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30,131 results for “control”
Lower American River restoration snorkel surveys at the project and control reaches
Sacramento Water Forum will implement spawning and rearing habitat enhancement projects on the Lower American River at the Lower Sailor Bar, Nimbus Basin, and Upper River Bend reaches. Enhancements include installation of gravel to restore over 18 acres of spawning habitat and in-channel/floodplain grading to create over 14 acres of rearing habitat. Construction of Lower Sailor Bar and Nimbus Basin were completed in summer 2022 and Upper River Bend was completed in summer 2023.The goal of the projects is to increase existing spawning and rearing habitat for salmonids under typical flows. This work supports effectiveness monitoring for this project, including spawning and rearing (snorkel) surveys before and after restoration. This work also informs performance metrics and adaptive management strategies.
Lower American River restoration spawning surveys at project and control reaches (2022 - 2024)
Sacramento Water Forum has implemented spawning and rearing habitat enhancement projects on the Lower American River at the Lower Sailor Bar, Nimbus Basin, Upper River Bend and Lower River Bend reaches. Enhancements include installation of gravel to restore over 28 acres of spawning habitat and in-channel/floodplain grading to create over 44 acres of rearing habitat. Construction of Lower Sailor Bar and Nimbus Basin were completed in summer 2022, Upper River Bend was completed in summer 2023, and Lower River Bend was completed in summer 2024. The goal of the projects is to increase existing spawning and rearing habitat for salmonids under typical flows. This work supports effectiveness monitoring for this project, including spawning and rearing (snorkel) surveys before and after restoration. This work also informs performance metrics and adaptive management strategies.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. A 100 yr old thermal erosion event under control conditions.
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for a 100yr old TEF response to N fertilization over 25 years.
Long term response of arctic tussock tundra to thermal erosion features: A modeling analysis. Tussock tundra control simulation
The Multiple Element Limitation (MEL) model is used to simulate the recovery of Alaskan arctic tussock tundra to thermal erosion features (TEFs) caused by permafrost thaw and mass wasting. TEFs could be significant to regional carbon (C) and nutrient budgets because permafrost soils contain large stocks of soil organic matter (SOM) and TEFs are expected to become more frequent as climate warms. These simulations deal only with recovery following TEF stabilization and do not address initial losses of C and nutrients during TEF formation. To capture the variability among and within TEFs, we simulate a range of post-stabilization conditions by varying the initial size of SOM pools and nutrient supply rates. This file contains the results for 25 years of tussock tundra under control conditions.
Plant biomass in mesic acidic tussock tundra, 1998 15Nitrogen controls, Toolik, Alaska.
Five or six quadrats (20cm x 20cm squares) along a line (block) were collected for plant biomass in mesic tussock tundra. In the lab each quadrat was separated into individual species, new and old aboveground and belowground biomass.
Litterfall and Hare Pellet Summary at Bonanza Creek LTER Control Plots (1985 - Present)
Litterfall weights and Hare Pellet counts at LTER Control Plots within the Bonanza Creek Experimental Forest and the Caribou Poker Creek Research Watershed.
Learning, Inhibitory Control, and Perception
Open the record for dataset details and reuse information.
Cortical myelin measured by the T1w/T2w ratio in individuals with depressive disorders and healthy controls
Open the record for dataset details and reuse information.
Smartphone sensor data (accelerometer, virtual keyboard) collected in-the-wild by Parkinson's Disease patients and Healthy Controls
<p>For detailed description of the dataset see the relevant <a href="https://www.nature.com/articles/s41598-020-78418-8">journal article</a>.</p> <p>Python code for model inference and training is available <a href="https://github.com/alpapado/deep_pd">here</a>.</p> <p> </p> <p><strong>DESCRIPTION</strong></p> <p>The dataset contains accelerometer recodings and keyboard typing data contributed by Parkinson's Disease patients and Healthy Controls. Accelerometer data consists of acceleration values recorded during phone calls and typing data consist of virtual keyboard press and release timestamps. The dataset is divided into two parts: the first part, called SData, contains data from a small, medically evaluated, set of users, while the second part, called GData, contains recordings from a large body of users with self-reported PD labels.