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585 results for “Goal”
A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13: Climate Action
<p>This data set pertains to the following research article: Purnell, P.J. (2022) <em>A comparison of different methods of identifying publications related to the United Nations Sustainable Development Goals: Case Study of SDG 13 – Climate Action</em>. arXiv:2201.02006</p>
Data set for "Cortical sensory processing across motivational states during goal-directed behavior"
<p>Data set for: Matteucci G, Guyoton M, Mayrhofer JM, Auffret M, Foustoukos G, Petersen CCH, El-Boustani S, Cortical sensory processing across motivational states during goal-directed behavior (2022).</p> <p>Neuron https://doi.org/10.1016/j.neuron.2022.09.032</p> <p>There are 2 files in this upload:</p> <p>1. The file named "Matteucci2022.pdf" is the Open Access pdf file of the manuscript published in Neuron.</p> <p>2. The file named "Matteucci_data_code.zip" (~26.5 GB) is a zipped version of a folder "Matteucci_data_code" (~33 GB), which contains the data analysed in the study along with Matlab code used to generate all main figures of the paper. The analysis code is in a subfolder named "code". This subfolder in turn has three subfolders "analysis_scripts", “analysis_functions” (containing the original code for intermediate data processing) and “paper_figures_scripts” (containing the code for generating each figure panel from pre-processed data). The main script “reproduce_figures.m” will call the subscripts contained in the “paper_figures_scripts” folder to reproduce the plots contained in all main figures of the paper (and take care of adding the relevant code and data folders and subfolders to Matlab file path). The raw and pre-processed data analysed in the study can be found in the folder named "data". A “README.txt” file provides further details on the content of each subfolder.</p>
Figure 8. Potential at every point; it is highest in the obstacles and lowest at the goal-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>The numerical potential field path planner is guaranteed to produce a<br> path even if the start or goal is placed in an obstacle. If there is no possible way to get from the start<br> to the goal without passing through an obstacle then the path planner will generate a path through<br> the obstacle, although if there is any alternative then the path will do that instead. For this reason, it<br> is important to make sure that there is some possible path, although there are ways around this<br> restriction such as returning an error if the potential at the start point is too high. The path is found<br> by moving to the neighboring square with the lowest potential, starting at any point in the space and<br> stopping when the goal is reached.</p>
Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV.</p>
Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be<br> used to anticipate the position of the obstacles and make better decisions in order to reach the<br> desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that<br> is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the<br> value of the potential at all other points is positive.</p>
Georgetown Outbreak Activity Library (GOAL) dataset
<p>The Georgetown Outbreak Activity Library (GOAL) dataset is a research effort led by Dr. Rebecca Katz at the Georgetown University Center for Global Health Science and Security. The data is designed to support responders in understanding what needs to get done, by whom, and when in the context of an event, providing information on existing guidance and authorities and to define response requirements for new and emerging events. GOAL can also be used for preparedness and planning efforts to ensure that this work is comprehensive and based in the practical realities of outbreak response.</p> <p>The GOAL dataset is available as an Excel file (.xlsx) which includes data coded about response activities needed throughout all phases of an outbreak, with case studies available to exemplify these activities during preparedness, response, and recovery efforts. The GOAL dataset contains a comprehensive set of fields describing what needs to be done for each response activity, when, by whom, and under what circumstances. A Data Dictionary and Glossary are included in the Excel file download.</p> <p>In addition to the coded data, the GOAL Case Study PDF Files (.zip) folder contains the original case studies written by Georgetown researchers which exemplify many of the activities described in the GOAL dataset. </p> <p>To learn more about the project and interact with the data, visit <a href="https://outbreaklibrary.org/">https://outbreaklibrary.org/.</a> The complex data contained within the GOAL dataset have also been translated for a public audience into <em>The Outbreak Atlas, </em>a book by Rebecca Katz and Mackenzie S. Moore. </p>
Dataset: iShares MSCI Global Sustainable Development Goals ETF (SDG) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Figure 6. Adding a Global Goal with its required type-Designing a Growing Functional Modules "Artificial Brain"
