Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,457
datasets available to search
ShareScore release 0.9.0
Dataset results
1,457 results for “feedback”
Figure1. Static stimulation of a single TCR- The kinetic proofreading by the receptor on input x Є X forwards the receptor position p toward l. The receptor will generate negative feedback if p > β. The receptor will generate success signal when p== l.-AIDEN: A Density Conscious Artificial Immune System for Automatic Discovery of Arbitrary Shape Clusters in Spatial Patterns
<p>A TCR at position p is stimulated if rp (x) - rn(x) > l. Figure 1 depicts this process. When a T<br> Cell receives stimulations on more than k receptors, it generates activation signal to a B Cell, as<br> represented in Figure2.</p>
BRAIN Journal-Motor Imagery signal Classification for BCI System Using Empirical Mode Décomposition and Bandpower Feature Extraction-Figure 2. Timing of one trial of the experiment with continuous feedback (Guger et al, 2001)
<p>At the beginning of each trial (t = 0 s), a fixation cross appeared on the black screen. After two seconds a warning stimulus was given in the form of a beep. From 3 to 4.25s, an arrow (cue stimulus), pointing to the left or right, was shown on the screen. The subject was instructed to imagine a left or right hand movement until the end of the trial, depending on the direction of the arrow. The EEG was sampled and classified on line throughout the session. Between 4.25 and 8s, the classification result was used to give a continuously updated feedback stimulus in the form of a horizontal bar that appeared in the center of the screen. The paradigm is illustrated in fig (2).</p>
BRAIN Journal-A Synoptic of Software Implementation for Shift Registers Based on 16th Degree Primitive Polynomials-Figure 1. Basis scheme for a Feedback Shift Register
<p>Every LFSR works by taking the XOR of the selected bits in its internal state and any LFSR containing all zero bits will never move to any other state, so one possible state must be excluded from any cycle. A LFSR is composed of memory cells connected together as a shift register with linear feedback. In digital circuits a shift register is formed by flip-flops and EXOR gates chained together with a synchronous clock. Shift registers are a form of sequential logic like counters. Always the shift registers produce a discrete delay of a digital signal or waveform. Considering that a shift register has n stages, the waveform is delayed by n discrete clock times. Usually the naming of the shift register follows a type of convention shown normally in digital logic, with the least significant bit on the left. According to the communication protocol, the signals will be addressed, not the registers. There are n+1 signals for each n-bit register. Always the next state of an LFSR is uniquely determined from the previous one by the feedback network. Any LFSR will generate a sequence of different states starting with the initial one, called seed. A feedback shift register is composed of: - a shift register - a feedback function. </p>
Supplemental material to the article "Improving silent reading performance through feedback on eye movements: A feasibility study"
<p>This repository accompanies the article:</p> <p>Korinth, S., & Fiebach, C.J. (2018). Improving silent reading performance through feedback on eye movements: A feasibility study. Scientific Studies of Reading.</p> <p>For a more convenient download files were zipped. Organized into four subfolders it provides the data and the R-scripts allowing the replication of all analyses and the manuscript's figures. For further information please refer to the README.txt files in the individual subfolders.</p> <p>Please note, a former version of this repository contained the same data and the same analyses and was used for manuscript review purposes. Changes to the current version concern only the manuscript structure (i.e., analyses presented in previous manuscript version as appendices were integrated into the main text). </p>
Visual stimuli used in fMRI study on top-down feedback across cortical depths
<p>These videos contain samples of visual stimuli used in an fMRI study on top-down feedback across cortical depths in human early visual cortex [in preparation]. There is one video sample for each of the three experimental conditions in the main experiment: ‘Pac-Man dynamic’, ‘Pac-Man static’, and ‘control dynamic’. In addition, there are videos of stimuli used in a control experiment in which the shape of the stimulus (‘Pac-Man’ or square) and the background (texture or uniform) was varied. Please note that these videos are short sample segments from the experiment, and that in the actual experiment the rest blocks surrounding the stimulus presentations were much longer. The stimulus design of the main experiment is adapted from Akin et al. (2014).</p> <p>Akin, B., Ozdem, C., Eroglu, S., Keskin, D. T., Fang, F., Doerschner, K., Kersten, D., Boyaci, H. (2014). Attention modulates neuronal correlates of interhemispheric integration and global motion perception. Journal of Vision, 14(12). https://doi.org/10.1167/14.12.30</p>