</p> <p>The dataset is organized into 5 pickle files:</p> <p>1. <strong>imu_sdata.pickle</strong>: Contains the tri-axial accelerometer recordings for the SData part of the dataset in the form of a list of python dictionaries, one for each participating subject. Accelerometer data have been pre-processed to a sampling frequency of 100Hz and come segmented into non-overlapping 5 second windows. Hence, a segment's dimension will be 500 x 3 samples.</p> <p>Sample Python code for accessing the acceleration data of a subject</p> <pre><code class="language-python">sdata = pickle.load(open('imu_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] # subject_data is a list of length 4 ## The actual data is in the last element of the list acc_segments = subject_data[-1] num_acc_sessions_for_subject = len(acc_segments) acc_segments_for_first_session = acc_segments[0] acc_segments_for_second_session = acc_segments[1] # ..etc In: print(acc_segments_for_first_session.shape) Out: (3, 500, 3) ## The first accelerometer session for this subject consists of 3 five-second segments. In: print(acc_segments_for_second_session.shape) Out: (8, 500, 3) ## The second accelerometer session for this subject consists of 8 five-second segments.</code></pre> <p>2. <strong>imu_gdata.pickle</strong>: Same layout as imu_sdata.pickle but with data ffrom GData subjects.</p> <p>3. <strong>typing_sdata.pickle</strong>: This files contains the typing data originating from the SData part of the dataset. It is a list of dictionaries with one entry per subject. The typing data are given in the form of concatenated hold time (the time elapsed between press and release of the virtual key) and flight time (the time between releasing a key and press the next) histograms, computed over 10ms bins in the range of [0, 1]s for hold time and [0, 4]s for flight time (an additional bin that contains the values in the (1, +oo) and (4, +oo) intervals is also used). So, the total length of the concatenated histogram is 1000/10 + 1 + 4000/10 + 1 = 502.</p> <p>Sample Python code for accessing the typing data of a subject:</p> <pre><code class="language-python">sdata = pickle.load(open('typing_sdata.pickle', 'rb')) subject_list = list(sdata.keys()) ## Data for first subject subject_data = sdata[subject_list[0]] ## The actual data is in the first element of the list typing_histograms = subject_data[0] num_typing_sessions_for_subject = len(typing_histograms) typing_hist_for_first_session = typing_histograms[0] typing_hist_for_second_session = typing_histograms[1] # ..etc In: print(typing_hist_for_first_session.shape) Out: (502, ) ht_hist = typing_hist_for_first_session[:101] # Hold time histogram of the session ft_hist = typing_hist_for_first_session[101:] # Flight time histogram of the session</code></pre> <p>4. <strong>typing_gdata.pickle</strong>: Same layout as typing_sdata.pickle but with data from GData subjects.</p> <p>5. <strong>subject_metadata.pickle</strong>: A list of dictionaries with one entry per subject containing demographic information. The relevant demographic fields have the following interpretation:<br> 'age': Year of birth,<br> 'gender_id': 0 indicates male, 1 indicates female<br> 'healthstatus_id': 0 indicates PD patient, 1 indicates Healthy with PD family history, 2 indicates Healthy without PD family history</p> <p>In the case of SData subjects, there is also symptom UPDRS scores from one or two medical examinations. These are ncoded in the fields med_eval_1 and med_eval_2.</p> <p> </p> <p><strong>ETHICS & FUNDING</strong></p> <p>The study during which the present dataset was collected is a multi-center study approved in each country available (for more info visit: <a href="http://www.i-prognosis.eu/?page_id=3606">http://www.i-prognosis.eu/?page_id=3606</a>). Informed consent, including permission for third-party access to pseudo-anonymised data, was obtained from all subjects prior to their engagement with the study. The work has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No 690494 - i-PROGNOSIS: Intelligent Parkinson early detection guiding novel supportive interventions (<a href="http://www.i-prognosis.eu/">i-prognosis.eu</a>).</p> <p> </p> <p><strong>CORRESPONDANCE</strong></p> <p>Any inquiries regarding this dataset should be adressed to:</p> <p>Mr. Alexandros Papadopoulos (Electrical & Computer Engineer, PhD candidate)</p> <p>Multimedia Understanding Groupmug<br> Department of Electrical & Computer Engineering<br> Aristotle University of Thessaloniki<br> University Campus, Building C, 3rd floor<br> Thessaloniki, Greece, GR54124</p> <p>Tel: +30 2310 996359, 996365 <br> Fax: +30 2310 996398<br> E-mail: alpapado@mug.ee.auth.gr</p> <p> </p> <p><br> </p> <p> </p>
Dataset of "Sensitivity analysis in photodynamics: How the electronic structure controls cis-stilbene photodynamics?"
