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.
346
datasets available to search
ShareScore release 0.7.1
Dataset results
346 results for “structural complexity”
Fig. 1 in Changes In The Structure Of Nest Complexes Of The Red Wood Ants Formica Rufa And F. Polyctena (Hymenoptera, Formicidae) In Urban Forests
Fig. 1. Location of nest complexes of Formica rufa (diamonds), F. polyctena (triangles) on the territory of the city of Kyiv (Ukraine). The city limits are marked by a red line, the forest areas by dark grey. The numbers correspond to the serial number of each complex.
Research data supporting: "Machine learning of microscopic structure-dynamics relationships in complex molecular systems"
<p>This repository contains the set of data and the code to reproduce the results shown in "Machine learning of microscopic structure-dynamics relationships in complex molecular systems" published on Machine Learning: Science and Technology (DOI: 10.1088/2632-2153/ad0fa5).</p>
Dataset for "Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows"
<p>This dataset accompanies the article "Structural complexity and benthic metabolism: resolving the links between carbon cycling and biodiversity in restored seagrass meadows" accepted for publication in Biogeosciences (https://doi.org/10.5194/bg-2023-173). The dataset includes benthic fluxes and biodiversity data in from bare sediments, restored <em>Zostera marina</em> and a natural <em>Z. marina</em> meadow collected in Gåsö, Sweden (58.2325, 11.3984) between July 05 - July 20, 2022. </p>
Data from: Social complexity affects cognitive abilities but not brain structure in a Poecilid fish
<p>Some cognitive abilities are suggested to be the result of a complex social life, allowing individuals to achieve higher fitness through advanced strategies. However, most evidence is correlative. Here, we provide an experimental investigation of how group size and composition affect brain and cognitive development in the guppy (<em>Poecilia reticulata</em>). For six months, we reared sexually mature females in one of three social treatments: a small conspecific group of three guppies, a large heterospecific group of three guppies and three splash tetras (<em>Copella arnoldi</em>) – a species that co-occurs with the guppy in the wild, and a large conspecific group of six guppies. We then tested the guppies' performance in self-control (inhibitory control), operant conditioning (associative learning), and cognitive flexibility (reversal learning) tasks. Using X-ray imaging, we measured their brain size and major brain regions. Larger groups of six individuals, both conspecific and heterospecific groups, showed better cognitive flexibility than smaller groups, but no difference in self-control and operant conditioning tests. Interestingly, while social manipulation had no significant effect on brain morphology, relatively larger telencephalons were associated with better cognitive flexibility. This suggests alternative mechanisms beyond brain region size enabled greater cognitive flexibility in individuals from larger groups. Although there is no clear evidence for the impact on brain morphology, our research shows that living in larger social groups can enhance cognitive flexibility. This indicates that the social environment plays a role in the cognitive development of guppies.</p>
Raw diffraction images of the crystal structure of human VISTA extra cellular domain in complex with Fab fragment of pH-selective anti-VISTA antibody
<p>The diffraction datasets was collected at X10SA, SLS. The dataset was collected from one crystal using a rotation scheme for 222º oscillation with the following experimental parameters; Wavelength: 0.9998 Å, Detector: EIGER2 Si 16M (DECTRIS Co. Ltd.). The crystal belonged to space group C 1 2 1 with unit cell parameters a=207.66, b=39.51, c=177.98 Å, and beta=117.12°.</p><p>PDB ID: 8TBQ</p>
AutoDock and CB-Dock data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p>AutoDock 4.2 and CB-Dock data for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> with M<sup>pro</sup> from SARS-CoV-2 from PDB Id: 6LU7. </p>
NMR data for (NPA)6Zn3(H2O)2 in Synthesis, structural analysis, and docking studies with SARS-CoV-2 of a trinuclear zinc complex with N-phenylanthranilic acid ligands
