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4,210 results for “memory”
Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures
<p><span>Characterisation dataset for “</span><span>Resistive switching memories with enhanced durability enabled by mixed-dimensional perfluoroarene perovskite heterostructures”</span><span>, DOI:</span><span>10.1039/d4nh00104d</span><span>. Data for main and supporting figures provided as *.xlsx and *.txt files.</span></p> <p> </p> <p> </p> <p> </p>
Data set of "Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"
<p>The dataset of all data presented in the article published in the virtual special issue of the Journal of Physical Chemistry Letters:</p> <p>"Capacitive and Inductive Characteristics of Volatile Perovskite Resistive Switching Devices with Analog Memory"</p> <p>DOI: <a title="DOI URL" href="https://doi.org/10.1021/acs.jpclett.4c00945">https://doi.org/10.1021/acs.jpclett.4c00945</a></p> <p> </p> <p>The dataset contains the following raw data:</p> <p>## FILE DESCRIPTION<br>--------------<br>### Figure 2<br>- Fig2a.txt : Representative characteristic _I-V_ response of memristor (5 cycles)<br>- Fig2b.txt : Upper vertex-dependent multilevel/multistate analog resistive switching<br>- Fig2c.txt : Characteristic _I-V_ response of 20 distinct devices<br>- Fig2d.txt : Endurance measurements for 1000 cycles of the LRS (ON state) and HRS (OFF state)</p> <p>### Figure 3<br>- Fig3a.txt : Characteristic _I-V_ response with an upper vertex of 0.25 V<br>- Fig3b.txt : Characteristic _I-V_ response with an upper vertex of 0.75 V<br>- Fig3c.txt : Characteristic _I-V_ response with an upper vertex of 1.25 V</p> <p>### Figure 4<br>- Fig4a.txt : IS spectrum under dark conditions at 0 V<br>- Fig4b.txt : IS spectrum under dark conditions at 0.2 V<br>- Fig4c.txt : IS spectrum under dark conditions at 0.3 V<br>- Fig4d.txt : IS spectrum under dark conditions at 0.4 V<br>- Fig4e.txt : IS spectrum under dark conditions at 0.6 V<br>- Fig4f.txt : IS spectrum under dark conditions at 1.0 V</p> <p>### Figure 5<br>- Fig5a.txt : Voltage-dependent transient current response of the perovskite memristor<br>- Fig5b.txt : Magnified view of the transient current response of a single voltage pulse at representative applied voltages<br>- Fig5c.txt : Pulse width-dependent transient current response<br>- Fig5d.txt : Corresponding magnified view of the first and last transient responses<br>- Fig5e.txt : Synaptic potentiation and depression characteristic response of the memristor</p> <p>### Figure 6<br>- Fig6a.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.4 V<br>- Fig6b.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.4 V<br>- Fig6c.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 0.8 V<br>- Fig6d.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 0.8 V<br>- Fig6e.txt : Transient current response of the volatile perovskite memristor with a single long pulse vs. a train of short pulses at 1.2 V<br>- Fig6f.txt : Corresponding magnified view of the transient current response of a first voltage pulse at 1.2 V</p>
Series data for the 22PN nonlinear memory effect in the l=2, m=0 mode.