<p>The techniques of computational photodynamics are increasingly employed to unravel reaction mechanisms and interpret experiments. However, inaccuracies in nonadiabatic dynamics can lead to misinterpretations, particularly when calculated observables exhibit low sensitivity to the underlying dynamics. This issue is exemplified in the photochemistry of cis-stilbene, where similar experimental outcomes have been differently interpreted based on the electronic structures supporting nonadiabatic dynamics. This study examines the predictions of cis-stilbene photochemistry using trajectory surface hopping methods coupled with various electronic structures (OM3-MRCISD, SA2-CASSCF, XMS-SA2-CASPT2, and XMS-SA3-CASPT2) and assesses their ability to interpret experimental observations. Although the excited-state lifetimes show consistency, ranging from 360 fs to 295 fs, the reaction quantum yields vary significantly. The quantum yield for cyclization ranges from nearly zero to 35% while the photoisomerization channel can either exceed 50% or be entirely suppressed completely in the second case. Intriguingly, the calculated photoelectron signal is not strikingly different for different reaction scenarios, making the methods seemingly reliable when treated separately Furthermore, analyzing stationary points on the potential energy surface does not reliably predict simulation outcomes, nor does it aid in selecting a specific method before simulations. Therefore, we advocate for incorporating sensitivity analyses in the simulation protocol. While employing an ensemble of methods is impractical, nonadiabatic simulations with external bias present a resource-efficient approach to achieve this goal.</p>
DAS Control over the spatial correlation of silica perforations in thin films as a function of solution conditions
<p><span>Dataset production context : A perforated silica layer with structural correlation is engineered using sol-gel chemistry, applied to large-scale flat and curved sur-faces. The anion(s) used in the preparation give tailored spatial correlation, and control over perforation size and density. Surface structuration is rapidly and reproducibly created using water and salts as inexpensive and ecofriendly reagents.</span></p>
Data for Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes
<p>This dataset contains the experimental data used in the figures of the paper "Reducing leakage of single-qubit gates for superconducting quantum processors using analytical control pulse envelopes" by E. Hyyppä, A. Vepsäläinen, ..., and J. Heinsoo published in PRX Quantum 5, 030353 (2024): https://doi.org/10.1103/PRXQuantum.5.030353.</p> <p>The data is stored mostly as csv-files, the contents of which are explained in the readme-files. Each subfolder corresponds to one figure of the paper and also contains a Jupyter Notebook for plotting the data. The subfolders S1-S10 correspond to the supplementary figures, i.e., figures 6-15 in the Appendix of the paper.</p> <p>Furthermore, we provide a Jupyter notebook in the folder Code_to_plot_FAST_and_HD_DRAG_pulses/ that provides Python functions for evaluating and plotting the proposed FAST DRAG and HD DRAG pulses in time domain and frequency domain. Please cite our paper if you use the Python code for your published research.</p> <p>The notebooks have been tested using the following Python package versions<br>Python 3.11<br>scipy 1.14.1<br>numpy 2.1.0<br>matplotlib 3.9.2</p>
Four lipidomics datasets (mouse liver, mouse pancreatic islets, mouse soleus muscle and mouse visceral adipose tissue), generated for the publication Mehl et al., "A multiorgan map of metabolic, signalling, and inflammatory pathways that coordinately control fasting glycemia in mice"
<p>Mehl, Thorens et al present a multiomics study aimiing to<span> identify the pathways that are coordinately regulated in pancreatic </span><span>b</span><span>-cells, muscle, liver, and fat to control fasting glycemia we fed C57Bl/6, DBA/2 and Balb/c mice a regular chow or a high fat diet for 3, 10 and 30 days. We measured fasted glycemia, insulinemia and whole-body insulin resistance. Transcriptomic and lipidomic analysis were used in a data fusion approach to identify organ-specific pathways related to the glycemic levels across all conditions investigated. In pancreatic islets, constant insulinemia despite higher glycemic levels were associated with reduced expression of mRNAs encoding hormone and neurotransmitter receptors as well as OXPHOS, cadherins, integrins and gap junction proteins. Higher glycemia and whole-body insulin resistance were associated, in muscle, with reduced expression of mRNAs encoding insulin signaling proteins and enzymes of the glycolysis, Krebs’ cycle and OXPHOS pathways, as well as endocytosis and exocytosis proteins; in hepatocytes, with lower expression of mRNAs of the insulin signaling pathway, of branched chain amino acid catabolism and of OXPHOS; in adipose tissue, with increased expression of mRNAs of innate immunity and lipid catabolism. These data provide a map of the pathways that are coordinately recruited in the investigated tissues to control fasting glycemia and a resource for further studies of interorgan communication in glucose homeostasis. </span></p>
Extended data for the paper: "SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters"
<p>Extended data 1 to 4 for the software article:<br>SentemQC - A novel and cost-efficient method for quality assurance and quality control of high-resolution frequency sensor data in fresh waters. </p> <p>The extended data is tables and a Figure output and input from/to SentemQC runs relevant for the SentemQC paper.</p>
SERENA EJPSOIL PL EROSION CONTROL SOIL MASS NOT ERODED
<p>General description of SERENA</p> <p>The internal EJP SOIL project SERENA contributed to the evaluation of soil multifunctionality aiming at providing assessment tools for land planning and soil policies at different scales. By co-working with relevant stakeholders, the project provided co-developed indicators and associated cookbooks to assess and map them, to report both on soil degradation, soil-based ecosystem services and their bundles, under actual conditions and for climate and land-use changes, at the regional, national, and European scales.</p> <p>Files description</p> <p>Data was prepared as a result of SERENA EJP SOIL. The attached files are a part of the analysis of Assessment of Soil Threats and Ecosystem Services from each MS with the harmonized procedures. SERENA deliverable 3.3 (https://doi.org/10.5281/zenodo.13991087). The RUSLE method was used to prepare the attached files. </p>
Dataset from: "Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention"
<p>Dataset for Henare, D. T., Kadel, H., & Schubö, A. (2020). Voluntary Control of Task Selection Does Not Eliminate the Impact of Selection History on Attention. <em>Journal of Cognitive Neuroscience</em>, <em>32</em>(11), 2159-2177. <a href="https://doi.org/10.1162/jocn_a_01609">https://doi.org/10.1162/jocn_a_01609</a></p>
Dissolved Cr concentration and stable isotope data presented in "Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ53Cr distributions in the ocean interior" (Janssen et al., 2021, EPSL).