<p><sup>1</sup>H, <sup>13</sup>C, COSY, HMBC, and HSQC NMR data in fid format for (NPA)<sub>6</sub>Zn<sub>3</sub>(H<sub>2</sub>O)<sub>2</sub> (NPA = 2-(phenylamino) benzoate) in DMSO-<em>d</em><sub>6.</sub></p>
CX-MS Datasets for "Comprehensive Structure and Functional Adaptations of the Yeast Nuclear Pore Complex"
<p>This repository contains chemical cross-linking mass spectrometry data of affinity-purified Yeast nuclear pore complexes.</p> <p>Data Files Description:</p> <p>NPC_XL_Identification_Inter_Crosslinked.csv: Inter-protein cross-links identified by pLink 2.</p> <p>NPC_XL_spectra.mgf: MS2 spectra data for the identified cross-links.</p> <p>NPC_XL_proteins.fasta : Protein sequences used for search.</p> <p>Sample Processing:</p> <p>NPCs were immuno-purified from Mlp1 tagged S. cerevisiae strains (Kim et al., 2018). After native elution, 1.0 mM disuccinimidyl suberate (DSS) was added and the sample was incubated at 25ºC for 40 min with shaking (1,200 rpm). The reaction was quenched by adding a final concentration of 50 mM freshly prepared ammonium bicarbonate and incubating for 20 min with shaking (1,200 rpm) at 25ºC. The sample (50 µg) was then concentrated and denatured at 98ºC for 5 min in a solubilization buffer (10% solution of 1-dodecyl-3-methylimidazolium chloride (C12-mim-Cl) in 50 mM ammonium bicarbonate, pH 8.0, 100 mM DTT). After denaturation, the sample was centrifuged at 21,130 g for 10 min and the supernatant was transferred to a 100 kDa MWCO ultrafiltration unit (MRCF0R100, Microcon). The sample was quickly spun at 1,000 g for 2 min and washed twice with 50 mM ammonium bicarbonate. After alkylation (50 mM iodoacetamide), the cross-linked NPC in-filter was digested by trypsin and lysC O/N at 37ºC. After proteolysis, the sample was recovered by centrifugation and peptides were fractionated into 10-12 fractions by using a stage tip self-packed with basic C18 resins (Dr. Masch GmbH). Fractionated samples were pooled prior to LC/MS analysis.</p> <p>Desalted cross-link peptides were dissolved in the sample loading buffer (5% Methanol, 0.2% FA), separated with an automated nanoLC device (nLC1200, Thermo Fisher), and analyzed by an Orbitrap Q Exactive HFX (Pharma mode) mass spectrometer (Thermo Fisher) as previously described (Xiang et al., 2020; Xiang et al., 2021). Briefly, peptides were loaded onto an analytical column (C18, 1.6 μm particle size, 100 Å pore size, 75 μm × 25 cm; IonOpticks) and eluted using a 120-min liquid chromatography gradient. The flow rate was approximately 300 nl/min. The spray voltage was 1.7 kV. The QE HF-X instrument was operated in the data-dependent mode, where the top 10 most abundant ions (mass range 380 – 2,000, charge state 4 - 8) were fragmented by high-energy collisional dissociation (HCD). The target resolution was 120,000 for MS and 15,000 for tandem MS (MS/MS) analyses. The quadrupole isolation window was 1.8 Th; the maximum injection time for MS/MS was set at 200 ms.</p> <p>Data Processing:</p> <p>The raw data were searched with pLink2 (Chen et al., 2019b). An initial MS1 search window of 5 Da was allowed to cover all isotopic peaks of the cross-linked peptides. The data were automatically filtered using a mass accuracy of MS1 ≤ 10 ppm (parts per million) and MS2 ≤ 20 ppm of the theoretical monoisotopic (A0) and other isotopic masses (A+1, A+2, A+3, and A+4) as specified in the software. Other search parameters included cysteine carbamidomethyl as a fixed modification and methionine oxidation as a variable modification. A maximum of two trypsin missed-cleavage sites was allowed. The initial search results were obtained using a default 5% false discovery rate (FDR) expected by the target-decoy search strategy. Spectra were manually verified to improve data quality (Kim et al., 2018; Shi et al., 2014). Cross-linking data were analyzed and plotted with CX-Circos (http://cx-circos.net).</p>
Fig. 3 in Communities Of Ditylenchus Destructor Satellite Species Of Nematodes In Infected Potato Tubers: Species Composition Of Phytonematode Complex And The Structure Of Their Infracommunities
Fig. 3. The dynamics of the ratio of the total number of various trophoecological group nematodes during the disease of potato tubers caused by D. destructor.
