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.
3,118
datasets available to search
ShareScore release 0.7.1
Dataset results
3,118 results for “resources”
Different currencies for calculating resource phenology result in opposite inferences about trophic mismatches
<p>Shifts in phenology are among the key responses of organisms to climate change. When rates of phenological change differ between interacting species they may result in phenological asynchrony. Studies have found conflicting patterns concerning the direction and magnitude of changes in synchrony, which have been attributed to biological factors. A hitherto overlooked additional explanation is differences in the currency used to quantify resource phenology, such as abundance and biomass. Studying an insectivorous bird, Sanderling, and its prey, we show that the median date of cumulative arthropod biomass occurred, on average, 6.9 days after the median date of cumulative arthropod abundance. In some years this difference could be as large as 21 days. For 23 years, hatch dates of Sanderlings became less synchronized with the median date of arthropod abundance, but more synchronized with the median date of arthropod biomass. The currency-specific trends can be explained by our finding that mean biomass per arthropod specimen increased with date. Using a conceptual simulation, we show that estimated rates of phenological change for abundance and biomass can differ depending on temporal shifts in the size distribution of resources. We conclude that studies of trophic mismatch based on different currencies for resource phenology can be incompatible with each other.</p>
Fast-slow traits predict competition network structure and its response to resources and enemies
<p>Plants interact in complex networks but how network structure depends on resources, natural enemies, and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow-growing species responding differently. Nitrogen addition increased competition asymmetry in slow-growing networks, as expected, but decreased it in fast-growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other's effect. Our results show that plant growth strategy is key to understanding how competition responds to resources and enemies, a prediction from classic theories that has rarely been tested by linking functional traits to competition networks.</p>
Dataset for the submitted manuscript titled 'Response of Southern Ocean Resource Stress in a Changing Climate'
<p>Netcdf output files of Primary Production, carbon export, Fe and Mn limitations, and deficiencies from PISCES-QUOTA model with Mn limitation (described in Anugerahanti and Tagliabue, 2023) forced by historical IPSL CM5A climate model simulation (1850-2005) and RCP8.5 high emission IPSL CM5A simulation (2005-2100) on the ORCA2 grid, as described and discussed in Anugerahanti and Tagliabue, in the manuscript submitted for Geophysical Research Letters. Due to the large size of the files, this has been collated to only contain surface/ upper 100m south of 40S. </p>
External resources
<ul> <li>ex_databases: demo database `ex_databases`, which includes three sublists: `LigRec.DB`, `RecTF.DB`, and `TFTG.DB`. Each sublist consists of three columns: `source`, `target`, and `score`. In the aforementioned sublists, the `source` column represents signalling molecules such as ligands, receptors, and transcription factors, while the `target` column represents signalling molecules such as receptors, transcription factors, and target genes. The `score` column in `LigRec.DB` and `TFTG.DB` indicates the frequency of interactions occurring in the collected database, while the `score` column in `RecTF.DB` represents the predicted probabilities of interactions occurring in the collected database.</li> <li>ex_inputs: The demo data is derived from the breast cancer dataset of 10X Visiumd, which can be found at [here](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1). We analyzed this spatial transcriptomic data using Seurat (Version 4.0.2) and performed deconvolution using the RCTD method. The demo data `ex_inputs`, which includes: <p> `exprMat`: Normalized expression matrix.<br> `annoMat`: Cell type annotation matrix.<br> `locaMat`: Spatial location information matrix.<br> `ligs_of_inter`: Potential ligand lists for different cell types.<br> `recs_of_inter`: Potential receptor lists for different cell types.<br> `tgs_of_inter`: Potential target gene lists for different cell types.</p> </li> <li>unprocessed_ex_inputs: The demo data is derived from the breast cancer dataset of 10X Visiumd, which can be found at [here](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Breast_Cancer_Block_A_Section_1). We analyzed this spatial transcriptomic data using Seurat (Version 4.0.2) and performed deconvolution using the RCTD method. The demo data `unprocessed_ex_inputs`, which includes: `exprMat`: Normalized expression matrix.</li> </ul> <p> `exprMat`: Unnormalized expression matrix.<br> `annoMat`: Cell type annotation matrix.<br> `locaMat`: Spatial location information matrix.</p>
Data from: emergence of structure in plant-pollinator networks: low floral resource constrains network specialisation
<p>Specialisation enhances the efficiency of plant-pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant-pollinator interactions, but the understanding of how floral resources affect the structure of plant-pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability to be equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka-Volterra consumer-resource model to investigate the influence of floral resource availability on plant-pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density-dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the Generalised Linear Model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant-pollinator networks. This is because floral resource dynamics constraint animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles on the structure of plant-pollinator network, distinctive from plant species density dynamics.</p>
