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2,516 results for “Seri”
Reducing the number of accepted species in Aspergillus series Nigri
<p>The <em>Aspergillus</em> series <em>Nigri</em> contains biotechnologically and medically important species. They can produce hazardous mycotoxins, which is relevant due to the frequent occurrence of these species on foodstuffs and in the indoor environment. The taxonomy of the series has undergone numerous rearrangements, and currently, there are 14 species accepted in the series, most of which are considered cryptic. Species-level identifications are, however, problematic or impossible for many isolates even when using DNA sequencing or MALDI-TOF mass spectrometry, indicating a possible problem in the definition of species limits or the presence of undescribed species diversity. To re-examine the species boundaries, we collected DNA sequences from three phylogenetic markers (<em>benA</em>, <em>CaM</em> and <em>RPB2</em>) for 276 strains from series <em>Nigri</em> and generated 18 new whole-genome sequences. With the three-gene dataset, we employed phylogenetic methods based on the multispecies coalescence model, including four single-locus methods (GMYC, bGMYC, PTP and bPTP) and one multilocus method (STACEY). From a total of 15 methods and their various settings, 11 supported the recognition of only three species corresponding to the three main phylogenetic lineages: <em>A</em>. <em>niger</em>, <em>A</em>. <em>tubingensis</em> and <em>A</em>. <em>brasiliensis</em>. Similarly, recognition of these three species was supported by the GCPSR approach (Genealogical Concordance Phylogenetic Species Recognition) and analysis in DELINEATE software. We also showed that the phylogeny based on <em>benA</em>, <em>CaM</em> and <em>RPB2</em> is suboptimal and displays significant differences from a phylogeny constructed using 5 752 single-copy orthologous proteins; therefore, the results of the delimitation methods may be subject to a higher than usual level of uncertainty. To overcome this, we randomly selected 200 genes from these genomes and performed ten independent STACEY analyses, each with 20 genes. All analyses supported the recognition of only one species in the <em>A</em>. <em>niger</em> and <em>A</em>. <em>brasiliensis</em> lineages, while one to four species were inconsistently delimited in the <em>A</em>. <em>tubingensis</em> lineage. After considering all of these results and their practical implications, we propose that the revised series <em>Nigri</em> includes six species: <em>A</em>. <em>brasiliensis</em>, <em>A</em>. <em>eucalypticola</em>, <em>A</em>. <em>luchuensis</em> (syn. <em>A</em>. <em>piperis</em>), <em>A</em>. <em>niger</em> (syn. <em>A</em>. <em>vinaceus</em> and <em>A</em>. <em>welwitschiae</em>), <em>A</em>. <em>tubingensis</em> (syn. <em>A</em>. <em>chiangmaiensis</em>, <em>A</em>. <em>costaricensis</em>, <em>A</em>. <em>neoniger</em> and <em>A</em>. <em>pseudopiperis</em>) and <em>A</em>. <em>vadensis</em>. We also showed that the intraspecific genetic variability in the redefined <em>A</em>. <em>niger</em> and <em>A</em>. <em>tubingensis</em> does not deviate from that commonly found in other aspergilli. We supplemented the study with a list of accepted species, synonyms and unresolved names, some of which may threaten the stability of the current taxonomy.</p>
Taxonomy of Aspergillus series Versicolores: Species reduction and lessons learned about intraspecific variability
<p><em><span class="fontstyle0">Aspergillus </span></em><span class="fontstyle2">series </span><em><span class="fontstyle0">Versicolores </span></em><span class="fontstyle2">members occur in a wide range of environments and substrates such as indoor environments, food, clinical materials, soil, caves, marine or hypersaline ecosystems. The taxonomy of the series has undergone numerous re-arrangements including a drastic reduction in the number of species and subsequent recovery to 17 species in the last decade. The identification to species level is however problematic or impossible in some isolates even using DNA sequencing or MALDI-TOF mass spectrometry indicating a problem in the definition of species boundaries. To revise the species limits, we assembled a large dataset of 518 strains. From these, a total of 213 strains were selected for the final analysis according to their calmodulin (</span><em><span class="fontstyle0">CaM</span></em><span class="fontstyle2">) genotype, substrate and geography. This set was used for phylogenetic analysis based on five loci (</span><em><span class="fontstyle0">benA</span><span