<p>The next step consists of adding a Global Goal expressing a motivation required by the<br> controller. The goal is to keep the vehicle's front free of obstacles, thus the Sensation “free” should<br> stay equal to “1”. After adding a new Global Goal, its assigned type should be “Cst” corresponding<br> to a constant output request (see figure 6). In the parameter field, its specified value is “1”.</p>
Figure 2. The editor's components from left to right: a) Sensation, b) Sensing Module, c) Global Goal, d) Acting Module.-Designing a Growing Functional Modules "Artificial Brain"
<p>Each “Sensation” corresponds to an integer value corresponding to a specific system's<br> sensor. Sensations are symbolized by a green rectangle on the editor's canvas (figure 2.a). Each<br> newly created sensation is assigned an identifier previously incremented. Its unique field, initially<br> filled with question-marks, allows it to associate a mnemonic in order to facilitate its interpretation.</p>
The Neural Basis of Attentional Selection in Goal-Directed Memory Retrieval
<p>The provided behavioral and EEG data belongs to the publication entitled: "Neural Basis of Attentional Selection in Goal-Directed Memory Retrieval" published in the journal Scientific Reports (article DOI: 10.1038/s41598-024-71691-x). </p> <p><strong>Abstract</strong></p> <p>Goal-directed memory reactivation involves retrieving the most relevant information for the current behavioral goal. Previous research has linked this process to activations in the fronto-parietal network, but the underlying neurocognitive mechanism remains poorly understood. The current electroencephalogram (EEG) study explores attentional selection as a possible mechanism supporting goal-directed retrieval. We designed a long-term memory experiment containing three phases. First, participants learned associations between objects and two screen locations. In a following phase, we changed the relevance of some locations (selective cue condition) to simulate goal-directed retrieval. We also introduced a control condition, in which the original associations remained unchanged (neutral cue condition). Behavior performance measured during the final retrieval phase revealed faster and more confident responses in the selective vs. neutral condition. At the EEG level, we found significant differences in decoding accuracy, with above-chance effects in the selective cue condition but not in the neutral cue condition. Additionally, we observed a stronger posterior contralateral negativity and lateralized alpha power in the selective cue condition. Overall, these results suggest that attentional selection enhances task-relevant information accessibility, emphasizing its role in goal-directed memory retrieval.</p>
Data set for "Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice"
<p>Data set for: Huang J, Crochet S, Sandi C, Petersen CCH (2024) Dopamine dynamics in nucleus accumbens across reward-based learning of goal-directed whisker-to-lick sensorimotor transformations in mice. Heliyon 10: e37831. https://doi.org/10.1016/j.heliyon.2024.e37831<br><br></p> <p>There are 2 files in this upload:</p> <p>1. The file named "2024_Huang_Heliyon.pdf" is the Open Access pdf of the online publication in Heliyon.</p> <p>2. The file named "Huang_data_code.zip" (~6 GB) is a zipped version of a folder "Huang_data_code" (~6 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder "Huang_data_code" and all subfolders. The main folder unzips into three subfolders: i) "Huang_dLight_data_code", which contains the dLight data; ii) "Huang_muscimol_data_code", which contains the behavioral data for muscimol inactivation experiments; and iii) "Huang_singletrial_example", which contains the data for the single trial example data shown in Figure 1C (note for this to run you first need to load the data file "JH056_190308_WD.mat"). In the folder "Huang_dLight_data_code", you can also find a "DataViewer" to visualise the data trial-by-trial, which you can run by executing "DataViewer.mlapp" directly from the subfolder "Huang_dLight_data_code" after loading the data "Huang_database.mat".</p>
A goal-based FAIRification planning method
<p>This image illustrates the first draft of the goal-based FAIRification planning method, which builds on experience gained from recent FAIRification projects and feedback from experts on FAIR.</p> <p><strong>An up-to-date version of this method is described at</strong> <a href="https://doi.org/10.21203/rs.3.rs-3092538/v1">https://doi.org/10.21203/rs.3.rs-3092538/v1</a></p>
Data for 'A multi‐methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'
