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.
18,621
datasets available to search
ShareScore release 0.9.0
Dataset results
18,621 results for “development;”
(05)-Strobl2018A-DS0001 – Tribolium castaneum AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(05)-Strobl2018A-DS0001 – <em>Tribolium castaneum</em> AGOC{Zen1'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(05)-Strobl2018A-DS0003 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(05)-Strobl2018A-DS0003 – <em>Tribolium castaneum</em> AGOC{ARP5'#O(LA)-mEmerald} #2 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(05)-Strobl2018A-DS0002 – Tribolium castaneum AGOC{ARP5'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(05)-Strobl2018A-DS0002 – <em>Tribolium castaneum</em> AGOC{ARP5'#O(LA)-mEmerald} #1 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
DCASE 2018, Task 5: Monitoring of domestic activities based on multi-channel acoustics - Development dataset
<p>This repository contains the development data of task 5 of the DCASE 2018 challenge. The dataset is a derivative of the SINS database.</p> <p>The SINS database contains a continuous recording of one person living in a vacation home over a period of one week. The recordings were manually annotated on daily activity level: "Cooking", "Dishwashing", "Eating", "Social activity (visit, phone call)", "Vacuum cleaning", "Watching TV", "Working", "Presence" and "Absence". More information can be found on (please cite this papers when using the dataset):</p> <p>G. Dekkers, S. Lauwereins, B. Thoen, M. W. Adhana, H. Brouckxon, T. van Waterschoot, B. Vanrumste, M. Verhelst, and P. Karsmakers, “The SINS database for detection of daily activities in a home environment using an acoustic<br> sensor network,” in Proceedings of the Detection and Classification of Acoustic Scenes and Events 2017 Workshop (DCASE2017), Munich, Germany, November 2017, pp. 32–36.</p> <p>G. Dekkers, L. Vuegen, T. van Waterschoot, B. Vanrumste, and P. Karsmakers, “DCASE 2018 Challenge - Task 5: Monitoring of domestic activities based on multi-channel acoustics,” KU Leuven, Tech. Rep., July 2018.</p> <p>The derivative of the SINS database, 'DCASE 2018 – Task 5 development dataset' consists of data collected by 4 microphone arrays in the combined living room and kitchen area. The continuous recordings were split into audio segments of 10s. These audio segments are provided as individual files along with the ground truth. In total 72984 segments are made available, leading to approximately 200 hours of data.</p> <p>More information about the challenge and the specific dataset can be found <a href="http://dcase.community/challenge2018/task-monitoring-domestic-activities">here</a>. Information solely related to the content of the dataset is available in 'DCASE2018-task5-dev.doc.zip'. <br> <br> <strong>By accessing or using this database, the user accepts the provided EULA (available in DCASE2018-task5-dev.doc.zip).</strong></p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 4. Intelligent robots (accessed 01.11.2017). 4.1. Erica, a humanoid robot (https://www.tech-review.com/erica-is-the-latest-japanese-robot-with-human-appearance.html). 4.2. Atlas, a bipedal humanoid robot developed by Boston Dynamics (https://en.wikipedia.org/wiki/Atlas_(robot))
<p>One of the most highly quoted and interesting definitions of machine intelligence was presented by Alan Turing (1950). Turing considered a computing system intelligent if a human assessor could not decide the nature of the system (being human or artificial) based on questions asked from a room hidden from a human assessor. Until recently there were performed different discussions and comments on the Turing test. Hernández-Orallo (2000) presents an interesting study related to the Turing Test. Dowe and Hajek, (1998) propose a computational extension of the Turing Test. The design and development of intelligent systems are historically very recent. But, even if the advance of hardware and software is very fast, it will take a longer time until the artificial computing systems will attain a similar intelligence with the humans. Based on this fact, we consider that is not appropriate to formulate the problem of the direct comparison at a general level of human intelligence with the machine intelligence. Different definitions were proposed for the intelligence of the agents (Russell, & Norvig, 2003; Iantovics, & Zamfirescu, 2013). Many authors (Russell, & Norvig, 2003; Iantovics, 2005) argue that the intelligence of the agents cannot be defined universally. The impossibility to give a universal definition to the human intelligence is based mostly on the