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52 results for “trend models.”
Accompanying data for the paper "Making Sense of Wildlife Habitat Use on Active Oil Sands Mines: Quasi-experiments, Occupancy Models, Trends Assessments, and Upland Habitat Reclamation"
<p>This data set contains both the raw species detection records and the derived occupancy model data used to assess usage patterns for the nine species of wildlife. Data have been anonymized by using non-identifying company and lease names. These attributes are not required to reproduce the results in this paper and was done per contractual requirements between LGL Limited and its clients.</p> <p>Data is currently being reviewed by the client and will be shared publicly once final approval has been received.</p>
Replication data for: "Revisiting the 'East African Paradox': CMIP6 models also struggle to reproduce strong observed Long Rain drying trends."
<h3><strong>Intro</strong></h3> <p>This repository contains replication data for Schwarzwald, Kevin and Richard Seager (2024), "'Revisiting the “East African Paradox': CMIP6 models also struggle to reproduce strong observed MAM long rain drying trends." (under revision at Journal of Climate). This includes the data necessary to replicate all main text figures and most figures in Supplementary Materials. Additional figures in Supplementary Materials require raw precipitation time series, detailed below. This repository also includes a copy of the code necessary to replicate the entire study; the latest version of the code, in addition to instructions on how to use it, is kept at <a href="https://github.com/ks905383/gha_trends">this GitHub archive</a>. </p> <h3><strong>Structure</strong></h3> <p>The repository is structured as follows: </p> <ul> <li><code>code</code>: Static / stable version of reproduction code for Schwarzwald and Seager (2024) that uses and creates these data (see <a href="https://github.com/ks905383/gha_trends">here</a> for more detailed instructions)</li> <li><code>figures</code>: Static / stable versions of main and supplemental figures for Schwarzwald and Seager (2024).</li> <li><code>climate_raw</code>: "raw" (often pre-processed) climate data files; only some files are included, see below for more details</li> <li><code>climate_proc</code>: Processed climate data files upon which the analysis is based, used by main and supplemental figure code</li> <li><code>aux_data</code>: Certain auxiliary data files (fonts, critical values) and intermediate files for long code processes. Created and used by the replication code. </li> </ul> <h3><strong>Notes on raw climate data</strong></h3> <p>Due to space limitations (and a desire to not create yet another cloud copy of CMIP6 data), this repository only contains processed CMIP6 data: the calculated linear trends of rainfall, sea surface temperatures, and 500 hPa geopotential height used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://aims2.llnl.gov/search/cmip6/">ESGF</a>, and consists of every available CMIP6 monthly rainfall, sea surface temperature (SST), and geopotential height file on the archive at the time of processing for the experiments detailed in the manuscript. For precipitation, only a bounding box (-3 S to 12.5 N, 32 E to 55 E) around East Africa was downloaded, and saved with the file suffix "_HoAfrica" (see replication code README for more details). One example precipitation file (one ensemble member of ACCESS-CM2 historical precipitation) is included in this repository for reference. </p> <p>Similarly, this repository only contains processed NMME data: calculated linear trends of rainfall used in the analysis of Schwarzwald and Seager (2024). The raw data used to create these files can be downloaded from the <a href="https://iridl.ldeo.columbia.edu/SOURCES/.Models/.NMME/">IRI Data Library</a> and consists of every available monthly rainfall hindcast / forecast file from the NMME archive for the models used (see manuscript Table S1). As above, only a bounding box around East Africa was downloaded, and files were standardized to a partial CMIP* file format (one file per variable, with CMIP file and variable name conventions, but with forecast lead time as an additional dimension). Preprocessing is a bit more extensive than for CMIP6 models: first, hindcasts and forecasts were concatenated into a single file (since hindcasts are saved in the archive only up to the time when the model was operationalized), and then reindexed such that the "time" variable refers to the time <em>for which the forecast is made</em>, and not the time <em>at</em> which the forecast is made. One example precipitation file (CanCM4i rainfall hindcasts/forecasts) is included in this repository for reference. </p> <p>This repository does, however, contain preprocessed copies of the "raw" gridded observational rainfall data products used (in addition to the processed trends), since harmonizing and standardizing the data into a format easily compatible with the CMIP6 data was a nontrivial amount of work that would be tedious to replicate. For the ten gridded observational data products used, monthly rainfall was brought into the CMIP* file format (one file per variable, with CMIP file and variable name conventions), with one notable exception: all observational rainfall is saved in units of mm/day. </p> <p>Reanalysis and gridded ocean observations can be downloaded from each product's respective repositories. As before, the code assumes the data have been preprocessed into something akin to the CMIP* file format. </p> <h3><strong>Notes on processed climate data</strong></h3> <p>Note that the repository includes a file (<code>aux_data/pr_doyavg_CHIRPS_historical_seasstats_dunning_19810101-20141231_HoAfrica.nc</code>) containing CHIRPS seasonal characteristics in East Africa, created as part of Schwarzwald et al., 2023, <em>Climate Dynamics</em>. This file is primarily used to set the boundaries of the study region, see the manuscript for details of how it was calculated. </p> <h3><strong>Licensing, citing, questions</strong></h3> <p>These data are offered under a CC 4.0 license, which allows redistribution and reuse as long as they are correctly cited; note that for much of these data (especially for "raw" data), this requires citations to the original creators. </p> <p>For questions, please feel free to reach out to corresponding author Kevin Schwarzwald. </p>
Artifacts for the 2023 Trends in Functional Programming Publication: Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture
<p>This dataset contains the artifacts for the performance evaluation conducted in the publication <em>Versatile and Flexible Modelling of the RISC-V Instruction Set Architecture</em> which will be published in the proceedings of the 2023 <em>Trends in Functional Programming</em> conference. The provided artifacts contain a <a href="https://docker.io">Docker</a> container for executing <a href="https://embench.org">Embench</a> benchmarks with LibRISCV, RISC-V VP, Grift and Forvis. A pre-built version of the container is included.</p>
Climate trends and behavior of a model Amazonian terrestrial insectivore, Black-faced Antthrush, indicate adjustment to hot and dry conditions
Open the record for dataset details and reuse information.