class="fontstyle2">, </span><span class="fontstyle0">CaM</span><span class="fontstyle2">, </span><span class="fontstyle0">RPB2</span><span class="fontstyle2">, </span><span class="fontstyle0">Mcm7</span><span class="fontstyle2">, </span><span class="fontstyle0">Tsr1</span></em><span class="fontstyle2">). Apart from the classical phylogenetic methods, we used multispecies coalescence (MSC) model-based methods, including one multilocus method (STACEY) and five single-locus methods (GMYC, bGMYC, PTP, bPTP, ABGD). Almost all species delimitation methods suggested a broad species concept with only four species consistently supported. We also demonstrated that the currently applied concept of species is not sustainable as there are incongruences between single-gene phylogenies resulting in different species identifications when using different gene regions. Morphological and physiological data showed overall lack of good, taxonomically informative characters, which could be used for identification of such a large number of existing species. The characters expressed either low variability across species or significant intraspecific variability exceeding interspecific variability. Based on the above-mentioned results, we reduce series </span><span class="fontstyle0">Versicolores </span><span class="fontstyle2">to four species, namely </span><span class="fontstyle0"><em>A. versicolor</em>, <em>A. creber</em></span><span class="fontstyle2">, </span><span class="fontstyle0"><em>A. sydowii</em> </span><span class="fontstyle2">and </span><em><span class="fontstyle0">A. subversicolor</span></em><span class="fontstyle2">, and the remaining species are synonymized with either </span><span class="fontstyle0"><em>A</em>. <em>versicolor</em> </span><span class="fontstyle2">or </span><span class="fontstyle0"><em>A</em>. <em>creber</em></span><span class="fontstyle2">. The revised descriptions of the four accepted species are provided. They can all be identified by any of the five genes used in this study. Despite the large reduction in species number, identification based on phenotypic characters remains challenging, because the variation in phenotypic characters is high and overlapping among species, especially between </span><span class="fontstyle0"><em>A</em>. <em>versicolor</em> </span><span class="fontstyle2">and </span><span class="fontstyle0"><em>A</em>. <em>creber</em></span><span class="fontstyle2">. Similar to the 17 narrowly defined species, the four broadly defined species do not have a specific ecology and are distributed worldwide. We expect that the application of comparable methodology with extensive sampling could lead to a similar reduction in the number of cryptic species in other extensively studied </span><span class="fontstyle0"><em>Aspergillus</em> </span><span class="fontstyle2">species complexes and other fungal genera.</span></p>
Dataset: Evaluation of post-hoc interpretability methods in time-series classification
<p>This repository contains the dataset, trained models as well as results for the article <em>Evaluation of post-hoc interpretability methods in time-series classification.</em></p> <p>The code to reproduce the results presented in the article is available on <a href="https://github.com/hturbe/InterpretTime">GitHub</a>. More details on the data and results can be found in the article.</p> <p><strong>Files:</strong></p> <ul> <li><strong>datasets.zip: </strong>Include the three datasets used in the article: <ul> <li><strong>ECG: </strong>Processed version of the CPSC dataset from <em>Classification of 12-lead ECGs: the PhysioNet - Computing in Cardiology Challenge 2020.</em></li> <li><strong>fordA: </strong>Dataset from the <a href="https://www.cs.ucr.edu/~eamonn/time_series_data_2018/">UCR Time Series Classification Archive</a></li> <li><strong>synthetic: </strong>Synthetic dataset developed specifically for the purpose of the article</li> </ul> </li> <li><strong>trained_models.zip: </strong>Include CNN, transformer and bi-lstm trained on the three datasets</li> <li><strong>results_paper.zip: </strong>Computed relevance and evaluation metrics for the trained models <ul> <li><strong>model_interpretability: </strong>Include the relevance computed using the different interpretability methods as well as the computed metrics for each method </li> <li><strong>summary_results: </strong>Summary of the evaluation metrics across all interpretability methods for each dataset as well as an excel file summarising the metrics across all datasets.</li> </ul> </li> </ul>
Time Series Analysis of a Daytime Urban Heat Island in Dar es Salaam Metropolitan Areas.