<p>Title: Data for 'A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal'</p> <p>Recommended citation: Adhikari, B., Urbach, D., Chettri, N., Sharma, E., Breu, T., Geschke, J., Fischer, M. & Prescott, G. W. (2023) A multi-methods approach for assessing how conserving biodiversity interacts with other sustainable development goals in Nepal. Sustainable Development https://doi.org/10.1002/sd.2582</p> <p>R code available at: https://github.com/biraj-ad/SDGIntearctions_Nepal_2023</p> <p>Principal Investigator: Graham W Prescott (graham.prescott.research@gmail.com)</p> <p>Authors:<br> Biraj Adhikari (biraj.adhikari@unibe.ch, ORCID: 0000-0002-4260-8706)<br> Davnah Urbach (davnah.payne@unibe.ch, ORCID: 0000-0001-9170-7834)<br> Nakul Chettri (nakul.chettri@icimod.org, ORCID: 0000-0002-3338-8879)<br> Eklabya Sharma (eklabya.sharma11@gmail.com, ORCID:0000-0003-3089-8838)<br> Thomas Breu (thomas.breu@unibe.ch, ORCID: 0000-0003-2348-504X)<br> Jonas Geschke (jonas.geschke@unibe.ch, ORCID: 0000-0002-5654-9313)<br> Markus Fischer (markus.fischer@ips.unibe.ch, ORCID: 0000-0002-5589-5900)<br> Graham W Prescott (graham.prescott.research@gmail.com, ORCID:0000-0001-5123-514X)</p> <p>Date of data collection: August 2021 - June 2022<br> Location of data collection: Kathmandu<br> Date of final release:</p> <p>Data Overview:<br> There are two excel files. Each excel sheet is also uploaded as a separate .csv file.</p> <p>1. 'Alldata.xls': This excel file contains data collected for three independent methods used to develop the study. There are four sheets in this excel workbook. The first three sheet pertains to data collected for the seven-point SDG interactions score, correlation, and expert elicitation method. The last sheet is a derivative of the first three methods, which contains synthesized information of data.<br> Sheet overview:<br> method1: This sheet relates to the data collected through online expert survey using Kobo Toolbox. Description of columns:<br> institution -> institutional affiliation of the participant<br> inst_cat -> categorization of the institution into Intergovernmental Organization (IGO), Non-governmental Organization (NGO), Academia, and Government.<br> exp_years -> experience (in years) of the participant in the conservation sector of Nepal<br> sdgout and sdgin -> The SDG that the participant was randomly assigned to rate its outgoing and incoming interaction with SDG 15 respectively<br> outscore -> the outgoing interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_out -> the degree of confidence of the participant in their answer to the outgoing interaction score<br> text_out -> (optional) a description of why the participant gave that outgoing interaction score<br> inscore -> the incoming interaction score assigned by the participant, consisting of values between -3 (Cancelling) to +3 (Indivisible)<br> conf_in -> the degree of confidence of the participant in their answer to the incoming interaction score<br> text_in -> (optional) a description of why the participant gave that incoming interaction score</p> <p>method2: This sheet relates to the data collected for correlation analysis. This includes time-series data of SDG indicators for Nepal obtained from the Global SDG Indicators Database (https://unstats.un.org/sdgs/indicators/database/). Description of columns:<br> Year -> Year when the indicator was measured<br> forest_cover -> Forest cover as a percent of total area (%)<br> kba_freshwater -> Average proportion of Freshwater Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> kba_terrestiral -> Average proportion of Terrestrial Key Biodiversity Areas (KBAs) covered by protected areas (%)<br> redlist -> Red List Index<br> undernourishment -> Prevalence of undernourishment (%)<br> food_insecurity -> Prevalence of moderate or severe food insecurity in the adult population (%)<br> death_chronic -> Mortality rate attributed to cardiovascular disease, cancer, diabetes or chronic respiratory disease (probability)<br> suicide -> Suicide mortality rate (deaths per 100,000 population)<br> uhc_index -> Universal health coverage (UHC) service coverage index<br> f_parliament -> Proportion of seats held by women in national parliaments (% of total number of seats)<br> safewater -> Proportion of population using safely managed drinking water services (%)<br> electricity -> Proportion of population with access to electricity, by urban/rural (%)<br> cleanfuel -> Proportion of population with primary reliance on clean fuels and technology (%)<br> renewable -> Renewable energy share in the total final energy consumption (%)<br> gdp_capita -> Annual growth rate of real GDP per capita (%)<br> manu_value -> Manufacturing value added as a proportion of GDP (%)<br> consumption_gdp -> Domestic material consumption per unit of GDP (kilograms per constant 2010 United States dollars)<br> consumption_all -> Domestic material consumption (tonnes)<br> consumption_cap -> Domestic material consumption per capita (tonnes)<br> death_missing -> Number of deaths, missing persons and persons affected by disaster per 100,000 people<br> local_drr -> Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)</p> <p>method3: This sheet relates to the data collected through key informant interviews. We obtained the count of interactions by coding interview responses as outgoing or incoming co-benefits or trade-offs in MaxQDA. The sheet summarizes the count for type of interaction (co-benefit or trade-off) for each SDG.</p> <p>allmethods: This sheet summarizes the proportion of co-benefits or trade-offs uncovered by each method for each SDG.</p> <p>2. 'ForSynthesistable.xlsx': This excel file synthesizes goal level interactions for the three methods in three separate sheets. We use this table to develop figure 6 of the Manuscript.</p> <p> </p>
Replication Data for "How Do Different Types of Testing Goals Affect Test Case Design?"