enormous complexity of the human brain and complexity of the human thinking and decision making. Similarly, we may consider the impossibility of universal definition of intelligence of the agents based on the very large variety (by type and complexity) of intelligent agents. The machine intelligence frequently is defined based on different abilities such as (Iantovics, 2005; Sharkey, 2006): autonomous learning, self-adaptation, and evolution. These principles of considering the intelligence are inspired by biological life forms able to learn autonomously during their life cycle, to adapt to the environment and to evolve during more generations. We would like to outline that not all the designed agents are intelligent. There is not a required property of an agent to be intelligent.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 4. Influence of AI in the development of smart cities by respondents age
<p>Respondents were asked to indicate how they evaluate the influence of AI in the development of intelligent cities. In order to fully understand the concept of smart cities, the definition of smart city given by Caragliu (2009) was given to the respondents. It is presented in section 2. On question 6 two-way analysis was used to determine the difference by age and gender. There was no statistically significant interaction between groups as determined by two-way ANOVA F (3, 106) = 8.675, p value>0.05 (p=.387). The assumption of homogeneity of variance was tested using the Brown-Forsythe Test. There were statistically significant differences by gender (F=2.169, p<0.05) and by age (F=30.885, p<0.05). More than 9 in ten (almost 94%) consider AI to be important (50%) or very important (43.8%) while just a few (6.2%) recall a moderate importance for smart cities development. Female participants scored significantly higher (M=4.60, SD=.49) than male participants (M=4.27, SD=.62) on question 6 „Generally speaking, how do you assess the influence of AI in the development of intelligent cities”. At the same question: the 41-50 age group scored the highest score followed by the 18-25 age group (M=4.40, SD=.49). The 26-30 age group scored lower than the 18-25 age group (M=4.37, SD=.48) and significantly higher than the 31-40 age group (Figure 4).</p>
Platform Specific Metamodel in Ecore for developing learning ecosystems
<p>The learning ecosystem metamodel is a M2-model instantiated from Ecore, a M3-model in the four-layer metamodel architecture of OMG. The main objective of this metamodel is to provide a Platform Specific Model (PSM) for describing learning ecosystems build from Open Source software components, human elements and information flows between components which are represented by web services.</p>
Survey and Interview Data from Mixed-Method Survey of Serverless Computing and Function-as-a-Service Software Development in Industrial Practice
<p>This dataset contains the almost-raw data resulting from two out of the three methods chosen by the researchers for their namesake study «A Mixed-Method Empirical Study of Function-as-a-Service Software Development in Industrial Practice». Among the files are web survey questions, anonymised survey results, and interview guidelines. We encourage other researchers to perform open coding and other analysis techniques on the data to verify our claims and to generate new insights.</p>
Supplementary Data for "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" (abridged version)
<p>This is a supplementary data set associated with the publication "Development of a General Calibration Model and Long-Term Performance Evaluation of Low-Cost Sensors for Air Pollutant Gas Monitoring" from the Center for Atmospheric Particle Studies, submitted to Atmospheric Measurement Techniques. This is an abbreviated version which does not include the calibrated models; these models must be re-generated by running the codes contained with the data set.</p> <p> </p>
Dataset of "What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation"
<p>Dataset of <em>What Should Developers Be Aware Of? An Empirical Study on the Directives of API Documentation</em> (Martin Monperrus, Michael Eichberg, Elif Tekes, Mira Mezini), In Empirical Software Engineering, Springer, 2011.</p> <p><br> * dataset-src.tar.bz2 contains the source code of the Java libraries used as raw data.<br> * dataset.xml.bz2 contains the API documentation extracted from source code.<br> * directives.xml.bz2 contains the API directives found during the exploratory case study.<br> * directive-appendix.pdf is a human-readable PDF version of directives.xml.bz2.<br> <br> All datasets are published under the Creative Commons Attribution License: if you use them, please cite:<br> </p>