Fig. 1 in Communities Of Ditylenchus Destructor Satellite Species Of Nematodes In Infected Potato Tubers: Species Composition Of Phytonematode Complex And The Structure Of Their Infracommunities
Fig. 1. Ditylenchus destructor (Thorne, 1945) potato nematode: A — female; B — head end of the female; C — spicules; D — male; E — head end of the male; F, G — lateral field by the Thorne, 1945.
Fig. 5 in Communities Of Ditylenchus Destructor Satellite Species Of Nematodes In Infected Potato Tubers: Species Composition Of Phytonematode Complex And The Structure Of Their Infracommunities
Fig. 5. Occurrence of various species of phytonematodes during successive stages of the pathological process.
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment
<p>The file contains two benchmark sets: Heterodimer-AF2 and Docking benchmark 5.5-AF2 test. Each set includes (1) doecy folder, (2) native folder, and (3) label_info.csv. </p>
Benchmark Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three benchmark datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
DProQ: A Gated-Graph Transformer for Protein Complex Structure Assessment: MAF2 set
<p>Multimer AF2 set. For more information, please read our paper on biorxiv:</p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2">https://www.biorxiv.org/content/10.1101/2022.05.19.492741v2</a> </p>
Full Datasets for: EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures
<p>This archive contains three datasets associated with the Equivariant Graph Refiner (EGR), two for protein complex structure refinement (PSR Test and Benchmark 2) and the other for protein complex structure assessment (M4S Test). The refinement datasets contain (1) a `pred` directory that contains decoy structure PDB files and (2) a `true` directory that contains native structure PDB files. The quality assessment dataset contains (1) `target_name` directories that each contain decoy structure PDB files for a given protein target and (2) a `label_info.csv` file listing each decoy structure's DockQ score and CAPRI class label.</p>
Transformed crane data from: Balancing structural complexity with ecological insight in spatio-temporal species distribution models
<p>The potential for statistical complexity in species distribution models (SDMs) has greatly increased with advances in computational power. Structurally complex models provide the flexibility to analyse intricate ecological systems and realistically messy data, but can be difficult to interpret, reducing their practical impact. Founding model complexity in ecological theory can improve insight gained from SDMs. </p> <p>Here, we evaluate a marked point process approach, which uses multiple Gaussian random fields to represent population dynamics of the Eurasian crane (<em>Grus grus</em>) in a spatio-temporal species distribution model. We discuss the role of model components and their impacts on predictions, in comparison with a simpler binomial presence/absence approach. Inference is carried out using Integrated Nested Laplace Approximation (INLA) with inlabru, an accessible and computationally efficient approach for Bayesian hierarchical modelling, which is not yet widely used in SDMs. </p> <p>Using the marked point process approach, crane distribution was predicted to be dependent on the density of suitable habitat patches, as well as close to observations of the existing population. This demonstrates the advantage of complex model components in accounting for spatio-temporal population dynamics (such as habitat preferences and dispersal limitations) that are not explained by environmental variables. However, including an AR1 temporal correlation structure in the models resulted in unrealistic predictions of species distribution; highlighting the need for careful consideration when determining the level of model complexity.</p> <p>Increasing model complexity, with careful evaluation of the effects of additional model components, can provide a more realistic representation of a system, which is of particular importance for a practical and impact-focused discipline such as ecology (though these methods extend to applications for a wide range of systems). Founding complexity in contextual theory is not only fundamental to maintaining model interpretability, but can be a useful approach to improving insight gained from model outputs. </p>
Structurally well-defined anti-π-allyliridium complexes catalyze Z-retentive asymmetric allylic alkylation of oxindoles