<p>The study aimed to determine the existing linear influence levels of various causative factors of Urban Heat Island (UHI) by using Geographically Weighted Regression Model (GWR) that determines non-stationarity by generating a new equation for each sample size. Urban heat island occurs when higher temperatures reside in urban areas compared to surrounding areas due to the replacement of natural vegetation with construction materials during development purposes. Time Series with Linear Regression was used to determine the linearity between dependent and independent variables. Moderate Resolution Imaging Spectroradiometer (MODIS) products such as MOD11A1, MCD43A1, MOD09A1 and MOD13A1 were used to acquire Land Surface Temperature (LST), Albedo, Indexed-Based Built-Up Index (IBI) and Enhanced Vegetation Index (EVI) respectively. In-situ datasets of the wind speed were acquired from Tanzania Meteorological Agency (TMA). UHI was observed to have a non-linear trend with IBI and wind speed and a linear trend with Albedo and EVI. The strongest values of UHI were observed at the city center, IBI was observed as a leading causative factor by having a non-lineality influence of 0.023 followed by Albedo, wind speed and EVI with an influence of 0.019, 0.016 and -0.015 respectively. Since IBI and Albedo contribute more to the development of heat island, urban residents should be encouraged to use construction materials with a lower absorption rate of solar energy.</p>
STE CECs: Coastal Radon Time-series v1.0
<p><strong>Description: </strong>Radon in coastal water time-series data and accompanying water and meteorological parameters collected from 27 different locations globally. These data were used to train and validate two deep learning models.</p> <p>Data for each study site were provided by authors - please see the "References" sheet in the document for the original source and citation of the data.</p> <p>Associated code can be found here: 10.5281/zenodo.7581389</p> <p>Associated publication accepted (in press) in <em>Water Resources Research.</em></p> <p><strong>Column names in the "data" sheet are as follows:</strong></p> <p>datetime = date and time of measurement (local time, MM/DD/YY HH:MM)</p> <p>Location = name of location of measurement (data = string data)</p> <p>Aquifer_type = categorical aquifer type (data = string data, options: rocky, sandy, or muddy).</p> <p>depth_m = depth of water column (m). Measured with CTD probe or similar.</p> <p>ctdtemp_C = water temperature (degrees Celsius) at point of radon measurement. Measured with CTD probe or similar.</p> <p>ctdsal = water salinity (unitless) at point of radon in water measurement. Measured with CTD probe or similar.</p> <p>windsp_ms = wind speed, 10 m above sea level (units = m/s). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/)</p> <p>airtemp_C = air temperature (units = degrees Celsius). Data from wunderground.com from closest weather station for all sites except for Kīholo Bay, HI, USA (data sourced from RAWS USA Climate Archive, Puu Waawaa station: https://raws.dri.edu/cgi-bin/rawMAIN.pl?hiHPUW) and FSUCML, FL, USA (data sourced from FAWN Carrabell Station: https://fawn.ifas.ufl.edu/data/reports/)</p> <p>Rn_Bqm3 = radon in water (units = Bq/m^3). Measured with Durridge RAD7 or similar radon-in-air detector or underwater gamma spectrometer (e.g., Dulai et al., 2016: https://doi.org/10.1007/s10967-015-4580-9).</p>
Time series analysis of tegument ultrastructure of in vitro transformed miracidium to mother sporocyst of the human parasite Schistosoma mansoni
<p>Here is a compilation of all the Scanning Electron Microscopy pictures at our disposal regarding the in vitro transformation of miracidia to mother sporocysts of <em>Schistosoma mansoni</em>. These datas were partially published in:</p> <p><a href="https://doi.org/10.1016/j.actatropica.2023.106840">https://doi.org/10.1016/j.actatropica.2023.106840</a></p> <p> </p>
Steady-state time series and code
<p>Pressure-flow travelling waves are a key topic for understanding arterial haemodynamics. However, wave trasmission and reflection processes induced by body posture changes have not been thoroughly explored yet.</p> <p>Current <em>in-vivo</em> research has shown that the amount of wave reflection detected at the central level (ascending aorta, aortic arch) decreases during tilting to the upright position, despite the widely-proved stiffening of the cardiovascular system (CVS). Moreover, it is not known whether the optimized configuration of the arterial system typical of the supine condition - i.e., enabled propagation of direct waves vs. trapping of reflected waves, protecting the heart - is preserved or not after posture changes. </p> <p>To shed light on these aspects, we propose a multiscale modelling approach to inquire into posture-induced arterial wave dynamics elicited by simulated head-up tilting. In spite of remarkable remodelling of the human vasculature following posture changes, our analysis shows that: (i) vessels lumens at arterial bifurcations remain well matched in the forward direction; (ii) the reduced wave reflection observed at the central level seems to be caused by backward propagation of weakened pressure waves produced by cerebral autoregulation; and (iii) backward wave trapping is preserved upon orthostatic stress.</p>