<p># Replication Data for "How Do Different Types of Testing Goals Affect Test Case Design?"</p> <p>## Overview</p> <p>Background: Test cases are designed in service of one or more goals, e.g., assessing functional correctness or performance. We lack a clear understanding of how specific goal types influence test design.</p> <p>Aims: We explore the relationship between types of testing goals and test design, including identification and importance of goal types, quantitative relations between goal types and test cases, and personal, organizational, methodological, and technological factors that may influence this relationship.</p> <p>Method: We have conducted both qualitative and quantitative analysis of interviews and a survey with software developers in various domains and of varying experience.</p> <p>Results: We identify nine goal types, and focus on correctness, reliability, and quality. We observe that test design for correctness forms a "default" design process that is modified when pursuing other goals. For the examined goal types, test cases tend to be simple, with many tests targeting a single goal and each test focusing on 1-2 goals at a time. Testers often start by using past tests as templates. Testing practices, tools, and system types of interest vary between goal types. Test design can be influenced by organization, process, and team makeup.</p> <p>Conclusions: This study provides a foundation for future research on test case design and testing goals.</p> <p>The paper can be found at http://greg4cr.github.io/pdf/23goals.pdf </p> <p>## Data Contained in This Package</p> <p>- thematic_coding.pdf</p> <p>This is the theme map created from the interview data. We extracted important statements from the interviews (codes) and clustered them into themes and sub-themes.</p> <p>- survey_responses.pdf</p> <p>This file contains all survey responses.</p> <p>Both interview and survey data has been anonymized to protect the privacy of the participants.</p>
Data for "Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives"
<p>Research data supporting the study "Feeding climate and biodiversity goals with novel plant-based meat and milk alternatives".</p> <p>It contains:<br> 1) merged.gdx - data derived from the original scenario database<br> 2) map.csv - mapping of food commodities to food groups used for analysis<br> 3) manure.csv - results on nitrogen input to cropland and N crop fertilization from manure<br> 4) AgMIP_regions.shp - shape file used to make maps<br> 5) Paper_visuals_NCOM.R - R script to analyze and visualize the data. It reproduces the main figures in the paper and the appendix<br> last tested for R Studio 2022.12.0 Build 353, Release (7d165dcf, 2022-12-03) for Windows 10 Pro, 64-bit operating system</p> <p>Instructions:<br> The R code, file 4, reads in files 1, 2 and 3 and generates figures, tables and maps.<br> The directories (line 50 and 58) need to be updated to the location of the data (the current folder). </p> <p> </p>
Trade-offs between Sustainable Development Goals in carbon capture and utilisation
<p>Dataset associated with the publication "Trade-offs between Sustainable Development Goals in carbon capture and utilisation" by Iasonas Ioannou, Ángel Galán-Martín, Javier Pérez-Ramírez, and Gonzalo Guillén-Gosálbez, available at <a href="https://doi.org/10.1039/D2EE01153K">https://doi.org/10.1039/D2EE01153K</a>. The dataset includes the numeric data required to plot all the figures embedded in the main manuscript.</p>
Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals
<p><strong>Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals</strong></p> <p><em>Authors: Xi Guo; Puying Zhang; Yaojie Yue</em></p> <p>This is the outcome data of our research which is under submission.</p> <p>Though the impact of climate change on potential crop distributions has been extensively explored, there are few studies on potential wheat distributions at specific global warming levels (GWLs), e.g., 1.5 °C and 2 °C.</p> <p>Here, a grided (0.5 degree × 0.5 degree) dataset of global potential wheat distribution under the 1.5 °C and 2 °C GWLs is proposed. This dataset is produced using the MaxEnt model with support of multi-model data(GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M).</p> <p>The predictive accuracy of the proposed dataset was carefully validated between the predicted global wheat distribution and multiple known datasets. For more details of the approach used to predict the global wheat distribution please refer to: Yue, Y., Zhang, P., Shang, Y., 2019. <em>The potential global distribution and dynamics of wheat under multiple climate change scenarios</em>. <em>Sci Total Environ</em> 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.