Cooperative aerial tele-manipulation with haptic feedback
<p>Data-set regarding the work "Cooperative aerial tele-manipulation with haptic feedback".</p> <p>Citation bibtex:</p> <p>@inproceedings{mohammadi2016cooperative,<br> title={Cooperative aerial tele-manipulation with haptic feedback},<br> author={Mohammadi, Mostafa and Franchi, Antonio and Barcelli, Davide and Prattichizzo, Domenico},<br> booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},<br> pages={5092--5098},<br> year={2016},<br> organization={IEEE}<br> }<br> </p>
Data - The Benefits of Neurofeedback Training for Alpha Enhancement and Cognitive Performance - a Single-Blind, Sham-Feedback Study Using a Low-Prized EEG Device
<p>This data set includes the minimal data set, which was used to obtain the results in Naas, Rodrigues, Knirsch, & Sonderegger (2019, doi: http://dx.doi.org/10.1101/527598).</p>
Dataset: Seeking feedback and taking action: An evidence based evaluation of the impact of using practice tests with inbuilt feedback in a first-year business class
<p><strong>This data set includes both qualitative and quantitative data.</strong></p> <p><strong>Qualitative, survey data sought student opinions on the purpose of feedback and the actions taken following receipt of feedback.</strong></p> <p>1. Survey data collection began with a 13 question survey used in classes in early February 2020. This initial feedback survey was conducted via MS Forms. The data from this is available in the following files: a. 2020-2-17_feedbacksurvey_1_rawdata.csv b. 2020-2-17_feedbacksurvey_1_how-many-times-got-feedback.csv c. 2020-2-17_feedbacksurvey_1_take-action-after-feedback.csv d. 2020-2-17_feedbacksurvey_1_type-feedback-you-prefer.csv e. 2020-2-17_feedbacksurvey_1_purpose-of-feedback.csv f. 2020-2-17_feedbacksurvey_1_define-feedback.csv g. 2020-2-17_feedbacksurvey_1_what-makes-feedback-useful.csv h. 2020-2-17_feedbacksurvey_1_know-how-feedback-effective.csv i. 2020-2-17_feedbacksurvey_1_what-can-institute-do-to-improve-feedback.csv</p> <p><br>2. The first practice test was followed by a short survey of approximately 3 questions (created using MS Forms, a mix of closed and open questions). The data from this is available in the following files: a. 2020-3-2_feedbacksurvey_2_rawdata.csv b. 2020-3-2_action-comments-coded.csv c. 2020-3-2_other-comments-coded.csv d. 2020-3-2_open-question-comments.csv</p> <p>3. The second practice test followed by a 3 question short survey (created using MS Forms, all open questions) The data from this is available in the following files: a. 2020-5-12_feedbacksurvey_3_rawdata.csv b. 2020-5-12_feedbacksurvey_3_what-learn-by-participating.csv c. 2020-5-12_feedbacksurvey_3_what-action-taking.csv d. 2020-5-12_feedbacksurvey_3_has-module-changed-view-of-feedback.csv<br><br><strong>Quantitative data was gathered from Learning Management System, Canvas. This data shows the relationship between number of practice tests taken by students and their resultant grades.</strong></p> <p><br>4. I gathered the number of times students had taken the practice test before the midterm summative exam and compared with the student grades from the 30% midterm summative exam. The data from this is available in the following files: a. 2020-11-26_Canvasdata_midterm.csv</p> <p>5. Following the completion of the second summative exam (worth 70%), Canvas data showing the number of times the students had taken the second practice test and the grade outcomes for the students for the summative 70% test was gathered. The data from this is available in the following files: a. 2020-11-26_Canvasdata_endterm.csv</p> <p><br>6. A regression model was created to examine the impact of number of times that the practice test was taken on final grades, whilst controlling for midterm grades. The data from this is available in the following files: a. 2024-2-12_Anonymised_mid_and_final_grades_with_number_of_practices_before_final_exam.csv</p>
Supplementary data for ''Observations of the Clear-Sky Spectral Longwave Feedback at Surface Temperatures Between 210K and 310K"