GNSS position time series (.neu files) and seismic velocity strcture (Profil_lat2822_Vp_Vs.dat) used in the manuscript
<p>These are the raw GNSS time series files (in .neu format) and seismic velocity strcture file (Profil_lat2822_Vp_Vs.dat) we used in the manuscript. These data are not allowed to use before the manuscript is accepted.</p>
Widespread flood severity index time series for Germany
<p>The dataset provides time series from 1951 to 2013 of monthly flood severity index and mean monthly precipitation derived for Germany (DE) and its three hydroclimatic regions (NW: North-Western region; NE: North-Eastern region; South: Southern region). The dataset also provides most frequent event type recorded in Germany for each months. </p> <p>The dataset was used to train and test a dilated Convolutional Neural Network for forecasting monthly flood severity in Germany (Tarasova et al., submitted to Earth's Future). </p>
Time series of phytoplankton size classes at LTER-MC in the Gulf of Naples (1984-2015)
<p>This database contains the abundance data of four phytoplankton size classes from the Long Term Ecological Research Station MareChiara (st. LTER-MC, 40°48.5’ N, 14°15’ E) over the years 1984-2015. Samples were collected with a Niskin bottle in surface waters, fixed with neutralized formaldehyde (0.8-1.6% final concentration), and counted at the inverted microscope. Linear measurements were taken on a number of phytoplankton specimens for each species and biovolumes were calculated based on species-specific shapes. The species were grouped in size classes (< 5 µm, 5-15 µm, 15-30 µm, > 30 µm) based on their equivalent spherical diameter (ESD).</p>
Time series of environmental data at LTER-MC in the Gulf of Naples (1984-2015)
<p>The present database contains data of mean temperature (TEMP), salinity (PSAL) and chlorophyll <em>a</em> (CHLT) collected in the surface (0-2 m) layer at the LTER-MC station in the Gulf of Naples (Tyrrhenian Sea, western Mediterranean) during the 1984-2015 period. For seven observations, the temperature and salinity values recorded at 5 m were reported owing to missing data at 0 and 2 m depths. The methods used for data collection and analysis are reported in Sabia et al. (2019) (https://doi.org/10.12681/mms.15935).</p>
Load and generation time series for German federal states: Static vs. dynamic regionalization factors (data)
<p>This dataset contains regionalization factors for electricity generation and demand time series in Germany for the years 2019 - 2022. The factors can be used to distribute national generation and demand time series available from SMARD or ENTSO-E to federal state level. The methods underlying the regionalization factors are described in [1], with a focus on the year 2021. However, an extended version of the dataset covering the years 2019-2022 is also included for comprehensive analysis. Moreover, the dataset comprises the corresponding regionalized generation and demand time series at the federal state level of Germany. This time series has been generated using the provided distribution factors for the years 2019-2022 and corresponding generation and demand time series from SMARD [2]. Addtionally, the regionalization methodology for the distributed generation and demand data for the year 2021 has been supplemented with validation data, as described in [1]. This data has been cross-checked against the available SMARD Transmission System Operator (TSO) data. A description of the preprocessing required to obtain the TSO data comparison is provided in a separate .txt file. A PDF document has been prepared, which includes scatter plots that illustrate a comparison between actual and allocated generation per production type or demand data for TSOs on an hourly basis for the year 2021.</p> <p><strong>"static_regionalization_factors.2021[csv, xlsx]"</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Regionalization factors are based on share of generation capacity in each state (generation) or population and GDP (demand).</p> <p><strong>"dynamic_regionalization_factors_2021.