</p> <p>The results indicate the regional differences in the potential suitability of wheat cultivation under different GWLs. Eastern Europe, Pakistan, Northern India, Russia, and Canada witnessed a significant increase in wheat planting suitability. In contrast, Central Eastern Africa, Southeastern Australia, Southeastern China, Southern Brazil, France, Spain, and Italy demonstrated a significant decrease in wheat suitability. Compared with 1.5 °C GWLs, wheat planting suitability decreases more evidently in 2 °C GWLs in Central and Eastern Africa, Central and Southern India, Southeastern China, Australia, Mexico, Southern Brazil, and Argentina. Simultaneously, regions such as Russia, Pakistan, Canada, and the Great Lakes area of the United States observed further increases in wheat planting suitability. To ensure favorable conditions for the cultivation of wheat, it is crucial to limit the global average temperature increase to less than 2 °C.</p> <p>Our findings demonstrate the influence of different GWLs on potential global wheat distribution, highlighting the regional differences in the potential suitability of wheat cultivation under different GWLs.</p> <p>We argue that the potential global wheat distribution datasets under different GWLs are a valuable complement to currently available products. This potential global wheat distribution is one of the few products to take into account 1.5 °C and 2 °C GWLs based on multi-modal data. We believe that it can provide more valuable information for policymakers to make decisions for the warming world.</p> <p>The data of the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals is stored in a zip package, that is <strong>Global Planting Suitability of Wheat</strong><strong>.zip</strong>. This package consists of 1 folder, i.e., <strong>SR1.5&2.0</strong>.</p> <p>This subfolder contains GeoTIFF files for the Global Planting Suitability of Wheat Under the 1.5 °C and 2 °C Warming Goals. Correspondingly <strong>Wheat_SR15</strong><strong>.tif</strong> and <strong>Wheat_SR</strong><strong>20</strong><strong>.tif</strong>. The grid value of each file ranges from 0 to 1, indicating the possibility of wheat planting in each grid, and the higher the value, the higher the possibility that wheat exists.</p> <p>Reference:</p> <p>Yue, Y., Zhang, P., Shang, Y., 2019. <em>The potential global distribution and dynamics of wheat under multiple climate change scenarios</em>. <em>Sci Total Environ</em> 688, 1308-1318. https://coi.org/10.1016/j.scitotenv.2019.06.153.</p>
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16 in Collecting Sounds. Online Sharing of Field Recordings as Cultural Practice
"The sound comes from a meadow in the Sierra Nevada Mountains in California. The meadow is at an elevation of 2400 meters near a mountain named Olancha Peak, which is 3700 meters in altitude. Ihave a group of friends with which Ibackpack (trek) into the mountains. Our goal was to spend some time in the mountains and hike to the top of Olancha Peak (…) By the time we reached the meadow, we were in a forest and there was still snow on the ground in some places. We took the trip in June of 2006. The Sierra Nevada Mountains are a large mountain range. Much of the range is protected by national parks or preserved areas we call 'wilderness areas' (…) Ihave been backpacking for nearly 40 years and Iwill hopefully continue with this challenging activity for 40 years more! Many of my friends are much younger than Iam and it gives me much satisfaction to be able to have as much or more stamina for this activity than they have! When we are on these trips, we hike up peaks, catch fish, drink some whiskey around campfires and enjoy our time in the beautiful solitude. My memories of this trip were of the steep, hot hike from the desert to the cool meadow; the overall beauty of the nature, the absolute solitude of our campsite near the meadow; the strenuous hike to the top of Olancha Peak; the camaraderie of my friends; and, of course the sound of the frogs in the meadow. The frog sounds were astounding to me and Iwould listen in awe of the creature's instinctual desire to reproduce and continue the existence of their kind. Surely there were different species in the meadow for some of the frog sounds were different than others. The sounds only occurred after the Sun went down for the evening. Istood next to the creek in the meadow and recorded the sounds using my digital camera." [Peter/plentz1960]16
Sleep Goal-focused Online Access to Lifestyle Support
ClinicalTrials.gov study NCT05942326. IPD Sharing: YES. Countries: 1. Publications: 1.
Data from: Complementary roles of dorsal and ventral hippocampus in the flexible adaptation of goal-directed behavior
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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.