In <em>2018 IEEE/CVF Conf. Comput. Vision and Pattern Recognition (CVPR)</em>, pages 3974–3983, Salt Lake City, UT, USA, June 2018.</li> </ol>
Tradeoffs and benefits explain scaling, sex differences, and seasonal oscillations in the remarkable weapons of snapping shrimp (Alpheus spp.)
<p>Evolutionary theory suggests that individuals should express costly traits at a magnitude that optimizes the cost-benefit ratio for the trait-bearer. Trait expression varies across a species because costs and benefits vary among individuals. For example, if large individuals pay lower costs than small individuals, then larger individuals should reach optimal cost-benefit ratios at a greater magnitude of trait expression. Using the remarkable cavitation-shooting weapons found in the big claws of male and female alpheid snapping shrimp, we test whether size- and sex-dependent expenditures explain the scaling of weapon size relative to body size and why males have larger proportional weapon size than females. We found that males and females from three snapping shrimp species (<em>Alpheus</em> <em>heterochaelis</em>, <em>Alpheus</em> <em>angulosus</em>, and <em>Alpheus</em> <em>estuariensis</em>) exhibit resource allocation tradeoffs between weapon and abdomen mass. For male <em>A</em>. <em>heterochaelis</em>, the species for which we had the greatest sample size and statistical power, the smallest individuals showed the steepest tradeoff. Our extensive dataset in <em>A</em>. <em>heterochaelis</em> also included data about pairing, breeding season, and egg clutch size. Therefore, we could test for reproductive tradeoffs and benefits in this species. Female <em>A</em>. <em>heterochaelis</em> exhibited additional tradeoffs between weapon size and egg count, average egg volume, and total egg mass volume. For average egg volume, the smallest females exhibited the steepest tradeoff relative to weapon size. Furthermore, for both sexes, large weapons were positively correlated with the relative size of their pair mate; however, for males only, large weapons were positively correlated with being paired in the first place. In conclusion, we establish that size-dependent tradeoffs underlie reliable scaling relationships of costly traits. Furthermore, we show that males and females differ in weapon investment, suggesting that weapons are especially beneficial to males and especially burdensome to females.</p>
Input data to model multiple effects of large-scale deployment of grass in crop-rotations at European scale
<p>This is the input dataset to a Python script (<a href="https://github.com/oskeng/MF-bio-grass">https://github.com/oskeng/MF-bio-grass</a>) used to model the effects of widespread deployment of grass in rotations with annual crops to provide biomass while remediating soil organic carbon (SOC) losses and other environmental impacts.</p> <p>For more information about the dataset and the study, see the original article:</p> <p>Englund, O., Mola-Yudego, B., Börjesson, P., Cederberg, C., Dimitriou, I., Scarlat, N., Berndes, G. Large-scale deployment of grass in crop rotations as a multifunctional climate mitigation strategy. GCB Bioenergy</p>
Data from: Factors at multiple scales drive parasite community structure
<p>1. Understanding how ecological communities are assembled remains a key goal of ecosystem ecology. Because communities are hierarchical, factors acting at multiple scales can contribute to patterns of community structure. Parasites provide a natural system to explore this idea, as they exist as discrete communities within host individuals, which are themselves part of a community and metacommunity.</p> <p>2. We aimed to understand the relative contribution of multi-scale drivers in parasite community assembly and assess how patterns at one level may mask those occurring at another. Specifically, we wanted to disentangle patterns caused by passive sampling from those determined by ecological drivers, and how these vary with scale.</p> <p>3. We applied a Markov Random Fields model and assessed measures of β-diversity and nestedness for 420 replicate parasite infracommunities (parasite assemblages in host individuals) across two freshwater mussel host species, three sites and two time periods, comparing our results to simulations from four different ecologically relevant null models.</p> <p>4. We showed that β-diversity between sites (explaining 25% of variation in parasite distribution) and host species (41%) is greater than expected, and β-diversity between individual hosts is smaller than expected, even after accounting for parasite prevalence and characteristics of host individuals. Further, parasite communities were significantly less nested than expected once parasite prevalence and host characteristics were both accounted for, but more nested than expected otherwise, suggesting a degree of modularity at the within-host level that is masked if underlying host and parasite characteristics are not taken into account. The Markov Random Fields model provided evidence for possible competitive within-host parasite interactions, providing a mechanism for the observed infracommunity modularity.</p> <p>5. An integrative approach that examines factors at multiple scales is necessary to understand the composition of ecological communities. Further, patterns at one level can alter the interpretation of ecologically important drivers at another if variation at higher scales is not accounted for. </p>
Small-scale fisheries of the US (2013-2017)
<p>This data set includes information about the diversity, catch composition, and economic value of small-scale fisheries in the United States. In includes 1,019 small-scale fishing “units” that target 350 commercially reported taxa. Additional information about the data and the methods used to compile the data set are published in <em>A review of small-scale marine fisheries in the United States: Definitions, scale, drivers of change, and policy gaps</em>, published in Marine Policy (2022). </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.