<p>Dataset associated with the preprint arXiv:2407.19017, “Waveform models for the gravitational-wave memory effect: Extreme mass-ratio limit and final memory offset” by Arwa Elhashash and David A. Nichols. It contains the 22 post-Newtonian-order series data for the l=2, m=0 spin-weighted spherical harmonic mode of the gravitational-wave memory signal from an extreme-mass ratio inspiral with nonspinning black holes.</p>
2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware
<p>This dataset contains the raw data used for the publication:</p> <p><strong>2D materials-based homogeneous transistor-memory architecture for neuromorphic hardware</strong></p> <p>By Lei Tong<sup>1</sup>, Zhuiri Peng<sup>1</sup>, Runfeng Lin<sup>1</sup>, Zheng Li<sup>1</sup>, Yilun Wang<sup>1</sup>, Xinyu Huang<sup>1</sup>, Kan-Hao Xue<sup>1</sup>, Hangyu Xu<sup>2</sup>, Feng Liu<sup>3</sup>, Hui Xia<sup>2</sup>, Peng Wang<sup>2</sup>, Mingsheng Xu<sup>4</sup>, Wei Xiong<sup>1</sup>, Weida Hu<sup>2,</sup>*, Jianbin Xu<sup>5</sup>, Xinliang Zhang<sup>1</sup>, Lei Ye<sup>1,</sup>*, Xiangshui Miao<sup>1</sup></p> <p>Detailed descriptions for each file can be found in "Dataset description.docx".</p>
Data from: Capacity and selection in immersive visual working memory following naturalistic object disappearance
<p>Trial datasets and timeseries datasets associated with the experiment reported in the manuscript "Capacity and selection in immersive visual working memory following naturalistic object disappearance", by Babak Chawoush, Dejan Draschkow & Freek van Ede</p>
Dataset for the publication "Reversal of nanomagnets by propagating magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory"
<p>Raw data associated to the manuscript ‘’Reversal of nanomagnets by propagating<br> magnons in ferrimagnetic yttrium iron garnet enabling nonvolatile magnon memory‘’, Nature Communications (2023); doi: <a href="https://deref-web.de/mail/client/P1XojCfdiYA/dereferrer/?redirectUrl=https%3A%2F%2Fdoi.org%2F10.1038%2Fs41467-023-37078-8">https://doi.org/10.1038/s41467-023-37078-8</a><br> Information about file formats and measurement parameters are described in text files in the specific folders. For micromagnetic simulations Mumax 3.10 was used. The simulation scripts (*.mx3 files) and exemplary plotting scripts in Python 3.9 (*.py files) are included.</p> <p>Paper abstract:<br> Despite the unprecedented downscaling of CMOS integrated circuits, memory-intensive machine learning and artificial intelligence applications are limited by data conversion between memory and processor. There is a challenging quest for novel approaches to overcome this so-called von Neumann bottleneck. Magnons are the quanta of spin waves. Their angular momentum enables power-efficient computation without charge flow. The conversion problem would be solved if spin wave amplitudes could be stored directly in a<br> magnetic memory. Here, we report the reversal of ferromagnetic nanostripes by spin waves which propagate in an underlying spin-wave bus. Thereby, the charge-free angular momentum flow is stored after transmission over a macroscopic distance. We show that the spin waves can reverse large arrays of ferromagnetic stripes at a strikingly small power level. Combined with the already existing wave logic, our discovery is path-breaking for the new era of magnonics-based in-memory computation and beyond von Neumann computer architectures</p>
Epigenetic and transcriptional landscape of stress memory in woodland strawberry
<p>Bedfiles of differentially methylated regions (DMRs) that were detected in stressed mother plants (M) and their (themselves unstressed) clonal daughter plants that were formed <em>via</em> stolon formation (St1, St2, St3). The M plants were grown and sampled <em>in vitro</em> and the St1, St2, St3 plants in the green house. The DMRs were called using the the EpiDiverse/dmr bioinformatic analysis pipeline (Nunn et al., 2021).</p> <p><strong>Stress assays <em>in vitro</em></strong></p> <p>One-month-old seedlings were transferred to a fresh MS media and growth chambers at 24<sup>o</sup>C/21<sup>o</sup>C (day/night),16 h light/8 h dark, as control conditions<em>. </em>For heat-stress, plants were exposed to 30<sup>o</sup>C (day/night) for one week followed by 2 days of recovery (24<sup>o</sup>C/21<sup>o</sup>C) on fresh medium as well as the control plants. Then, the plates were transferred to 37<sup>o</sup>C (day/night) for 1 week with 2 recovery days (Figure 1A). We sampled aerial parts of plants for the molecular analyses. To reduce variability resulting from individual plants, three biological replicates of 5 pooled plants were collected per condition. Samples were harvested in 1.5 mL tubes between 9:00-11:00 a.m. and immediately frozen in liquid nitrogen and stored at -80<sup>o</sup>C until required.</p> <p><strong>Greenhouse propagation assays</strong></p> <p><em>In vitro</em> plants after heat and control treatment were transferred to soil (one plant per pot) in square plastic pots (size: 12x12x10 cm) and to a greenhouse with long day conditions (24<sup>o</sup>C/21<sup>o</sup>C day/night and 60%-70% humidity).