FIGURE 2 in Evidence for dynamic resource partitioning between two sympatric reef shark species within the British Indian Ocean Territory
FIGURE 2 (a) Maximum likelihood standard ellipse areas (, 40% of the data) for isotopes δ13C v. δ15N in fin, (b) muscle, (c) red blood cell, (d) plasma and for isotope δ34S v. δ15C (e) and δ15N (f) of Carcharhinus amblyrhynchos () and Carcharhinus albimarginatus (). Convex hulls () are drawn between the centers of each group. Overlapping values, if present, are the proportion of overlapping area of the two ellipses. Potential competitor–prey teleost data are shown () with associated error bars (± 1 SD). Ellipses for red blood cell and plasma presented for reference but represent small sample sizes (<10) and therefore come with lower confidence
FIGURE 1 in Evidence for dynamic resource partitioning between two sympatric reef shark species within the British Indian Ocean Territory
FIGURE 1 Bayesian isotope mixing models were used to determine the extent that Carcharhinus amblyrhynchos and Carcharhinus albimarginatus were reliant on reef (blue) or pelagic (red) resources. End members were set as the most δ13C depleted (pelagic) and most δ13C enriched (reef) of the teleosts sampled (trevally (Carangidae) for reef, tuna (Scombridae) for pelagic). Posterior probability distributions indicate model predictions of reliance on a given source with higher values indicating greater reliance
Extending Grime's CSR model to predict plant demographic responses across resource availability gradients: evidence from the Patagonian steppes
<p>Sexual reproduction, growth, and survival are crucial demographic strategies for plant population viability. Here, we propose a conceptual model predicting demographic responses of species based on their ecological strategy and the heterogeneity of environmental conditions within a biogeographical unit and then applied it to a case study from a 5-degree latitudinal gradient in the Patagonian steppes. We also aim to disentangle genetic from environmental effects on demographic responses. We performed <em>in-situ </em>and common garden experiments with two species from six local populations of the Occidental Phytogeographical District of the Patagonian steppes. Species differ in key ecological traits, and thus fit into Grime´s model for evolutionary strategies in plants: one as competitive species and the other as stress-tolerant species. We calculated population growth rate (λ) and performed elasticity analyses to compare the contribution of each demographic strategy to population fitness between species and among local populations distributed along 600 km latitudinal gradient with differences in mean annual precipitation (MAP). We highlight four results. First, the competitive species change from sexual reproduction to growth as MAP increases. Second, the stress-tolerant species relied on growth and survival along the MAP gradient. Third, interannual variation in resource availability modulated demographic responses for both strategies. Fourth, based on the comparison of the <em>in-situ</em> and common garden experiments, we submit that demographic responses were genetically driven. Our study shows that demographic responses can be roughly predicted by the ecological strategy across environmental gradients. We show that differences arise not only between species, but also were genetically driven differences within species among local populations. Scaling up plant-level responses to population-level dynamics allows for a process-based understanding of current and future biogeographical species organization. Furthermore, conservation and restoration efforts should be guided by demographic strategies underlying population viability.</p>
Figure 17 Periglischrus torrealbai, female. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 17 Periglischrus torrealbai, female. A – General view; B – Mediodistal lobe of palpal tibia indicated in red arrow; C – Dorsal plate with proteronotal setae (Pn1–Pn5) and poststigmal seta (Pst) indicated; D – Dorsal opisthosoma with hysteronotal setae (Op3–Op6) indicated
Figure 13 Periglischrus iheringi, protonymph. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 13 Periglischrus iheringi, protonymph. A – General view; B – Dorsal view; C – Ventral view; D – Ventral setae on legs I and II with details; E – Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I withpv anddvsetae, indicated; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A = 200 µm, B–D = 100 µm, E–N = 50 µm.
Figure 10 Periglischrus herrerai, protonymph. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 10 Periglischrus herrerai, protonymph. A – General view; B – Dorsal view; C – Ventral view; D – Ventral setae on legs I and II with details; E – Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I withpv anddvsetae, indicated; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A–N = 50 µm.
Figure 9 Periglischrus herrerai, male. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 9 Periglischrus herrerai, male. A – General view; B – Dorsal view with proteronotal setae (Pn1–Pn5) and poststigmal seta (Pst) indicated; C – Ventral view with sternogenital setaeSt(1–St4) and genital seta (Sg) indicated; D – Ventral setae on legs I and II with details; E Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I with pv anddvsetae, indicated in red arrow; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A =200 µm, B–C, E–M = 50 µm, D and N = 100 µm.
