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ShareScore release 0.9.0
Dataset results
66 results for “memory model”
Generating mouse model with predominant naïve or innate memory phenotype CD4+ T cells
GEO Series GSE46892. Mus musculus. 11 samples. Type: Expression profiling by array.
Triple transgenic reprogrammable mouse model reveals no evidence of epigenetic memory in reprogrammed cells/induced pluripotent stem cells
GEO Series GSE195511. Mus musculus. 16 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Methylation profiling by high throughput sequencing.
Sustained down-regulation of alphaSmooth muscle actin expression in aortas induced by angiotensin II: a new model of vascular memory to hypertension
GEO Series GSE175588. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Elevated Dnah11 Expression in Hippocampal CaMKII Neurons Impairs Memory via Disrupted Synaptic Plasticity in a Mouse Model of Noise-induced Hidden Hearing Loss
GEO Series GSE300555. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Aberrant ERK signaling in astrocytes impairs learning and memory in RASopathy-associated BRAF mutant mouse models [mouse_BRAF K499E]
GEO Series GSE283653. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Aberrant ERK signaling in astrocytes impairs learning and memory in RASopathy-associated BRAF mutant mouse models [mouse]
GEO Series GSE234763. Mus musculus. 9 samples. Type: Expression profiling by high throughput sequencing.
Dataset for efficient modelling of ionic and electronic interactions by resistive memory- based reservoir graph neural network
<p>Dataset for training the resistive memory-based reservoir graph neural network.</p> <p>In the atomic force calculation experiment, <span lang="EN-HK"><span>a Li</span><sub>3</sub><span>PO</span><sub>4</sub><span> dataset is derived from the melting and quenching trajectory via AIMD simulations. The training, validation, and testing datasets consist of 40,000, 5,000, and 5,000 samples, respectively. </span></span></p> <p><span lang="EN-HK"><span>In the Hamiltonian calculation, a dataset </span><span lang="EN-HK">of various graphene (72 atoms) configurations are generated by AIMD simulations at room temperature, with Hamiltonian data calculated via the OpenMX code</span><span lang="EN-HK">.</span><span lang="EN-HK"> <span>The training, validation, and testing datasets consist of 270, 90, and 90 samples (including atomic structure and Hamiltonian matrix), respectively.</span></span></span></p> <p>Code: https://github.com/hustmeng/RGNN.git</p> <p>1-Atomic_force_dataset.zip and 2-Hamiltonian_dataset.zip are original data.</p> <p>3-Graph_atomic_force.zip and 4-Graph_training_Hamiltonian.zip are graphs. </p> <p> </p> <p>References:</p> <p> </p> <p>1. C.W. Park, M. Kornbluth, J. Vandermause, C. Wolverton, B. Kozinsky, J.P. Mailoa, Accurate and scalable graph neural network force field and molecular dynamics with direct force architecture, npj Comput. Mater. 7(1) (2021) 73. https://github.com/ken2403/gnnff.git</p> <p>2. H. Li, Z. Wang, N. Zou, M. Ye, R. Xu, X. Gong, W. Duan, Y. Xu, Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation, Nat. Comput. Sci. 2(6) (2022) 367-377. https://github.com/mzjb/DeepH-pack.git</p> <p>3. D. Pfau, J.S. Spencer, A.G.D.G. Matthews, W.M.C. Foulkes, Ab initio solution of the many-electron Schrödinger equation with deep neural networks, Phys. Rev. Res. 2(3) (2020) 033429. https://github.com/google-deepmind/ferminet.git</p> <p> </p>
Research data for "Device-scale atomistic modelling of phase-change memory materials"
<p>This is a dataset related to the publication "Device-scale atomistic modelling of phase-change memory materials".</p> <p>Two folders have been provided for (1) the production data shown in this work, and (2) GAP models trained in this work:</p> <p>(1) The production data have been categorised according to the main text figures:</p> <ul> <li>"reference_database": Reference databases (i.e., training structures) of three GAP models discussed in this work. The structure data are provided in (extended) XYZ format, as labelled using either the PBEsol or the PBE functional. <ul> <li>"main_GST-GAP-22_PBEsol": the main GST-GAP-22 database, which was fitted using a two-step iterative training protocol. The resulting GAP model was used to obtain the results shown in the main text. The reference database is visualised in Fig. 1 of the main text. </li> <li>"refitted_GST-GAP-22_PBE": this dataset contains the same structures as the original GST-GAP-22 training data, with all structures having been relabelled using the PBE functional.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": an extension of the GST-GAP-22 database to a new, task-specific application, i.e., electromigration under an external electric field (cf. Extended Data Fig. 4).</li> </ul> </li> </ul> <ul> <li>"crystallization_simulations": three trajectories for the crystallization simulations shown in Fig. 2, of which all were obtained from GAP-MD. <ul> <li>"fig2b_growth_GAP-MD": Growth of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2c_cumulative_set_cycles_GAP-MD": Cumulative set process of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (1008 atoms).