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4,210 results for “memory”
Experimental Data on Pragmatic, Constructive and Reconstructive Memory Influences on the Hindsight Bias
<p>After knowing how events turned out, we are quick to say ‘we knew it all along.’ Decades of research on hindsight bias have shown that outcome information biases what we later present as our original judgments. This experiment combined established between- and within-participant designs in a longitudinal study.</p>
Fig. 9 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Fig. 9. Gondarius inexpectatus Štys, 1972, female paratype with locality and type label. Photo: P. Baňař.
Fig. 9 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Fig. 9. Medocostes cf. lestoni Štys, 1967, female from Cameroon, Nkolbisson, 11.iv.1965, lgt. B. de Miré, coll. MNHN. 10.2 mm. Photo: M. Tkoč.
Fig. 7 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Fig. 7. Pavel Štys and his wife Alice Exnerová during the VI. European Hemiptera Congress in Blagoevgrad, Bulgaria in June 2012. Photo: F. Faraci.
Fig. 6 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Fig. 6. Pavel Štys and Yuri Popov in Rozhen Monastery at Melnik during the field trip of the VI. European Hemiptera Congress in Blagoevgrad, Bulgaria on 27th June 2012. Photo by P. Baňař.
Figs 1–4 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Figs 1–4. Young Pavel Štys in 1950s–1970s. 1 – Pavel as a university student. 2 – Pavel collecting insects during a field trip. 3 – Pavel at Šerlich in Orlické hory Mts. in 1975. 4 – Pavel on the field excursion with the students of the Charles University in 1970's. Photos 1–2 from archive of H. Šedová; photo 3 by V. Geislerová; photo 4 and the portrait on p. 351 from the archive of the Department of Zoology, Faculty of Science, Charles University.
Fig. 5 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Fig. 5. Pavel Štys in his office at the Charles University in Prague, consulting Heteroptera systematics with Dávid Rédei in 2006. Photo by P. Kment.
Fig. 8 in In memoriam of Professor Pavel Štys (1933-2018): biography, memories, bibliography and list of described taxa
Fig. 8. Mallik Malipatil, Pavel Štys and Randall T. Schuh during a coffee break, 4th Quadrennial Meeting of the International Heteropterist's Society, Nankai University, Tianjin, 2010. Photo: F. Faraci.
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</p>
An Easy Way to Show Memory Color Effects
<p>This dataset supplements the following article:</p> <p>Witzel, C. (2016). An Easy Way to Show Memory Color Effects. i-Perception, 7(5), 1-11. doi:10.1177/2041669516663751; http://journals.sagepub.com/doi/10.1177/2041669516663751</p> <p>The Excell file includes three sheets. In all sheets, rows correspond to participants, the three first columns provide sex, age, and colour deficiency (0 = colour deficient, 1 = non-deficient). The columns "memcol" provide the main data, i.e. the choice between the grey and the bluish version of the respective stimulus.</p> <p><strong>Sheet 1: Study1</strong></p> <p>Data corresponds to Figure 3: memcol1 = disk, memcol2 = banana.</p> <p><strong>Sheet 2: Study2a</strong></p> <p>Data corresponds to Figure 4: memcol1 = disk, memcol2 = banana, memcol3-6 = Mix1-4.</p> <p><strong>Sheet 3: Study2b</strong></p> <p>Data corresponds to Figure 5: memcol1-4 = Mix1-4.</p>
Divergent molecular networks program functionally distinct CD8+ skin-resident memory T cells
<p>Skin-resident CD8+ T cells comprise distinct IFN-γ- (TRM1) and IL-17-producing (TRM17) subsets that differentially contribute to immune responses. However, whether these populations employ common mechanisms to establish tissue residence is unknown. Here, we show that TRM1 and TRM17 cells navigate divergent trajectories to acquire tissue residency in skin. While TRM1 cells depend on a T-bet-Hobit-IL-15 axis, TRM17 cells develop independently of these factors. Instead, c-Maf commands a tissue-resident program in TRM17 cells parallel to that induced by Hobit in TRM1 cells, with an ICOS-c-Maf-IL-7 axis pivotal to TRM17 cell commitment. Accordingly, targeting this pathway enables ablation of skin TRM17 cells without compromising their TRM1 counterparts. Thus, skin-resident T cells rely on distinct molecular circuitries, which can be exploited to strategically modulate local immunity.</p>
