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.
911
datasets available to search
ShareScore release 0.9.0
Dataset results
911 results for “Temporal data”
Data from: Evaluating temporal and spatial transferability of a tidal inundation model for foraging waterbirds
Open the record for dataset details and reuse information.
Data from: Is temporal synchrony necessary for effective Batesian mimicry?
Open the record for dataset details and reuse information.
Data from: Dissecting factors behind temporal trends in the timing of breeding in two songbird species – evolutionary change or phenotypic plasticity?
Open the record for dataset details and reuse information.
Data for: Temporal variations in female moose responses to roads and logging in the absence of wolves
Open the record for dataset details and reuse information.
Data from: When beggars are choosers-How nesting of a solitary bee is affected by temporal dynamics of pollen plants in the landscape
Open the record for dataset details and reuse information.
Data from: DCDC2 READ1 regulatory element: how temporal processing differences may shape language
Open the record for dataset details and reuse information.
Data from: Exponential history integration with diverse temporal scales in retrosplenial cortex supports hyperbolic behavior
Open the record for dataset details and reuse information.
Data and software for: Temporal novelty detection and multiple timescale integration drive Drosophila orientation dynamics in temporally diverse olfactory environments
Open the record for dataset details and reuse information.
Data from: The temporal window of ecological adaptation in postglacial lakes: a comparison of head morphology, trophic position and habitat use in Norwegian threespine stickleback populations
Open the record for dataset details and reuse information.
Data from: Temporal dynamics of selection on early-life phenotypic plasticity in seasonal migration versus residence
Open the record for dataset details and reuse information.
Data from: Fate of atmospherically deposited NH4+ and NO3- in two temperate forests in China: temporal pattern and redistribution
Open the record for dataset details and reuse information.
Data from: Predicting fitness in future climate: Insights from temporally replicated field experiments in Arabidopsis thaliana
Open the record for dataset details and reuse information.
Data from: Temporal distribution of endophytic and exophytic insect guilds responds to host plant phenology in the Brazilian Savannah
Open the record for dataset details and reuse information.
Data from: Spatio-temporal trends in richness and persistence of bacterial communities in decline-phase water vole populations
<p><strong>ABSTRACT</strong><br> Understanding the driving forces that control vole population dynamics requires identifying bacterial parasites hosted by the voles and describing their dynamics at the community level. To this end, we used high-throughput DNA sequencing to identify bacterial parasites in cyclic populations of montane water voles that exhibited a population outbreak and decline in 2014-2018. An unexpectedly large number of 155 Operational Taxonomic Units (OTUs) representing at least 13 genera in 11 families was detected. Individual bacterial richness was higher during declines, and vole body condition was lower. Richness as estimated by Chao2 at the local population scale did not exhibit clear seasonal or cycle phase-related patterns, but at the vole meta-population scale, exhibited seasonal and phase-related patterns. Moreover, bacterial OTUs that were detected in the low density phase were geographically widespread and detected earlier in the outbreak; some were associated with each other. Our results demonstrate the complexity of bacterial community patterns with regard to host density variations, and indicate that investigations about how parasites interact with host populations must be conducted at several temporal and spatial scales: multiple times per year over multiple years, and at both local and long-distance dispersal scales for the host(s) under consideration.</p> <p><strong>FILE DESCRIPTION:</strong></p> <p><strong>Trapping, physical, and demographic data for the 1376 <em>Arvicola terrestris</em> included in sequencing runs 1 to 8</strong></p> <p>This XLSX file contains the following information concerning the 1376 animals included in the eight sequencing runs: location, session numbering, animal_id, trap_name, capture_date, species, sex (1=male, 2=female), weight (g), length_body (mm), length_tail (mm, testes (0=abdominal, 1=scrotal), testes_length (mm), testes_width (mm), nipples (0=small, 1=lactating), vagina (0=not perforate, 1=perforate), pub_symph (0=closed, 1=open), uterus_scars (#), embryos(#), lens_weight (g), lens_weight2 (g) and sequencing labels</p> <p>File name: Animal_details.xlsx</p> <p> </p> <p><strong>Information concerning the <em>Arvicola terrestris</em> samples and the positive and negative controls multiplexed in the 16Sv4 MiSeq sequencing runs 1 to 8</strong></p> <p>This XLSX file contains the Run IDs, Sample IDs, Sample types, Dates & Site names, DNA extraction kit, PCR IDs, PCR replicate numbers, numbers of reads before and after filtering and the fastq file names for the 6615 PCR products multiplexed in the eight different Illumina MiSeq runs.