Time series of annual TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models on a 2 x 2 deg grid. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the CMIP5 future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in a separate file. </p> <p>Each file contains the time series of the 40-year linear trend for tas at each grid point from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
Time series of Area mean TAS 40-year trend from historical to future in CMIP5 model simulations
<p>Time series of 40-year linear trend for the Arctic (ARC) and the Eastern Arctic (eARC) mean annual near surface air temperature (tas) for the period 1979-2100 as simulated by the CMIP5 models. The data for the period 1979-2005 are taken from the CMIP5 historical simulations, while data for 2006-2100 are from the future scenario rcp2.6, rcp4.5 and rcp8.5, respectively. The 40 year trend of 1979-2018 from the ERA-Interim reanalysis is also provided in separate files. The Arctic is defined as the area north of 70 N, and the eastern Arctic is defined as 0 - 180 E and north of 70 N. Each file contains the time series of the 40-year linear trend for the respective area mean tas from historical to one of the three scenarios simulated by one model. The linear trend is calculated using cdo, and the year associated with each data point in a file corresponding to the last year in the 40-year time period, e.g, the associated year 2018 corresponds to the linear trend calculated for the period 1979-2018. Unit: C/year.</p>
The Trend of DH Papers and Patent Applications for 3D modeling
<p>it is a figure of paper "A Probe into Patentometrics in Digital Humanities".</p>
Google Trends time series for the term "topic modeling"
<p>Dataset received from Google Trends for the phrase "topic modeling'' on 31 January 2024 using the URL <a href="https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de">https://trends.google.de/trends/explore?date=all&q=topic\%20modeling&hl=de</a></p> <p><em>Data obtained from Google LLC, which is the ultimate owner of these data. Published for academic and non-commercial replication purposes only.</em></p>
Rewired connectome of an SSCX model with inhibitory targeting based on trends found in MICrONS data
<p>This is a rewired connectome of the internal synaptic connectivity of the model deposited under <a href="../records/7930275" target="_blank" rel="noopener">https://zenodo.org/records/7930275</a>.</p> <p>In the original model, local connectivity is based on axo-dendritic overlaps combined with a pruning rule, which together are known to be able to recreate biological trends in excitatory subnetworks. By comparison with the <a href="https://www.microns-explorer.org/cortical-mm3" target="_blank" rel="noopener">MICrONS</a> dataset we found that the inhibitory trends characterized by <a href="https://www.biorxiv.org/content/10.1101/2023.01.23.525290v3" target="_blank" rel="noopener">Schneider-Mizell et al. (2023)</a> can be reproduced with some additional, relatively simple pruning rules (see accompanying <a href="https://www.biorxiv.org/content/10.1101/2022.08.11.503144" target="_blank" rel="noopener">anatomy publication</a>), and created a rewired version of the connectome in <a href="https://github.com/AllenInstitute/sonata" target="_blank" rel="noopener">SONATA</a> format (edges.h5) that reproduces these trends. We refer to this as the Schneider-Mizell compatible SM-connectome in the accompanying <a href="https://www.biorxiv.org/content/10.1101/2023.05.17.541168" target="_blank" rel="noopener">physiology publication</a>.</p> <p>Note that for technical reasons, all <em>afferent_segment_...</em> and <em>efferent_...</em> synapse properties (which are not required for running simulations) were set to zero in the rewired pathways.</p> <p><strong>[Update 08/05/2024 - v3]:</strong> Conductances of individual synapses were recalibrated to preserve pathway-specific reference PSP amplitudes. The <em>afferent_section_type</em> and <em>efferent_section_type</em> synapse properties were previously off by 1 and were fixed. Missing synaptic input compensation was recalibrated to obtain target firing rates and added to this release.</p> <p><strong>[Update 28/02/2024 - v2]:</strong> Source m-type L1_NGC-SA included</p>
Data for "Revisiting the reanalysis-model discrepancy in Southern Hemisphere winter storm track trends"
<p>The dataset supporting the conclusion of the submitted paper is uploaded here.</p> <p>The data are labeled after each figure. The npz files include data required to reproduce our results in python arrays.</p>
Abundance Trend Indicator - Models, Prediction, Stacked Environmental Data and Training Set Similarity