</li> <li>"fig2d_crystallization_12096at_GAP-MD": Crystallization of Ge<sub>1</sub>Sb<sub>2</sub>Te<sub>4</sub> (12096 atoms), in which three crystalline seeds were used.</li> </ul> </li> </ul> <ul> <li>"RESET_mushroom_model": two non-isothermal simulations shown in Fig. 3, obtained from GAP-MD. <ul> <li>"Fig3b_small_pulse": The 70 ps NVE equilibrium process after a small heating pulse was imposed in the focal area, giving an excess kinetic energy of 1,650 eV for the atoms in the focal area.</li> <li>"Fig3d_large_pulse": The 70 ps NVE equilibrium process after a large heating pulse was imposed in the focal area, giving an excess kinetic energy of 3,900 eV for the atoms in the focal area.</li> </ul> </li> </ul> <ul> <li>"RESET_device_scale_simulations": the GAP-MD simulations of the melting and heat dissipation process of a device-scale structural model shown in Fig. 4. <ul> <li>"Fig4b_device_scale<strong>_</strong>heating_10ps": The melting process of the device-scale model over 10 ps.</li> <li>"Fig4c_device_scale<strong>_</strong>cooling_40ps": The heat dissipation process of the device-scale model over another 40 ps.</li> </ul> </li> </ul> <p>(2) The GAP models trained in this work:</p> <ul> <li>"main_GAP_potential": the main GAP model used for the production data of this work, which is fitted based on PBEsol data. The XML identifier of this GAP model is GAP_2022_4_7_480_18_6_12_970.</li> </ul> <ul> <li>"other_GAP_potentials": two derivatives of the original GAP model. <ul> <li>"refitted_GST-GAP-22_PBE": using the same reference structures as the original GAP model but re-labelled using the PBE functional. The XML identifier of this GAP model is GAP_2022_5_7_480_0_58_2_26.</li> <li>"extended_GST-GAP-22_for_efield_PBEsol": An extension of the original GAP model to a new, task-specific application, i.e., electromigration under an external electric field. The XML identifier of this GAP model is GAP_2023_3_19_480_18_14_10_174.</li> </ul> </li> </ul> <p> </p>
Lincoln Memorial Sketch Model
American sculptor Daniel Chester French was chosen to create the statue that would be the centerpiece of the new Lincoln Memorial in Washington, DC. After consultations with Henry Bacon, the architect and his longtime personal friend, French decided to have the president seated. The first tentative model for the statue was this fast sketch model. Fine details weren't important at this early stage. The sketch model was intended to be a general idea of the possible final statue. This first draft model is interesting as it was eventually mirrored into the final design. The projecting left leg and foot were moved back under the seat while the opposite foot and leg were extended. The clenched and relaxed hands were switched as well. The chair was eventually turned into a smooth monolithic throne. The massive marble statue today unmistakably reflects this first early design. The sketch model can be seen today in the studio at Chesterwood. It sits on the edge of the six-foot model that dominates the room. Source: Objaverse 1.0 / Sketchfab
Development of a Model-based Working Memory Training and Investigation of Its Comparative Efficacy
ClinicalTrials.gov study NCT04042779. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: Impaired spatial memory codes in a mouse model of Rett syndrome
Open the record for dataset details and reuse information.
Ethanol increases diffuse amyloid plaque load and impairs memory in the 5xFAD mouse model of Alzheimer’s disease
GEO Series GSE301393. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Aberrant ERK signaling in astrocytes impairs learning and memory in RASopathy-associated BRAF mutant mouse models [organoid]
GEO Series GSE234548. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.
Hydroxyurea attenuates oxidative, metabolic, and excitotoxic stress in rat hippocampal neurons and improves spatial memory in a mouse model of Alzheimer’s disease
GEO Series GSE111943. Mus musculus; Rattus norvegicus. 24 samples. Type: Expression profiling by array.
Hydroxyurea protects hippocampal neurons against oxidative, metabolic and excitotoxic stress, and improves spatial memory in a mouse model of Alzheimer’s disease (Mouse)
GEO Series GSE111940. Mus musculus. 8 samples. Type: Expression profiling by array.
Sex‐specific accelerated decay in time/activity‐dependent plasticity and associative memory in an animal model of Alzheimer's disease
GEO Series GSE186710. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Transcription in a Jurkat cell model of T cell memory
GEO Series GSE61172. Homo sapiens. 4 samples. Type: Expression profiling by array.
Aberrant ERK signaling in astrocytes impairs learning and memory in RASopathy-associated BRAF mutant mouse models
GEO Series GSE234764. Mus musculus; Homo sapiens. 21 samples. Type: Expression profiling by high throughput sequencing.
Loss of the APP regulator RHBDL4 preserves memory in an Alzheimer’s disease mouse model.
GEO Series GSE288040. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
PanCancer Immune panel on infiltrating memory CD8 T cells purified from the allografts subjected to prolonged cold ischemic storage in mice cardiac heart transpalnatation model.
GEO Series GSE233620. Mus musculus. 12 samples. Type: Expression profiling by array.
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.