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50 results for “temporal trends”
Data repository for "Spatio-temporal trends of Holocene peat carbon accumulation in China: climatic and human drivers"
<p>Dating results collected from peatlands in China are used to calculate the spatiotemporal trends of the Holocene peat accumulation rate (PAR) and net carbon balance (NCB), including all original dating, calculated intermediate results, and final composite results. This file includes a total of 14 tables (Supplementary Tables S1-S14).</p>
Data from: The spatial distribution and temporal trends of livestock damages caused by wolves in Europe
<p>The preprint of the corresponding manuscript can be found here: doi: https://doi.org/10.1101/2022.07.12.499715</p> <p>Wolf populations are recovering and expanding across Europe, causing conflicts with livestock owners. We here compiled incident-based livestock damage data caused by wolves across 21 European countries for the years 2018, 2019 and 2020.</p> <p>The file "<strong>wolf_damages_2018_2019_2020_complete_data_to_publish.csv</strong>" contains the following information per incident: country, target species, cause, number of animals killed/injured/missing, assessment level probability, reported date, number of days until inspection, location, incidentID, uniqueID, NUS1_ID, NUTS2_ID, NUTS3_ID, damage prevention measure, number of wolves attacking, latitude, longitude, comments, metadata constraints.</p> <p>The file "<strong>nuts3_regions_and_LC_where_wolves_are_present.csv</strong>" contains information of the percentage of area occupied by wolves per NUTS3 region for selected land cover variables.</p> <p>The file "<strong>prevention_measures.csv</strong>" contains information about the financial support of livestock damage prevention measure per country or NUTS region</p> <p>The file "<strong>wolf_presence_now_vs_50_years_ago_nuts3.csv</strong>" contains information on NUTS3 regions that had a documented wolf presence 50 years ago.</p> <p>The "<strong>scripts_to_publish.zip</strong>" folder contains the scripts that we used to conduct the analyses.</p>
Temporal Patterns and Trends in Corporate Donations Using PageRank and Node Similarity Graph Algorithm
<p>Corporate donations wield considerable influence within political arenas, shaping policies and influencing decision-making processes. This study uses Neo4j, an advanced graph database tool, to explore a comprehensive company dataset, focusing on unraveling temporal patterns and evolving trends in corporate contributions. Visual representations, such as bar charts, reveal significant fluctuations in donations, indicating potential cyclic patterns occurring every six years. The study explores intricate relationships between donor entities and recipients, highlighting diverse donation patterns—both focused and widespread. The study's derived PageRank scores offer a comprehensive portrayal of the varying degrees of influence among diverse entities receiving donations within the network. Notably, the Conservative and Unionist Party emerges as the most prominent entity, boasting a striking score of 1.86, indicating a substantial influx of financial support likely to significantly shape its political endeavors. Despite a lower score of 0.62, the Labor Party still signifies a noteworthy level of financial backing, albeit less extensive than its counterpart. In contrast, the Liberal Democrats, The In Campaign Ltd, and Network for Animals Ltd exhibit comparatively restrained financial backing, warranting deeper investigation into the factors affecting their funding. Moreover, undisclosed findings regarding 170 similarity scores using Node Similarity algorithm disclose a prevalent similarity trend among entities, notably observed between Company 1 and Company 2, implying potential synergistic partnerships in donation-related endeavors. This high similarity often indicates shared values, highlighting prospects for collaborative initiatives or partnerships to augment positive impacts. Utilizing these insights supports the formulation of targeted donation strategies, circumventing donation redundancies, and ensuring optimal resource allocation for maximal societal benefit within specified sectors.</p> <p>Keywords—Company Dataset, Corporate Donations, Neo4j, Node Similarity, PageRank, Political Influence </p> <p> </p>
Spatial and temporal thermal trends of the lower Kuskokwim and Aniak Rivers, AK, USA
<h4><span>Adequate environmental data describing freshwater conditions (particularly temperature) that may influence Chinook salmon populations (which have had record low returns in recent years) are lacking. This ongoing project is using a combination of site-based temperature logger data and satellite imagery (MODIS and Landsat 8) to model continuous temperature data at two different scales, with the aim to identify thermal freshwater refugia for juvenile and adult salmon in the main Kuskokwim and Aniak Rivers. Adopting a standard for thermal data collection protocols for the Kuskokwim basin and important spawning tributaries could be used to understand changing temperature regimes, effects of temperature on salmon population dynamics, and identifying critical thermal refugia, all of which are important for managing subsistence fisheries. Our project seeks to employ a methodology having sufficient rigor that it can identify thermal regimes in critical habitats and at broad spatial and temporal scales, while also being cost effective. Modeling efforts are currently underway. Mean daily temperature data collected by all temperature loggers in the mainstem Kuskokwim and mainsteam Aniak Rivers, as well as floodplain habitats of the Aniak River (including beaver ponds, orthofluvial and parafluvial springbrooks) are provided here. Predicted temperatures and data from imagery will be published online when the project manuscript is published.</span></h4>