<h3>This is supplementary data for the manuscript ''Observations of the Clear-Sky Spectral Longwave Feedback at Surface Temperatures Between 210K and 310K".</h3> <h3>satellite_data.nc</h3> <p>This file contains the spectral feedback derived from satellite observations by the AIRS instrument (Huang et al., 2020, DOI: 10.5067/5P7KQ31XI7XJ) as a function of wavenumber and surface temperature regime.</p> <h3>model_data_Ts.nc</h3> <p>This file contains the atmospheric profiles from our single-column model that were used for the radiative transfer calculations as well as the spectrally resolved outgoing longwave radiation from those calculations.</p> <h3>model_data_regimes.nc</h3> <p>This file contains derived atmospheric variables used for our analysis as well as the spectral longwave feedback for different surface temperature regimes.</p> <h3>brightness_temperatures.nc</h3> <p>This file contains the simulated and observed brightness temperatures averaged over the atmospheric window for different skin temperatures.</p> <h3>surface_skin_temperatures.nc</h3> <p>This file contains bin-averaged near-surface air temperatures and skin temperatures from ERA5 reanalysis and AIRS retrieval.</p>
SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
<p><span>This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.</span></p>
Cloud feedback maps from CMIP5 and CMIP6 models
<p>This repository contains maps of cloud feedbacks computed using up to three methodologies from two types of experiments conducted across CMIP5 and CMIP6 models.</p> <p>Methods:</p> <ul> <li>The approximate partial radiative perturbation technique (shortwave cloud feedbacks only), denoted with 'APRP' in filename. </li> <li>The adjusted change in cloud radiative effect, denoted with 'adjCRE' in filename. </li> <li>The cloud radiative kernel method, in files without 'APRP' or 'adjCRE' in the name. </li> </ul> <p>Experiments:</p> <ul> <li>amip+4K experiments, known as amip4K and amip-p4K in CMIP5 and CMIP6, respectively</li> <li>abrupt CO2 quadrupling experiments, known as abrupt4xCO2 and abrupt-4xCO2 in CMIP5 and CMIP6, respectively</li> </ul> <p>Data formats:</p> <ul> <li>For cloud feedbacks derived using APRP and adjCRE methods, all models' maps are contained within single netcdf files.</li> <li>For cloud feedbacks derived using cloud radiative kernels, Individual netcdf files are provided for each model, which are compressed into a zip file.</li> </ul>
Feedback from retinal ganglion cells to the inner retina
<p>This repository contains the data files and codes used in the following paper:</p> <p>Feedback from retinal ganglion cells to the inner retina<br> Anastasiia Vlasiuk, Hiroki Asari<br> PLOS One (accepted)</p> <p>The preprint is also available from bioRxiv 2020.08.30.274514.</p>
Socio-economic information and preferred interaction modalities related to eco-feedback systems
<p>This dataset has been collected during a research conducted by the above authors to understand and improve upon the interactivity, functionality, and ultimately the sustainability effect of smart plugs. As observed in the spreadsheet, we collected socio-demographic information and gathered anonymous responses to three speculative scenarios related to how new eco-feedback systems are defined. Specifically, the dataset reports on the following aspects:</p> <p> </p> <ol> <li>Description and rationale of an eco-feedback information idea they would like to receive (what). This was provided via free-form text.</li> <li>Description of how a smart plug would display this information (how), provided via free-form text. This included one sub-question asking participants which Wattom features their scenario better relates to – provided via multiple choice using four short GIF animations of each feature in action.</li> <li>Description of which location would this information be most useful in (where). This was provided via three sub-questions: (i) four multiple choice options (home, work, shared home, other); (ii) a free-form text with more specific location information; and (iii) 23 multiple choice options with color-coded icons of various appliances and devices).</li> <li>Description of when this information should be displayed throughout the time (when). This was provided via five multiple choice options with the images illustrated in Figure 1.</li> </ol>
Dataset for Nature Geoscience: Acceleration of a large deep-seated tropical landslide due to urbanisation feedbacks
<p>This archive file contains datafiles used in the manuscript: 'Acceleration of a large deep-seated tropical landslide due to urbanisation feedbacks' [https://doi.org/10.1038/s41561-022-01073-3]. It contains historical aerial orthomosaics, UAS DSM and orthomosaics, shapefiles, deformation maps, etc. for Funu landslide.</p>
High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change (Dupont et al., GBC)