<p># Readme</p> <p>These trained models can be used to predict the abundance trends of New Zealand's forest species and can be used together with the code in https://github.com/lnilya/abundance-trend-indicator</p> <p>Since the process of using the models requires coding expertise and some setting up, please make sure to reach out to ilya.shabanov@vuw.ac.nz for any questions. All files will require the code in the repository to be read and used. </p> <p>If you want to explore the results generated with these models, please visit https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ for a user-friendly, interactive UI.</p> <p>## Contents</p> <p>_models: Contains the trained models (Artificial Neural Network (ANN), Random Forest (RF), SVMW (Support vector machine) and GLM (logistic regression)) at different degrees of noise filtering, different datasets and variable sets. The model files also contain test and training scores. To load the files please refer to the readme in the code repository: ttps://github.com/lnilya/abundance-trend-indicator</p> <p><br>_predictions/_environment: Contains the predictor variables for the study area (New Zealand, 1950-2019) that are needed by the models to make predictions. </p> <p>_predictions/_similarity: Contains the masks of areas that can be predicted by models and are similar to the training set.</p> <p>_predictions/_ati: Contain the predicted results for the abundance trend. These can be explored on https://ati-nz-predictions-7e6f3d514735.herokuapp.com/ </p> <p> </p>
Data from: A novel growth model evaluating age-size effect on long-term trends in tree growth.
Open the record for dataset details and reuse information.
List of papers reviewed to uncover trends in the use of model systems in infectious disease ecology & evolutionary biology
Open the record for dataset details and reuse information.
Data of the ER 2020 Publication: Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis
<p>Related Publication:</p> <p>Härer, Felix, Fill, Hans-Georg (2020): Past Trends and Future Prospects in Conceptual Modeling - A Bibliometric Analysis. Accepted for: 39th International Conference on Conceptual Modeling, ER 2020.</p> <p>Contents:</p> <ul> <li>The directory <em>Descriptive Analysis</em> contains all publications of the analysis database used for the descriptive analysis.</li> <li>The directory <em>Bibliometric Analysis</em> contains the NLP and analysis processes for RapidMiner with stopwords and synonyms.</li> </ul>
Supplementary material 1 from: Ancin-Murguzur FJ, Hausner VH (2020) Research gaps and trends in the Arctic tundra: a topic-modelling approach. One Ecosystem 5: e57117. https://doi.org/10.3897/oneeco.5.e57117
Supplementary table 1
Data from: An integrated assessment model of seabird population dynamics: can individual heterogeneity in susceptibility to fishing explain abundance trends in Crozet wandering albatross?
1. Seabirds have been incidentally caught in distant-water longline fleets operating in the Southern Ocean since at least the 1970s, and breeding numbers for some populations have shown marked trends of decline and recovery concomitant with longline fishing effort within their distributions. However, lacking is an understanding of how forms of among-individual heterogeneity may interact with fisheries bycatch and influence population dynamics. 2. We develop a model that uses comprehensive data on the spatial and temporal distributions of fishing effort and seabird foraging to estimate temporal overlaps, fishery catchability and consequent bycatch. We apply a population model that is structured by age, sex, life stage and spatially to Crozet Island wandering albatross and explore how heterogeneity in susceptibility to capture may have influenced the population's demography over time. 3. A model where some birds were assumed to be more susceptible to fisheries bycatch was able to successfully replicate the observed trend in breeding pairs. Considerably poorer fits were found without this assumption. Results suggested that the more susceptible birds may have been removed from the population by the 1990s. 4. The model was also able to highlight areas, times and fleets prone to increased bycatch. Knowledge of these factors should assist fisheries and conservation management bodies to quantify and reduce seabird bycatch through spatial management and fleet-specific mitigation efforts. 5. Synthesis and application. Many seabirds show complex life histories that make them highly susceptible to additional incidental mortality from fishing vessels. By applying a population model that integrates key aspects of seabird and fishery dynamics, we were able to explain the observed trends in the breeding population of Crozet wandering albatross and identify key areas and fleets where further mitigation may be required. In addition, the potential removal of a category of birds that shows increased susceptibility to capture has important implications for the conservation management of this population and other iconic species incidentally caught by large-scale commercial fisheries.
Supplementary material 4 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Geographic partitioning of the SOR
Supplementary material 1 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Criterion selection of the TOP-100 IAS
Supplementary material 7 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
The short description of invasive range of IAS in Russia
Supplementary material 3 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Species native range, introduction year, occurrence records
ScienceDex guides
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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.