Temporal trends in the spatial bias of species occurrence records
<p>Large-scale biodiversity databases have great potential for quantifying long-term trends of species, but they also bring many methodological challenges. Spatial bias of species occurrence records is well recognized. Yet, the dynamic nature of this spatial bias - how spatial bias has changed over time - has been largely overlooked. We examined the spatial sampling bias of species occurrence records within multiple biodiversity databases in Germany and tested whether spatial bias in relation to land cover or land use (urban and protected areas) has changed over time. We focused our analyses on urban and protected areas as these represent two well-known correlates of sampling bias in biodiversity datasets. We found that the proportion of annual records from urban areas has increased over time while the proportion of annual records within protected areas has not consistently changed. Using simulations, we examined the implications of this changing sampling bias for estimation of long-term trends of species' distributions. When assessing biodiversity change, our findings suggest that the effects of spatial bias depend on how it affects sampling of the underlying land-use change drivers affecting species. Oversampling of regions undergoing the greatest degree of change, for instance near human settlements, might lead to overestimation of the trends of specialist species. For robust estimation of the long-term trends in species' distributions, analyses using species occurrence records may need to consider not only spatial bias, but also changes in the strength of spatial bias through time.</p>
Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies (datasets and R scripts)
<p>This digital archive is an outcome of the paper Kolář J., Macek M., Tkáč P., Novák D. & V.Abraham: Long-term demographic trends and spatio-temporal distribution of past human activity in Central Europe: Comparison of archaeological and palaeoecological proxies. Quaternary Science Reviews, 2022</p>
Linked collectors and determiners for: Danish Tingidae - Data for study on occurrence, temporal trends and potential species.
Natural history specimen data linked to collectors and determiners held within, "Danish Tingidae - Data for study on occurrence, temporal trends and potential species". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/a968d7e9-5391-482d-8927-4b34b9863d56">https://bionomia.net/dataset/a968d7e9-5391-482d-8927-4b34b9863d56</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/a968d7e9-5391-482d-8927-4b34b9863d56">https://gbif.org/dataset/a968d7e9-5391-482d-8927-4b34b9863d56</a>. Formatted as a Frictionless Data package.
Temporal trends of surface water area in India's rivers and basins
<p>This dataset quantifies the extent and annual rate of change in surface water area (SWA) in India's rivers and basins over a period of 30 years from 1991 to 2020. Visit <a title="Surface Water Trends - India" href="https://sites.google.com/view/surface-water-trends-india/" target="_blank" rel="noopener">Surface Water Trends - India</a> for an interactive web interface to explore these results, and for additional data and information.</p> <p>It is derived from the <a href="https://global-surface-water.appspot.com/" target="_blank" rel="noopener">Global Surface Water Explorer</a> which maps terrestrial surface water globally using historical Landsat satellite imagery. (Pekel, J. et al., Nature 540, 418-422 (2016). (doi:10.1038/nature20584)). The data files contain zipped archives of shapefiles and CSV (comma separated values) files.</p> <p>Shapefiles are one for each season (dry, wet and permanent) and scale (river basin and reach) of our analysis, and contain annual trends in surface water area. To open and explore them in a GIS software (eg. QGIS), un-ZIP them and include them as vector datasets.</p> <p>CSV files are one for each scale (river basin and reach (transect)) of our analysis, and contain time series of surface water areas from 1991 to 2020. To open and explore them, for analysis or to explore in a table editing software, un-ZIP them and read them in.</p> <p>Refer to 00_README.txt for details on feature and table attributes in the files. </p>
Temporal trends in the spatial bias of species occurrence records
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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 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>
Geographic variation and temporal trends in ice phenology in Norwegian lakes during a century