[csv, xlsx]"<br> “dynamic_regionalization_factors_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to one factor per federal state and per production type or demand. Each row corresponds to a specific hour of the years 2019 through 2022. Regionalization factors are based on a combination of per unit generation data and share of generation capacity in each state, simulated renewable generation data based on spatio-temporal weather data and distribution of wind and solar generation capacities, and a regionalized load dataset for 2015 [3].</p> <p><strong>“time_series_federal_states_all.[csv, xlsx]”</strong></p> <p>Each column corresponds to the allocated electricity generation or demand per federal state per production type or demand in units of MWh. Each row corresponds to a specific hour of the years 2019 through 2022. The regionalized generation and demand time series has been created by utilizing the dynamic regionalization factors provided in the dataset, in conjunction with the national electricity generation and demand data of Germany as provided by SMARD [2].</p> <p><strong>“TSO_actual.[csv, xlsx]”<br> “TSO_allocated.[csv, xlsx]”</strong></p> <p>Each column corresponds to the spatially aggregated electricity generation per type or demand per TSO in units of GWh. Each row corresponds to one hour of the year 2021. The TSOs in Germany do not hold direct responsibility for individual federal states, but rather for specific regions. In order to assess the validity of the regionalization methodology employed, it was necessary to generate data at the NUTS3 level and subsequently aggregate it to correspond with the relevant TSOs. The data is pre-processed at NUTS3 level and then undergoes the same methodology as outlined in [1]. The preprocessing steps required to map the installed capacity to the TSO level are explained in the accompanying .txt file. The allocated generation and demand data are aggregated to correspond to the TSO level using a shapefile of mapped regions in Germany that correspond to the TSOs [4]. The actual TSO data is generation and demand as published by SMARD [2]. The accompanying PDF presents scatter plots that showcase the actual vs allocated hourly generation types or demand per TSO, expanding on the information provided in the article.</p> <p>[1] M. Sundblad, T. Fürmann, A. Weidlich and M. Schäfer, "<a href="https://arxiv.org/abs/2304.02951">Load and generation time series for German federal states: Static vs. dynamic regionalization factors</a>," <em>2023 Open Source Modelling and Simulation of Energy Systems (OSMSES)</em>, Aachen, Germany, 2023, pp. 1-6, doi: 10.1109/OSMSES58477.2023.10089686.</p> <p>[2] Bundesnetzagentur | <a href="https://www.smard.de/home">SMARD.de</a></p> <p>[3] Matthias Kühnbach, Anke Bekk, and Anke Weidlich (2021). <a href="https://www.forecast-model.eu/forecast-en/content/publications.php">Prepared for regional self-supply? On the regional fit of electricity demand and supply in Germany</a>. Energy Strategy Reviews, 34:100609, 20</p> <p>[4] Frysztacki, Martha Maria. (2023). Mapping of districts to control zones of German Transmission System Operators (TSOs) (v0.1) [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7530196">https://doi.org/10.5281/zenodo.7530196</a></p>
Behavioral responses to a series of similar but distinct novel objects in house sparrows
<p>Generalization refers to the process by which animals assign different stimuli into cognitive categories based on their similarity/dissimilarity to previously experienced stimuli. Stimuli which strongly deviate from previous experience may be avoided due to their novelty (i.e., neophobia), while stimuli which are sufficiently similar to a known/recognized cue may elicit the same response as that cue (i.e., generalization). While generalization has been widely researched, few studies have examined among-individual variance in its expression. Our study quantified among-individual variation in neophobia and generalization in house sparrows (<em>Passer domesticus</em>) by placing a series of different objects next to each subject's food source on successive days and measuring the subject's latency to approach the food source in the presence vs. absence of each object. Similarly to previous work, we found that house sparrows are neophobic on average and exhibit significant among-individual variance in neophobia. More interestingly, we found that the neophobic response declined across presentations of different novel objects. This implies that the sparrows generalized some aspect(s) of the objects. We also found significant among-individual variance in the rate at which approach latency changed across this series, possibly reflecting individual differences in propensity to generalize. These results raise new questions about how neophobia, habituation, and generalization are linked and about the potential for selection to act on these traits under different ecological conditions.</p>