Artifact for "Challenges in Empirically Testing Memory Persistency Models"
<p>Presented here are the litmus tests, auxiliary scripts, and output data constituting the artifact accompanying the paper titled 'Challenges in Empirically Testing Memory Persistency Models,' published in ICSE NIER'24. For detailed information, please refer to the README.</p>
Temporal cluster-based organisation of sleep spindles underlies motor memory consolidation
<p><span>Sleep benefits motor memory consolidation, which is mediated by sleep spindle activity and associated memory reactivations during non-rapid eye movement (NREM) sleep. However, the particular role of NREM2 and NREM3 sleep spindles and the mechanisms triggering this memory consolidation process remai<span>n unclear. Here, sim</span>ultaneous electroencephalographic and functional magnetic resonance imaging (EEG-fMRI) recordings were collected during night-time sleep following the learning of a motor sequence task. Adopting a time-based clustering approach, we provide evidence that spindles iteratively occur within clustered and temporally organised patterns during both NREM2 and NREM3 sleep. However, the clustering of spindles in trains is related to motor memory consolidation during NREM2 sleep only</span><span>. Altogether</span><span>,</span><span> our findings suggest t</span><span>hat</span><span> spindles' clustering and </span><span>rhythmic occurrence </span><span>during NREM2 sleep may serve as an intrinsic rhythmic sleep mechanism for the timed reactivation and subsequent consolidation of motor memories, through synchronised oscillatory activity within a subcortical-cortical network involved during learning</span>.</p>
Measuring semantic memory using associative and dissociative retrieval tasks
<p>Recent theoretical advances highlighted the need for novel means of assessing semantic cognition. Here, we introduce the Associative-Dissociative Retrieval Task (ADT), positing a novel way to test inhibitory control over semantic memory retrieval by contrasting the efficacy of associative (automatic) and dissociative (controlled) retrieval on standard set of verbal stimuli. All ADT measures achieved excellent reliability, homogeneity, and short-term temporal stability. Moreover, in-depth stimulus level analyses showed that associating is easier for words evoking few but strong associates, yet such propensity hampers the inhibition. Finally, we provided critical support for the construct validity of the ADT measures, demonstrating reliable correlations with domain-specific measures of semantic memory functioning (semantic fluency and associative combination) but negligible correlations with domain-general capacities (processing speed and working memory). Together, we show that ADT provides simple yet potent and psychometrically sound measures of semantic memory retrieval and offers noteworthy advantages over the currently available assessment methods.</p>
Dataset of "Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models"
<p>This dataset supports the article published<em> </em>in the Advanced Energy Materials:</p> <p>"Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models"</p> <p> </p> <p>Raw data for the article "Hysteresis, impedance and transients effects in halide perovskite solar cells and memory devices analysis by neuron-style models". For further details see the readme.txt file.</p>
quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data
<p>This page contains the code and datasets used in "quickSparseM: a library for memory- and time-efficient computation on large, sparse matrices with application to omics data".</p> <p>File <strong>test_datasets.zip</strong> containes three datasets:</p> <ul> <li><em>D1.RData</em>: scRNA-seq omics data derived from Salcher et. al (2022)</li> <li><em>D2.RData</em>: scRNA-seq omics data derived from Pineda et al. (2024)</li> <li><em>D3.RData</em>: in silico WGS SNP data.</li> </ul> <p>File <strong>test_scripts.zip</strong> containes the code to reproduce the results.</p>
Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks
<p>This dataset comes from the following paper:</p> <p>Matteo Arricca, Nicoletta Inverardi, Stefano Pandini, Maurizio Toselli, Massimo Messori, Giulia Scalet, Finite strain continuum phenomenological model describing the shape-memory effects in multi-phase semi-crystalline networks, Journal of the Mechanics and Physics of Solids, 105955, 2024. <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.jmps.2024.105955" target="_blank" rel="noopener"><span><span>https://doi.org/10.1016/j.jmps.2024.105955</span></span></a></p> <p>It contains:</p> <ul> <li>"Notes.pdf" describing all the files uploaded</li> <li>. txt experimental data</li> </ul>
Gwendolyn Koldofsky Memorial: Remarks by Steven Sample, Larry Livingston, Alan Smith and Marilyn Horne
<p>Previously unpublished, private recording sponsored and organized by Alan Smith of the Gwendolyn Koldofsky Memorial event held in October 1999 at Bovard Auditorium at the University of Southern California. Elvia Puccinelli and Lisa Sylvester discovered the recording while cleaning out Smith's office in June 2024 following his death in October 2023. Published here in part: opening remarks of USC President Steven Sample, as well as spoken tributes by Dean of the Music School, Larry Livingston, Alan Smith and Marilyn Horne, with permission. </p> <p>This material is published in connection with the monograph "Accompanying in America: Contextualizing Collaborative Piano" by Chanda VanderHart et al (2025)</p> <p> </p> <p> </p>
Memory B Cells and Their Transcriptomic Profiles Associated with Belimumab Resistance in Systemic Lupus Erythematosus in the Maintenance Phase
<p>Normalized count of RNA sequences from systemic lupus erythematosus patients treated with belimumab.</p> <p>We collected blood samples from patients with systemic lupus erythematosus (n=44) before and approximately 3 and 6 months after treatment by belimumab, respectively. We also included healthy individuals (n=17). There were no missing samples in the before-treatment specimen for 44 patients; however, five samples were missing at the three-month time, and four were missing at the six-month time after treatment. Whole blood samples were stored in PAXgene tubes (QUIAGEN). RNA was extracted by PAXgene Blood RNA Kit (QUIAGEN). Library preparation was performed using TruSeq stranded Total RNA Library PrepKit with Ribo-Zero Globin Human. Sequencing was conducted by NovaSeq6000 in the 100-bp paired-end mode. Sequencing reads were trimmed using Trimmomatic ver. 0.36 (leading: 20, trailing: 20, slidingwindow: 4:15, minlen: 36) and aligned to hg38 reference genome using STAR (ver. 2.7.3a). Gene counts were generated by RSEM (ver. 1.3.1) using Homo_sapiens.GRCh38.95.gtf from the Ensembl database. Gene counts were normalized by size factor implemented in DESeq2 and converted to count per million (CPM), and log<sub>2</sub>(CPM+1) was calculated. </p> <p>Sample names starting with 'R_' indicate responders, while those ending with '_NR' indicate non-responders. '0M' represents before treatment, '3M' represents three months after treatment, and '6M' represents six months after treatment. The numeric ID, excluding the last two characters, represents individual patients.</p> <p> </p> <p>If you use this data, please cite Iwasaki et al. Front. Immunol., 04 February 2025 <a href="https://doi.org/10.3389/fimmu.2025.1506298">https://doi.org/10.3389/fimmu.2025.1506298</a></p>
The entorhinal cortex modulates trace fear memory formation and neuroplasticity in the lateral amygdala via cholecystokinin
<p>Although the neural circuitry underlying fear memory formation is important in fear-related mental disorders, it is incompletely understood. Here, we utilized trace fear conditioning to study the formation of trace fear memory. We identified the entorhinal cortex (EC) as a critical component of sensory signaling to the amygdala. Moreover, we used the loss of function and rescue experiments to demonstrate that release of the neuropeptide cholecystokinin (CCK) from the EC is required for trace fear memory formation. We discovered that CCK-positive neurons extend from the EC to the lateral nuclei of the amygdala (LA), and inhibition of CCK-dependent signaling in the EC prevented long-term potentiation of sensory signals to the LA and formation of trace fear memory. Altogether, we suggest a model where sensory stimuli trigger the release of CCK from EC neurons, which potentiates sensory signals to the LA, ultimately influencing neural plasticity and trace fear memory formation.</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.