<p>Files used to make the analysis in the paper "High trophic level feedbacks on global ocean carbon uptake and marine ecosystem dynamics under climate change" (Dupont et al., accepted in GBC)</p> <p>- HTL_LTL_figures.ipynb is the python notebook in which are computed the different terms to make the figures of the paper </p> <p>- histrcp85.1-PISAPE-N-OW** and piCtrl2-PISAPE-N-OW** files contain the raw outputs of the one way (OW) simulation</p> <p>- histrcp85.1-PISAPE-N-TW* and piCtrl2-PISAPE-N-TW* files contain the raw outputs of the two way (TW) simulation</p> <p>- all files ending with *rmp_f.nc/ *regrid.nc/ *f20.nc are regridded files to make maps used in the paper. More details can be found in the python noteboook (briefly, dAT_* = change in active export, dDIC_*= change in dissolved inorganic carbon, dEPC200_* = change in carbon export at 200m depth, OW/TW_<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">dBMape</a>* = OW/TW change in small high trophic levels biomass, OW/<a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/TW_dBMapermp_f.nc">TW_dBM</a>meszo* = OW/TW change in mesozooplankton biomass)</p> <p>- <a href="https://zenodo.org/api/files/cc2ae8cc-a150-4bc6-9125-04d9e3465859/egestt2_2.nc">egestt2_2.nc</a>, excrett2_2.nc and graztt2_2.nc are the outputs of egestion, excretion and grazing terms used to compute the active export (AT). </p>
Estimation of the Optical Feedback Factor C
<p>The dataset contains the estimated Optical Feedback factor value C for different experimental setups (i.e. laser-target distances, laser diode driving currents) as well as the estimated amplitude of the optical feedback interference signals. The calculations of C were performed using the non-uniform sampling approach [1].</p> <p>The raw data is provided in text format that is readable with Matlab for instance. The excel files summarize the estimated C and amplitude values for each set.</p> <p>[1] Bernal, O. D., Zabit, U., Jayat, F., & Bosch, T. (2021). <a href="https://www.mdpi.com/1424-8220/21/10/3528">Toward an Estimation of the Optical Feedback Factor C on the Fly for Displacement Sensing</a>. <em>Sensors</em>, <em>21</em>(10), 3528.</p>
Bases da Revisão Sistemática da Literatura DO trabalho "Adequação para a Engenharia de Requisitos dos requisitos extraídos a partir dos feedbacks de multidão de usuários de APPS"
<p>Base Revisão Sistemática da Literatura da dissertação de mestrado <strong>"Adequação para a Engenharia de Requisitos dos requisitos extraídos a partir dos <em>feedbacks</em> de multidão de usuários de APPS".</strong></p> <p> </p> <ul> <li>BaseArtigos_RSL.xlsx - contém os estudos que participaram da revisão, os estudos selecionados, dados extraídos e sintetizados</li> <li>AvaliacaoAmeacasAvalidade.xlsx - contém o formulario de avaliação das ameaças à validade</li> </ul>
Induction of Sis1 promotes fitness but not feedback in the heat shock response
<div class="page"> <div class="layoutArea"> <div class="column"> <p>The heat shock response (HSR) controls expression of molecular chaperones to maintain protein homeostasis. Previously, we proposed a feedback loop model of the HSR in which heat-denatured proteins sequester the chaperone Hsp70 to activate the HSR, and subsequent induction of Hsp70 deactivates the HSR. However, recent work has implicated newly synthesized proteins (NSPs) – rather than unfolded mature proteins – and the Hsp70 co-chaperone Sis1 in HSR regulation, yet their contributions to HSR dynamics have not been determined. Here we generate a new mathematical model that incorporates NSPs and Sis1 into the HSR activation mechanism, and we perform genetic decoupling and pulse-labeling experiments to demonstrate that Sis1 induction is dispensable for HSR deactivation. Rather than providing negative feedback to the HSR, transcriptional regulation of Sis1 by Hsf1 promotes fitness by coordinating stress granules and carbon metabolism. These results support an overall model in which NSPs signal the HSR by sequestering Sis1 and Hsp70, while induction of Hsp70 – but not Sis1 – attenuates the response.</p> </div> </div> </div>
Dataset for "Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback"
<p><strong>Dataset for the manuscript entitled: Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.</strong></p> <p><strong><em>DOI of the manuscript:</em> <a href="https://doi.org/10.1126/sciadv.adh1328">10.1126/sciadv.adh1328</a></strong></p> <p><em><strong>Abstract of the manuscript:</strong></em></p> <p>Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics in order to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal new properties of sensorimotor cortical integration and set new constraints on the design of neuroprosthetics.</p> <p><strong><em>Description of the variables in the data storage dictionary:</em></strong></p> <ol> <li>cursor_positions: sequence of the virtual cursor position over a session</li> <li>range_of_rewardable_cursor_position: range of cursor positions that can be rewarded. Upper threshold excluded. Lower threshold included. </li> <li>cursor_times: timing of the cursor positions provided by the cursor_positions data, in the same clock as spike and lick times. </li> <li>lick_times: timing of all recorded licks. </li> <li>reward_times:timing of the opening of the valve that releases the water reward. </li> <li>master_spike_times: time of the spikes of the master neurons.