Transition and Drivers of Elastic to Inelastic Deformation in the Abarkuh Plain from InSAR Multi-Sensor Time Series and Hydrogeological Data
<p>This repository contains the datasets used in <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JB026430">Mirzadeh et al., 2023</a>. It includes three InSAR time-series datasets from the Envisat descending orbit, ALOS-1 ascending orbit, and Sentinel-1A in ascending and descending orbits, acquired over the Abarkuh Plain, Iran, as well as the geological map of the study area and the GNSS and hydrogeological data used in this research.</p> <p>Dataset 1: Envisat descending track 292</p> <ul> <li>Date: 06 Oct 2003 - 05 Sep 2005 (12 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_LOD_tropHgt_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: ALOS-1 ascending track 569</p> <ul> <li>Date: 06 Dec 2006 - 17 Dec 2010 (14 acquisitions)</li> <li>Processor: ISCE/stripmapStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>Dataset 2: Sentinel-1 ascending track 130 and descending track 137</p> <ul> <li>Date: 14 Oct 2014 - 28 Mar 2020 (129 ascending acquisitions) + 27 Oct 2014 - 29 Mar 2020 (114 descending acquisitions)</li> <li>Processor: ISCE/topsStack + MintPy</li> <li>Displacement time-series (in HDF-EOS5 format): timeseries_ERA5_ramp_demErr.h5</li> <li>Mean LOS Velocity (in HDF-EOS5 format): velocity.h5</li> <li>Mask Temporal Coherence (in HDF-EOS5 format): maskTempCoh.h5</li> <li>Geometry (in HDF-EOS5 format): geometryRadar.h5</li> </ul> <p>The time series and Mean LOS Velocity (MVL) products can be georeferenced and resampled using the makTempCoh and geometryRadar products and the MintPy commands/functions.</p>
In-situ time series collected in the Patagonian fjords.
<p>The dataset included the time series of Conservative temperature, Absolute Salinity, Dissolved Oxygen, and Marine Current published in this manuscript.</p>
Exploratory Analysis of Top 50 Companies in Indian Stock Market: A Time Series Analysis of Historical Stock Prices
<p>The dataset consists of 'open, close, high, low, close, adj close and volume' columns for the top 50 Indian Companies and the dataset been fetched from YahooFinance for over 20 years. </p>
Source data for VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images publication
<p>Source data used to create figures in <em>VALIS: Virtual Alignment of pathoLogy Image Series for multi-gigapixel whole slide images</em> (Nature Communications, 2023)</p>
Coasts for Kids - Series Trailer
<p>[For more information and resources please visit www.coastsforkids.com] Coasts for Kids is a collaborative experience between children and their parents, coastal scientists, community artists, teachers, animators, and coastal managers. The series is aimed at kids aged 6 and over. It includes important concepts in coastal processes and coastal evolution in Episodes 1, 2, and 3, followed by analyses of how people affect the coast in Episode 4 and what we can do about it in Episode 5. Join us to explore coastal areas and the many things we enjoy from them! [The Coasts for Kids Series is currently available for free downloading from the Southport Eco Centre (UK) and the Coast and Ocean Collective (NZ)].</p>
Data and code example for the article: "Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification"
<p>Data needed to reproduce the figures of <a href="https://arxiv.org/abs/2305.05881">https://arxiv.org/abs/2305.05881</a> and a simple code example of a quantum-convex-classical neural network used to train a sine versus cosine classification problem.</p>
Simulated and real datacubes for developing and testing changepoint algorithms for spatially correlated, short and noisy time-series
<p>A set of datacubes where z-dimension is time, thus each (x,y,.) is a timeseries. The idea is to detect sudden changes in each series, assuming 1) the series can be quite short and noisy 2) the change occurs in spatial patches. The set has synthetic examples with known change-events, and a real-world dataset with unknown change-events. The data files are related to the pape</p> <p>T Rajala, P Packalen, M Myllymäki, A Kangas (2023): Improving detection of changepoints in short and noisy time-series with local correlations: Connecting the events in pixel neighbourhoods, "Journal of Agricultural, Biological and Environmental Statistics", https://doi.org/10.1007/s13253-023-00546-1</p> <p>More of the NFI data is available from Natural Resources Institute Finland, https://kartta.luke.fi/index-en.html</p> <p>See `data/00data_readme.txt` for further details.</p> <p> </p>
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.