Figure 7 Periglischrus caligus, male. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 7 Periglischrus caligus, male. A – General view; B – Dorsal view with proteronotal setae (Pn1–Pn5) and poststigmal seta (Pst) indicated; C – Ventral view with sternogenital setaeSt(1–St4) and genital seta (Sg) indicated; D – Ventral setae on legs I and II with details; E Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I with pv anddvsetae, indicated in red arrow; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A =200 µm, B, C, E–N = 50 µm, D = 100 µm.
Figure 6 Periglischrus caligus, female. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 6 Periglischrus caligus, female. A – General view; B – Mediodistal lobe of palpal tibia indicated in red arrow; C – dorsal plate
Figure 5 Periglischrus acustisternus, protonymph. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 5 Periglischrus acustisternus, protonymph. A – General view; B – Dorsal view; C – Ventral view; D – Ventral setae on legs I and II with details; E – Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I withpv anddvsetae, indicated; H – Femur–tibia I with proximal ad setae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpd setae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximaladandpd on tarsus IV, indicated. Scale bars: A = 200 µm, C–N = 50 µm, B = 100 µm.
Figure 19 Periglischrus torrealbai, protonymph. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 19 Periglischrus torrealbai, protonymph. A – General view; B – Dorsal view; C – Ventral view; D – Ventral setae on legs I and II with details; E – Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I withpv anddvsetae, indicated; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A = 200 µm, B–D = 100 µm, E–N = 50 µm.
Figure 4 Periglischrus acustisternus, male. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 4 Periglischrus acustisternus, male. A – General view; B – Dorsal view with proteronotal setae (Pn1–Pn5) and poststigmal seta (Pst) indicated; C – Ventral view with sternogenital setaeSt(1–St4) and genital seta (Sg) indicated; D – Ventral setae on legs I and II with details;
Figure 8 Periglischrus caligus, protonymph. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 8 Periglischrus caligus, protonymph. A – General view; B – Dorsal view; C – Ventral view; D – Ventral setae on legs I and II with details; E – Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I withpv anddvsetae, indicated; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A =200 µm, B–N = 50 µm.
Figure 3 Periglischrus acustisternus, female. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 3 Periglischrus acustisternus, female. A – General view; B – Mediodistal lobe of palpal tibia indicated in red arrow; C – Dorsal plate with proteronotal setae (Pn1–Pn5) and poststigmal seta (Pst) indicated; D – Dorsal opisthosoma with hysteronotal setae Op3–Op6) indicated in red arrow; E and F – Sternal plate with sternal setaeSt1(–St4); G – Proximal anterodorsal (ad) seta on femur–tibia I; H – Distal posteroventral (pv) seta on femur–genu I (finely serrated) and tibia–tarsus I (blunt and peglike), indicated in red arrow; I – Proximal antero (ad) and posterodorsalpd () setae on tibia II; J – Proximaladandpd setae on femur II; K – Distal pv setae on femur II (finely serrated) and genu–tarsus II (blunt and peglike), indicated in red arrow; L – Proximaladandpd setae on femur IV; M and N – Posterolateral (pl) setae on femur–tibia IV with details, indicated in red arrow. Scale bars: A = 200 µm, B–C, F, H, J, K–N = 50 µm, D = 50 µm, E, G, I = 100 µm.
Figure 12 Periglischrus iheringi, male. A in DNA barcoding, visual-guide resource, new localities and host associations of genus Periglischrus Oudemans, 1902 (Acari: Mesostigmata, Spinturnicidae) from Minas Gerais, Brazil
Figure 12 Periglischrus iheringi, male. A – General view; B – Dorsal view with proteronotal setae (Pn1–Pn5) and poststigmal seta (Pst) indicated; C – Ventral view with sternogenital setaeSt(1–St4) and genital seta (Sg) indicated; D – Ventral setae on legs I and II with details; E Details ventral setae on leg I; F – Details ventral setae on leg II; G – Coxa I with pv anddvsetae, indicated in red arrow; H – Femur–tibia I with proximal adsetae, indicated; I – Coxa II with posterolateral setapl () indicated; J – Femur II, proximaladandpdsetae, indicated; K – Proximal adandpd on tarsus III, indicated; L – Femur IV, proximaladandpd setae, indicated; M – Genu IV, proximaladandpd setae, indicated; N – Proximal adandpd on tarsus IV, indicated. Scale bars: A = 200 µm, B–D = 100 µm, E–N = 50 µm.
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.