</li> <li>master_spike_shape: spike shape of each spike stored in master_spike_times. The shipe shapes are shown for 3s (30kHz sampling rate), for each 4 electrode of the corresponding tetrode.</li> <li>neighbor_spike_times': same as master_spike_times for neighbor neurons.</li> <li>neighbor_spike_shape': same as master_spike_shape for neighbor neurons. </li> </ol> <p><em><strong>General structure of the data set:</strong></em></p> <p>Each variable is a hierarchical tree of lists: [<em>Protocol</em>][<em>Mouse</em>][<em>Session</em>]. </p> <p><em>Protocol</em> takes one of the following values: 0: Bar feedback | 1: Full shuffle | 2: No Feedback | 5: Playback Structured Feedback | 7: Spontaneous activity | 8: Barrel Shuffle | 9: Frame shuffle.</p> <p><em>Mouse </em>and <em>Session</em> iterate through respectively the mice that were involved in the protocole, and the sessions, generally 5 in total.</p> <p><em><strong>Python code to load the hdf5 File</strong></em></p> <p>The code below relies on libraries available on a standard, mac anaconda install of the jupyter notebook system on the 25/03/2024. It provides with several nested lists of Numpy arrays.</p> <p>For instance, to access the series of cursor positions of Mouse M during session number S of Protocole P, index as follows: </p> <p>CP = cursor_positions_DATA[P][M][S]</p> <p># 1 - Load the libraries</p> <p> import h5py<br> import numpy as np<br> import pylab as pl</p> <p> filename = "./data.h5"<br> f = h5py.File(filename, "r")</p> <p># 2 - Extraction of the cursor position, time, lick and reward time, as well as the activity of the master and neighbor neurons. </p> <p> cursor_positions = f['cursor_positions']</p> <p> cursor_positions_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[cursor_positions[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> cursor_positions_DATA.append(DATA_protocole)</p> <p> </p> <p> cursor_times = f['cursor_times']</p> <p> cursor_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[cursor_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> cursor_times_DATA.append(DATA_protocole)</p> <p> </p> <p> lick_times = f['lick_times']</p> <p> lick_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[lick_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> lick_times_DATA.append(DATA_protocole)</p> <p> </p> <p> reward_times = f['reward_times']</p> <p> reward_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[reward_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> bfr = np.array(S)<br> DATA_mouse.append(bfr) <br> DATA_protocole.append(DATA_mouse)<br> reward_times_DATA.append(DATA_protocole)</p> <p> </p> <p> master_spike_times = f['master_spike_times']</p> <p> master_spike_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[master_spike_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> DATA_unit = []<br> for Unit in range(len(S)):<br> if not(list(S) == [0,1]):<br> N = f[S[Unit][0]]<br> bfr = np.array(N)<br> DATA_unit.append(bfr) <br> DATA_mouse.append(DATA_unit)<br> DATA_protocole.append(DATA_mouse)<br> master_spike_times_DATA.append(DATA_protocole)</p> <p> </p> <p> neighbor_spike_times = f['neighbor_spike_times']</p> <p> neighbor_spike_times_DATA = []<br> for Protocole in range(9):<br> DATA_protocole = []<br> P = f[neighbor_spike_times[Protocole][0]]<br> for Mouse in range(16): <br> # Loop through the mice, and collect <br> M = f[P[Mouse][0]]<br> if not(list(M) == [0,1]) and (len(M) == 5):<br> DATA_mouse = []<br> for Session in range(5):<br> S = f[M[Session][0]]<br> DATA_unit = []<br> for Unit in range(len(S)):<br> if not(list(S) == [0,1]):<br> N = f[S[Unit][0]]<br> bfr = np.array(N)<br> DATA_unit.append(bfr) <br> DATA_mouse.append(DATA_unit)<br> DATA_protocole.append(DATA_mouse)<br> neighbor_spike_times_DATA.append(DATA_protocole)</p> <p> </p>
Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation
<p>Biophysical effects of different bioenergy crop cultivation scenarios on global water cycles simulated by coupled IPSL-CM model, with ORCHIDEE-MICT-BIOENERGY as the land component and LMDz as the atmosphere component. The spatial resolution of the coupled model was 1.26° latitude × 2.5° longitude (i.e., 143*144 grid cells globally).</p> <p>Datasets includes:</p> <p>1. Source data and plotting code for Figure 1, the global precipitation changes induced by bioenergy cultivation.</p> <p>2. Source data and plotting code for Figure 2, the diagnostic precipitation changes for global land area, in and outside of the bioenergy cultivation area.</p> <p>3. Source data plotting code for Figure 3, the changes in water balance for global land area, in and outside of bioenergy cultivation area, monsoon regions and four different humidity zones.</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.