<p>The physical characteristics of ice formation in water courses are ideal for studying climatic variation in space and time. We used a large set of observations for 9-116 years (1890-2019) of the timing of freeze-up and break-up, and the length of ice-free season for 99 Norwegian lakes to elucidate variation in ice phenology across space and time. The dataset of Norwegian lakes is unusual, covering considerable variation in altitude (4 – 1401 m above sea level) and climate (varying from oceanic to continental) within a large latitudinal and longitudinal gradient (58.2 – 69.9 °N; 4.9 – 30.2 °E).</p> <p>The average date of ice break-up occurred later in spring with increasing altitude and latitude, but earlier in spring with increasing longitude. The average date of freeze-up and the length of the ice-free period decreased significantly with altitude and longitude. No correlation with distance from the ocean was detected. The geographical gradients are related to regional climate due to adiabatic processes (altitude), solar radian (latitude) and the degree of continentality (longitude). There was a significant lake area effect as small lakes froze-up earlier due to less volume. There was also a significant trend that lakes were completely frozen over later in the autumn in recent years. After accounting for the effect of long-term trends in the large-scale NAO index, a significant but weak trend over time for earlier ice break-up was detected. An understanding of the relationship between ice phenology and geographical parameters is a prerequisite for predicting the potential consequences of climate change on ice phenology. Changes in ice phenology will have consequences for the behaviour and life cycle dynamics of the aquatic biota.</p>
Synthetic temporal dataset for temporal trend analysis and retrieval
<p>This repository contains a synthetic, temporal data set that was generated by the authors by sampling values from the Gaussian distribution. The dataset contains eight nontemporal dimensions, a temporal dimension, and a numerical measure attribute. The data set was generated according to the scheme and procedure detailed in this source paper: Kaufmann, M., Fischer, P.M., May, N., Tonder, A., Kossmann, D. (2014). TPC-BiH: A Benchmark for Bitemporal Databases. In: Performance Characterization and Benchmarking. TPCTC 2013. Lecture Notes in Computer Science, vol 8391. Springer, Cham. https://doi.org/10.1007/978-3-319-04936-6_2. The data set can be used for analyzing and locating temporal trends of interest, where a temporal trend is generated by selecting the desired values of the nontemporal dimensions, and then selecting the corresponding values of the temporal dimension and the numerical measure attribute. Locating temporal trends of interest, e.g., unusual trends, is a common task in many applications and domains. It can also be of interest to understand which nontemporal dimensions are associated with the temporal trends of interest. To this end, the data set can be used for analyzing and locating temporal trends in the data cube induced by the data set.</p>
Temporal and regional trends of antibiotic use in long-term aged care facilities across 39 countries, 1985-2019: systematic review and meta-analysis
<p><b>Background</b></p> <p>Antibiotic misuse is a key contributor to antimicrobial resistance and a concern in long-term aged care facilities (LTCFs). Our objectives were to: i) summarise key indicators of systemic antibiotic use and appropriateness of use, and ii) examine temporal and regional variations in antibiotic use, in LTCFs (PROSPERO registration CRD42018107125).</p> <p><b>Methods & Findings</b></p> <p>Medline and EMBASE were searched for studies published between 1990-2021 reporting antibiotic use rates in LTCFs. Random effects meta-analysis provided pooled estimates of antibiotic use rates (percentage of residents on an antibiotic on a single day [point prevalence] and over 12 months [period prevalence]; percentage of appropriate prescriptions). Meta-regression examined associations between antibiotic use, year of measurement and region. </p> <p>A total of 90 articles representing 78 studies from 39 countries with data between 1985-2019 were included. Pooled estimates of point prevalence and 12-month period prevalence were 5.2% (95% CI: 3.3-7.9; n=523,171) and 62.0% (95% CI: 54.0-69.3; n=946,127), respectively. Point prevalence varied significantly between regions (Q=224.1, df=7, p<0.001), and ranged from 2.4% (95% CI: 1.7-2.7) in Eastern Europe to 9.0% in the British Isles (95% CI: 7.6-10.5) and Northern Europe (95% CI: 7.7-10.5). Twelve-month period prevalence varied significantly between region (Q=15.1, df-3, p=0.002) and ranged from 53.9% (95% CI: 48.3-59.4) in the British Isles to 68.3% (95% CI: 63.6-72.7) in Australia. Meta-regression found no association between year of measurement and antibiotic use prevalence. The pooled estimate of the percentage of appropriate antibiotic prescriptions was 28.5% (95% CI: 10.3-58.0; n=17,245) as assessed by the McGeer criteria. Year of measurement was associated with decreasing appropriateness of antibiotic use over time (OR: 0.78, 95% CI: 0.67-0.91). The most frequently used antibiotic classes were penicillins (n=44 studies), cephalosporins (n=36), sulphonamides/trimethoprim (n=31), and quinolones (n=28). </p> <p><b>Conclusions</b></p> <p>Coordinated efforts focusing on LTCFs are required to address antibiotic misuse in LTCFs. Our analysis provides overall baseline and regional estimates for future monitoring of antibiotic use in LTCFs. </p>
Data from: Monitoring temporal and spatial trends of illegal and legal fishing in Canada's marine conservation areas using vessel tracking datasets
<p>Expansion of marine conservation areas (CA) necessitates resource-efficient and achievable strategies for monitoring and evaluation of ongoing fishing activity at national levels. To demonstrate and explore such a strategy, we conducted the first extensive analysis of fishing activity within Canada's static, geographically defined marine CAs with fishing regulations (n = 264 areas). We used eight years of Automatic Identification System data to estimate fishing effort across three oceans and conducted temporal and spatial comparisons specific to each CA's regulations and enactment date. We addressed questions on CA effectiveness, fishing displacement, fishing the line behavior, and relationships between fishing activity and spatial CA attributes. We estimated 22,000 hours of fishing activity within CAs after enactments, 22% of which was identified as illegal. CA effectiveness appeared to be lowest for Atlantic CAs based on illegal fishing effort density within CAs. Fishing displacement and fishing the line was generally not apparent as buffer areas around CAs tended to already have higher fishing effort prior to enactments. CA effectiveness and responses to CAs varied considerably, as was visualized using timeseries plots and maps developed for each CA. Our evaluation of a nation's full suite of CAs provides managers with a foundation and approach for continued monitoring and reporting.</p>