<p>Uploaded herein are all the output files of the computational studies on Ir-catalyzed <em>Z</em>-retentive asymmetric allylic alkylation of oxindoles.</p> <p>Some Gaussian checkpoint files, summary of Mayer bond order calculations (as plain txt files), and the output and checkpoint files of the calculations on a known Ir-complex (JACS, 2017, 3606), are included in this update.</p>
Structure of an MHC I–tapasin–ERp57 editing complex defines chaperone promiscuity
<p>Adaptive immunity depends on cell surface presentation of antigenic peptides by major histocompatibility complex class I (MHC I) molecules and on stringent ER quality control in the secretory pathway. The chaperone tapasin in conjunction with the oxidoreductase ERp57 is crucial for MHC I assembly and for shaping the epitope repertoire for high immunogenicity. However, how the tapasin–ERp57 complex engages MHC I clients has not yet been determined at atomic detail. Here, we present the 2.7 Å crystal structure of a tapasin–ERp57 heterodimer in complex with peptide-receptive MHC I. Our study unveils molecular details of client recognition by the multichaperone complex and highlights elements indispensable for peptide proofreading. The structure of this transient ER quality control complex provides the mechanistic basis for the selector function of tapasin and showcases how the numerous MHC I allomorphs are chaperoned during peptide loading and editing.</p>
Changes in the acoustic structure of Australian bird communities along a habitat complexity gradient
<p>Avian vocalizations have evolved in response to a variety of abiotic and biotic selective pressures. While there is some support for signal convergence in similar habitats that is attributed to adaptation to the acoustic properties of the environment (the ‘acoustic adaptation hypothesis’, AAH), there is also evidence for character displacement as result of competition for signal space among coexisting species (the ‘acoustic niche partitioning hypothesis’). We explored the acoustic space of avian assemblages distributed along six different habitat types (from herbaceous habitats to warm rainforests) in south eastern Queensland, Australia. We employed three acoustic diversity indices (acoustic richness, evenness, and divergence) to characterize the signal space. In addition, we quantified the phylogenetic and morphological structure (in terms of both body mass and beak size) of each community. Acoustic parameters showed a moderately low phylogenetic signal, indicating labile evolution. Although, we did not find meaningful differences in acoustic diversity indices among habitat categories, there was a significant relationship between the regularity component (evenness) and vegetation height indicating that acoustic signals are more evenly distributed in dense habitats. After accounting for differences in species richness, the volume of acoustic space (i.e., acoustic richness) decreased as the level of phylogenetic and morphological resemblance among species in a given community increased. Additionally, we found a significantly negative relationship between acoustic divergence and divergence in body mass indicating that the less different species are in their body mass, the more different their songs are likely to be. This implies the existence of acoustic niche partitioning at community level. Overall, while we found mixed support for the AAH, our results suggest that community-level effects may play a role in structuring acoustic signals within avian communities in this region. This study shows that signal diversity estimated by diversity metrics of community ecology based on basic acoustic parameters can provide additional insight into the structure of animal vocalizations. <br> </p>
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm. in Early Complex Tiering Pattern: Upper Ordovician, Barrandian Area, The Czech Republic
Text-fig. 13. Zoophycos isp. a: BK 17, Layer No. 6; b: BK 27, Layer No. 8; c: lateral tunnel continuing from spreite side to the surrounding rock, BK 28, Layer No. 23; d: BK 22, Layer No. 1; e: "juvenile" stage of the structure on a horizontal winding tunnel, BK 21, Layer No. 26; f: BK 24, Layer No. 17; g: broad winding tunnel adjacent to spreite, BK 26, Layer No. 6; h: BK 15, Layer No. 18; i: BK 23, Layer No. 2. Scale bar = 1 cm.
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.