</p> <p>File name: Sample_and_sequencing_informations.xlsx</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run1)</strong></p> <p>This ZIP file contains the Run1 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1570 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run1.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run2)</strong></p> <p>This ZIP file contains the Run2 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1668 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run2.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run3)</strong></p> <p>This ZIP file contains the Run3 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1646 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run3.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run4)</strong></p> <p>This ZIP file contains the Run4 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1712 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run4.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run5)</strong></p> <p>This ZIP file contains the Run5 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1680 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run5.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run6)</strong></p> <p>This ZIP file contains the Run6 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1704 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run6.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run7)</strong></p> <p>This ZIP file contains the Run7 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1620 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run7.zip</p> <p> </p> <p><strong>MiSeq raw sequences of the 16Sv4 rRNA gene from <em>Arvicola terrestris</em> samples (Run8)</strong></p> <p>This ZIP file contains the Run8 FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each <em>Arvicola terrestris</em> sample using the MiSeq platform. The 1630 multiplexed PCR products were indexed using both forward and reverse indices. The list of the multiplexed samples and positive & negative controls are provided in the following XLSX file titled: Sample_and_sequencing_informations.xlsx.</p> <p>File name: MiSeq_Reads_16S_Arvicola_terrestris_Run8.zip</p> <p> </p> <p><strong>Raw abundance table of the 16v4 rRNA gene from <em>Arvicola terrestris samples before data filtering (Run1 to 8)</em></strong></p> <p>This CSV file contains the number of reads for each distinct variant (OTU) and each of the 6615 PCR products, including the <em>Arvicola terrestris</em> samples and the controls, sequenced in the MiSeq runs 1 to 8 before the data filtering.</p> <p>File name: Read_abundance_table_before_filtering.csv</p> <p> </p> <p><strong>Abundance table of the 16v4 rRNA gene from <em>Arvicola terrestris</em> samples after data filtering (Run1 to 8)</strong></p> <p>This CSV file contains the number of reads for each distinct variant (OTU) and each <em>Arvicola terrestris</em> sample sequenced in the MiSeq runs 1 to 8 after the data filtering.</p> <p>File name: Read_abundance_table_after_filtering.csv</p>
Data from: Detailed temporal mapping of global human modification from 1990 to 2017
<p>Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 2000, 2010, 2015, and 2017 to evaluate temporal and spatial trends of land use modification of terrestrial lands (excluding Antarctica). Our novel datasets are detailed (0.09 km2 resolution), temporally consistent (for 1990-2015), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset for ~2017 with 14 stressors for an even more comprehensive dataset. Also provided is a land/water mask to support subsequent analyses.</p> <p>Please also be sure to check your spam folder if you do not receive an email with the link from Dryad, which is provided because of the large file size.</p>
Data from: Zebrafish retinal ganglion cells asymmetrically encode spectral and temporal information across visual space
<p>In vertebrate vision, the tetrachromatic larval zebrafish permits non-invasive monitoring and manipulating of neural activity across the nervous system in vivo during ongoing behaviour. However, despite a perhaps unparalleled understanding of links between zebrafish brain circuits and visual behaviours, comparatively little is known about what their eyes send to the brain via retinal ganglion cells (RGCs). Major gaps in knowledge include any information on spectral coding, and information on potentially critical variations in RGC properties across the retinal surface corresponding with asymmetries in the statistics of natural visual space and behavioural demands. Here, we use in vivo two photon (2P) imaging during hyperspectral visual stimulation as well as photolabeling of RGCs to provide a functional and anatomical census of RGCs in larval zebrafish. We find that RGCs' functional and structural properties differ across the eye and include a notable population of UV-responsive On-sustained RGCs that are only found in the acute zone, likely to support visual prey capture of UV-bright zooplankton. Next, approximately half of RGCs display diverse forms of colour opponency including many that are driven by a pervasive and slow blue-Off system - far in excess of what would be required to satisfy traditional models of colour vision. In addition, most information on spectral contrast was intermixed with temporal information. Taken together, our results suggest that zebrafish RGCs send a diverse and highly regionalised time-colour code to the brain.</p>