<p>This dataset presents all of the dissolved Cr data included and discussed in “Release from biogenic particles, benthic fluxes, and deep water circulation control Cr and δ<sup>53</sup>Cr distributions in the ocean interior” (Janssen et al., 2021, EPSL). Three primary datasets are included:</p> <ol> <li>Dissolved [Cr], [Cr(III)] and d53Cr in samples from shipboard particle regeneration incubations conducted in the subantarctic Southern Ocean.</li> <li>Dissolved [Cr] in porewater samples from a sediment core collected in the Tasman Sea in primarily calcareous sediments, along with [Cr] and δ<sup>53</sup>Cr in overlying bottom waters.</li> <li>3. A compilation of intermediate and deep water dissolved [Cr] and δ<sup>53</sup>Cr from seawater samples from the Southern, Pacific and Atlantic Oceans</li> </ol>
Look-Up Table of A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase
<p>Tabulated values (look-up table) of the magnitude (dB) and phase (degres) of the unit-cell versus voltage, experimentally characterized, for a Reconfigurable Intelligent Surface prototype based on varactors described in :</p> <p>R. Fara, P. Ratajczak, D. -T. Phan-Huy, A. Ourir, M. Di Renzo and J. de Rosny, "A Prototype of Reconfigurable Intelligent Surface with Continuous Control of the Reflection Phase," in IEEE Wireless Communications, vol. 29, no. 1, pp. 70-77, February 2022, doi: 10.1109/MWC.007.00345.</p> <p>also accessible here: https://arxiv.org/ftp/arxiv/papers/2105/2105.11862.pdf</p>
City of Seattle, Seattle Public Utilities, Knotweed Control 2010-current, Cedar River Municipal Watershed, King County, WA
The City of Seattle’s Cedar River Municipal Watershed is managed to support and supply clean drinking water to the greater Seattle area. The watershed covers 91,000 acres, hosts a rich diversity of plants, animals, and habitats, and is owned by the City of Seattle. In 1999, the noxious weed knotweed was detected in the watershed and control was limited to manual and mechanical methods due to an herbicide moratorium intended to prevent broadcast spraying of roadside vegetation. These manual control methods were largely unsuccessful and impractical for controlling acres of knotweed growing in remote locations of the watershed. In 2010, Seattle Public Utilities passed the first ordinance through Seattle City Council to approve the limited use of imazapyr to control knotweed in the watershed for three years. Successive knotweed ordinances have been passed, allowing for herbicide control of knotweed in the watershed from 2010-current. The reports included in this package are provided to Seattle City Council annually as a requirement of the ordinance. The data table provides the control history since the passage of the first ordinance and includes information about patch size and amount of imazapyr used. This data package will be updated annually with ongoing control efforts.
Efficacy and fate of fluridone applications for control of invasive submersed aquatic vegetation in the estuarine environment of the Sacramento-San Joaquin Delta
We conducted a study in the Sacramento-San Joaquin Delta to determine efficacy of the widely used herbicide fluridone in an estuarine ecosystem. The primary goal of SAV removal was restoration of open water habitat for endangered Hypomesus transpacificus (Delta Smelt). Over 18 months and multiple sets of multi-week fluridone applications, we monitored concentrations of fluridone and responses by SAV across pairs of treated and reference sites. Fluridone concentrations in the water were generally below the 2-5 parts per billion required for SAV control. Monitoring demonstrated these low water concentrations were likely due to dissipation by tides, despite use of pelleted fluridone formulations marketed for flowing water environments. Fluridone did, however, accumulate in sediment at concentrations hundreds of times higher than those measured in the water. Nonetheless, we did not observe lasting reductions in SAV abundance or changes in SAV community composition. By demonstrating lack of efficacy of one of the few herbicides permitted for use in this estuary, this study highlights the need for development of SAV management tools tailored to the challenges of hydrologically complex environments like estuaries.
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.