Temporal trends fish size spectra
<p>Analysis of the temporal trends of fish size spectra in stream communities across France. This releases is a brief overview of the main findings of the manuscript "Modulating effects of human pressures on climate warming-induced changes in size spectra of stream fish communities" published in Nature Ecology and Evolution</p>
Beyond 'greening' and 'browning': trends in grassland ground cover fractions across Eurasia that account for spatial and temporal autocorrelation
<p class="MsoNormal">Grassland ecosystems cover up to 40% of the global land area and provide many ecosystem services directly supporting the livelihoods of over 1 billion people. Monitoring long‑term changes in grasslands is crucial for food security, biodiversity conservation, achieving Land Degradation Neutrality goals, and modelling the global carbon budget. Although long-term grassland monitoring using remote sensing is extensive, it is typically based on a single vegetation index and does not account for temporal and spatial autocorrelation, which means that some trends are falsely identified while others are missed. Our goal was to analyze trends in grasslands in Eurasia, the largest continuous grassland ecosystems on Earth. To do so, we calculated Cumulative Endmember Fractions (annual sums of monthly ground cover fractions) derived from MODIS 2002–2020 time series and applied a new statistical approach PARTS that explicitly accounts for temporal and spatial autocorrelation in trends. We examined trends in green vegetation, non-photosynthetic vegetation, and soil ground cover fractions considering their independent change trajectories and relations among fractions over time. We derived temporally-uncorrelated pixel-based trend maps and statistically tested whether observed trends could be explained by elevation, land cover, SPEI3, climate, country, and their combinations, all while accounting for spatial autocorrelation. We found no statistical evidence for a decrease in vegetation cover in grasslands in Eurasia. Instead, there was a significant map-level increase in non‑photosynthetic vegetation across the region and local increases in green vegetation with a concomitant decrease in soil fraction. Independent environmental variables affected trends significantly, but effects varied by region. Overall, our analyses show in a statistically robust manner that Eurasian grasslands have changed considerably over the past two decades. Our approach enhances remote‑sensing‑based monitoring of trends in grasslands so that underlying processes can be discerned.</p>
Ecological and life-history traits predict temporal trends in biomass of boreal moths
<p> Dramatic insect declines, and their consequences for ecosystems globally, have received considerable attention recently. Yet, it is still poorly known if ecological and life-history traits can explain declines and whether insect decline occurs also at high latitudes. Insects' diversity and abundance are dramatically lower at high latitudes compared to the tropics, and insects might benefit from climate warming in high-latitude environments. </p> <p>We adopted a trait- and biomass-based approach to estimate temporal change between 1993 and 2019 in Finnish macro-moth communities by using data from 85 long-running light traps. We analysed spatio-temporal variation in biomass of moth functional groups with Joint Dynamic Species Distribution Models while accounting for environmental variables. </p> <p>We did not detect any declining trends in total moth biomass of moth functional groups, and most groups were stable over time. Moreover, biomass increased for species using coniferous trees, lichens, or mushrooms as hosts, multivoltine species, as well as monophagous and oligophagous species feeding on trees. We found that length and temperature of the growing season, winter climatic conditions, and habitat structure all partially explained variation in moth biomass.</p> <p>Although boreal moth communities are rapidly changing due to species turnover, in terms of total biomass they seem to contradict the trend of dramatic insect declines observed globally. This may lessen the immediate possibility of negative bottom-up trophic cascades in boreal food webs.</p>
Temporal trends in untreated stormwater quality
<p>This repository contains the excel file (NSQD+.xlsx) which has data from the National Stormwater Quality Database (NSQD) and more recent sources as well as two R files. These R files were used to analyze the NSQD+ in section 3.1 of the manuscript and create and analyze the NSQD++ in section 3.2 of the manuscript.</p>
Temporal Trends of Thrombolysis Treatment in Chinese Acute Ischemic Stroke (AIS) Patients From 2007-2017: Analysis of China National Stroke Registry (CNSR) I, II, and III; CTP-Draft Review Performed;
ClinicalTrials.gov study NCT04290494. IPD Sharing: YES. Countries: 1. Publications: 0.
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.