Data from: Overlap of spatial and temporal spawning distributions of spring and summer Chinook Salmon results in hybridization in the upper Columbia River
<p>The upper Columbia River in Washington State (main-stem and tributary habitat between McNary and Chief Joseph dams) is inhabited by two major lineages of Chinook Salmon (<i>Oncorhynchus tshawytscha</i>); endangered spring Chinook Salmon and summer Chinook Salmon which are not ESA listed. The lineages are highly genetically divergent from one another and historically spatial and temporal isolating mechanisms maintained these genetic differences. Both lineages occur in the Entiat River, a system where anthropogenic activity has changed habitat, flows, species composition, and the distribution of the two lineages over the past century. We examined the spatial and temporal overlap in spawning distributions between Entiat River spring and summer Chinook Salmon and we used genetic markers to assess the level of introgression between lineages. Redd surveys were conducted from 2003 to 2017 to describe spatial and temporal spawning patterns of both lineages. We genotyped sub-yearling juvenile Chinook Salmon captured in the Entiat River from 2009–2014 at 90 SNP loci to determine lineage and hybridization status. There was temporal overlap in spawning between lineages in several years and considerable spatial overlap in redd locations annually. Genetic analysis revealed hybridization between lineages does occur, albeit at relatively low rates (2.6% of sub-yearling juveniles genotyped). We detected hybrids each year samples were collected and they were distributed throughout the Entiat River basin. Hybridization between lineages of Chinook Salmon could result in introgression and a loss of genetic diversity between the lineages, and/or, a loss of production by ESA-listed spring Chinook Salmon. The presence of hybrids warrants concern for ESA-listed spring Chinook Salmon in both the Entiat River system and throughout the upper Columbia River basin.</p>
Data from: Patterns of annual and seasonal immune investment in a temporal reproductive opportunist
Historically, investigations of how organismal investments in immunity fluctuate in response to environmental and physiological changes have focused on seasonally breeding organisms that confine reproduction to seasons with mild environmental conditions and abundant resources. The red crossbill, <i>Loxia curvirostra</i>, is a songbird that can breed opportunistically if conifer seeds are abundant, on both short, cold, and long, warm days, providing an ideal system to investigate interactions between immunity, reproduction, and environmental fluctuations. In this study, we measured inter- and intra-annual variation in complement, natural antibodies, PIT54, and leukocytes in crossbills across four summers (2010-2013) and multiple seasons within one year (summer 2011-spring 2012). Overall, we observed substantial changes in crossbill immune investment among summers, with interannual variation driven largely by food resources, while seasonal variation was less pronounced and lacked a dominant predictor of immune investment. However, we found weak evidence that physiological processes (e.g., reproductive condition, moult) or abiotic factors (e.g., temperature, precipitation) affect immune investment. Collectively, this study suggests that a reproductively flexible organism may simultaneously invest in both reproduction and survival-related processes, potentially by exploiting rich patches with abundant resources. More broadly, these results emphasize the need for more longitudinal studies of trade-offs associated with immune investment.
Anonymized and aggregated temporal data on the number of research papers coauthored by Slovenian researchers
<p>The dataset includes: raw aggregated Slovenian researcher network data (available at http:((www.sicris.si), Benford law distribution conformity tests. Scripts for handling the data and Benford conformity tests.</p>
Data from: A temporally intensive survey of bacterial communities of Brassica napus genotypes grown in three environments
Soil bacterial communities play vital roles in nutrient cycling and plant health. Breeding staple crops to have more robust microbiomes may be a sustainable way to improve crop yield without increasing inputs, leading to better global food security. We collected root and rhizosphere soil samples from sixteen genotypes of canola weekly for ten weeks at one site in 2016 and at three time points across three sites in 2017. We sequenced the 16S ribosomal RNA gene generating a total of 127.7 million reads. The data shows that rhizosphere communities are more diverse than corresponding root communities. Beta diversity analysis demonstrates both temporal and site-to-site differences in community structure. Using this dataset, these and other aspects of the canola microbiome characterization can be explored to advance our understanding of genotype by environment interactions This is a large temporally and spatially rich dataset, which will further our understanding of bacterial communities associated with canola. These data will be used in a variety of other projects, with the goal of enhancing agricultural sustainability.
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.