</p> <p>Twelve mother plants (M) from control (CM; n=12) and heat-stress (HM; n=12) conditions were used for asexual propagation. From each mother plant, the two first stolons (St) were kept for producing the daughter plants of the first asexual propagation (St1) in individual pots. After two weeks, following root formation, the stolons were cut to get independent daughter plants from their mother plant (M). This process was continued until St3.</p> <p>The DMRs can also be visualized here: <a href="https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub">https://jbrowse.agroscope.info/jbrowse/?data=fragaria_sub</a></p> <p>And the raw bisulfite sequencing data can be found here: <a href="https://www.ebi.ac.uk/ena/browser/text-search?query=ERP135585">https://www.ebi.ac.uk/ena/browser/text-search?query=ERP135585</a></p> <p> </p> <p> How many daughter plants per stolon? One stolon produce a chain of daughter plants.</p>
Experimental Demonstration of In-Memory Computing in a Ferrofluid System
<p>Measurements that demonstrate the feasibility of in-memory computing using an amorphous ferrofluid system (in a liquid aggregation state). This work poses the basis for the exploitation of a colloid as both an in-memory computing device and as a full-electric liquid computer thanks to its fluidity and the reported complex dynamics, via probing read-out and programming ports.</p>
Joyce Kilmer Memorial Forest - microclimate station - midslope plot at the Coweeta Hydrologic Laboratory from 1996 to 2008
Tree stem temperature is currently being collected to determine both diurnal and seasonal variation in tree stem temperatures for all species and sizes present at the Joyce Kilmer Memorial Forest mid-slope plot. The purpose of the data is to characterize the tree stem temperature regimes as they relate to tree stem respiration. Data will continue to be collected indefinitely. Data will be subsequently summarized and published. This study supports project 4006: Southern Appalachian tree stem respiration. Resources for students about terms used in this study: Joyce Kilmer Memorial Forest - Source: University of North Carolina-Asheville, National Forests Forest Carbon Cycling - Source: Coweeta Schoolyard LTER
Neural Evidence of the Strategic Choice Between Working Memory and Episodic Memory in Prospective Remembering.
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Differentiation of functional networks during long-term memory retrieval in children and adolescents
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Neural Differentiation Tracks Improved Recall of Competing Memories Following Interleaved Study and Retrieval Practice
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Locus coeruleus activity strengthens prioritized memories under arousal
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Correcting False Memories: The Effect of Mnemonic Generalization on Original Memory Traces
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EEG: Visual Working Memory + Cabergoline Challenge
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EEG: Visual Working Memory in Acute TBI
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Virtual memory on a many-core NoC: experimental data
<p>Experimental data that accompanies the thesis "Virtual Memory on a Many-Core NoC" (http://etheses.whiterose.ac.uk/25675/). The data is textual and compressed.</p>
Ricchawar रिछावर (रायसेन ज़िला Madhya Pradesh). Memorial stone.
<p>Ricchawar रिछावर (रायसेन ज़िला Madhya Pradesh). Memorial stone, detail.</p>
Raw data for "Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning"
<p>This is the raw dataset for "Examining the effects of transcranial direct current stimulation on human episodic memory with machine learning". Each .xlsx file represents an experimental results of a single participant.</p> <p>Directory description:</p> <p>Eng_sham - the results from experiments without stimulation on English sample from Medvedeva, 2019.</p> <p>Eng_vlPFC - the results from experiments with vLPFC stimulation on English sample from Medvedeva, 2019.</p> <p>enc_off_new - the results from experiments with dLPFC offline encoding stimulation on Russian sample.</p> <p>enc_on_new - the results from experiments with dLPFC online encoding stimulation on Russian sample.</p> <p>sham_no stimulation - the results from experiments without stimulation offline encoding stimulation on Russian sample.</p> <p>vlPFC_stimulation - the results from experiments with vLPFC stimulation on Russian sample.</p> <p>Age.xlsx - the ages of the participants</p> <p> </p>
LSTM-REG: regional gridded runoff dataset for northwest Russia based on Long Short-Term Memory (LSTM) networks
<p>LSTM-REG: regional gridded runoff dataset for northwest Russia.</p> <p>Model: LSTM.</p> <p>Geographical domain: 25–57°E; 55–70°N.</p> <p>Spatial resolution: 0.5°x0.5°.</p> <p>Temporal resolution: daily.</p> <p>Period: 1980-2016.</p>
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.