The development and evaluation of an online application to assist in the extraction of data from graphs for use in systematic reviews (data repository)
<p>These are the data we generated in our evaluation of the graphical user interface.</p> <p>Please see our publication on Wellcome Open Research for information about the evaluations.</p>
Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 3: Pathological details
<p>The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; a scene for surgeons or a chance for medicines</p> <p>Supplementary Table 3: Pathological details</p>
The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; Supplementary Table 2: Metastatic sites
<p>The outcome of Childhood Adrenocortical Carcinoma in Developing Countries; a scene for surgeons or a chance for medicines.</p> <p>Supplementary Table 2: Metastatic sites</p>
Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 1: Patients' characteristics and endocrinal manifestations
<p>Outcome of Childhood Adrenocortical Carcinoma in Developing Countries. Supplementary Table 1: Patients’ characteristics and endocrinal manifestations</p>
A dataset of regional operational programmes (ROP) and rural development program (PROW) expenditures and socio-economic features in 2007-2013, Poland (source: Bank of Local Data)
<p>Dataset prepared on the bases of Polish Central Statistical Office (Statistics Poland) Bank of Local Data system https://bdl.stat.gov.pl/BDL/dane/podgrup/tablica [access: 1.07.2018]. The data set the expenditure of funds for individual priority axes in the programmes of both policies in the 2007-2013 programming period and the change in socio-economic features at the local (<em>poviat</em>, NUTS4) level. The Pearson correlation coefficients are used to assess the relationship between the level of expenditure for RDP and ROP <em>per capita</em> and selected indicators describing the level of economic, social and demographic development of local government units. The results of the analysis (the article <strong>Regional approach to rural development? A case of regional and rural programs 2007-2015 in Poland) </strong>will consist of tables, texts and of numerical data. Article with data is available here: OI: 10.5604/01.3001.0012.2934 GICID: 01.3001.0012.2934 Available language versions: en. <strong>Issue: </strong>Annals PAAAE 2018; XX (4): 22-28, https://rnseria.com/resources/html/article/details?id=176837</p>
(06)-He2019A-DS0003 – Tribolium castaneum AGOC #6 subline × foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0003 – <em>Tribolium castaneum</em> AGOC #6 subline × foxQ2-5' line long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
(06)-He2019A-DS0002 – Tribolium castaneum foxQ2-5' line × AGOC #6 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy
<p>(06)-He2019A-DS0002 – <em>Tribolium castaneum</em> foxQ2-5' line × AGOC #6 subline long-term live imaging dataset of embryonic development acquired with light sheet fluorescence microscopy</p>
Dataset: SiNGA Simulation of cAMP sink development in restricted and unrestriced environments.
<p>The setup files for the simulation of cAMP sink development in restricted and unrestricted environments. The results of the simulations were first presented on the 4th Central German Meeting on Bioinforatics 2019.</p> <p>To run a simulation the desired simulation setup file must be located in the same directory as the "singa-run.jar" and must be named "simulation_setup.json". Afterwards the simulation can be run using the "java -jar singa-run.jar" command on any system with at least Java 8 installed. Simulation parameters may be altered by changing the simulation setup file. The "singa-run" jar in this depository automatically runs all simulations resulting from all combinations of the "alternative values" annotated to concentrations or features.</p> <p>Results are saved in an "observations" folder that is created in the same directory. Each simulation creates one folder containing the variations used and the trajectory of the simulation.</p> <p>More information are available on the <a href="https://github.com/singa-bio/singa">SiNGA github</a>.</p>
Development, evaluation and application of NicheNet: datasets
<p>Here you can find the datasets used for development, validation and application of NicheNet, a method that links ligands to target genes to model intercellular communictation. The software to use NicheNet (i.e. R package nichenetr) is available at:</p> <p><a href="https://github.com/saeyslab/nichenetr">https://github.com/saeyslab/nichenetr</a></p> <p>Short description of individual files:</p> <p>* data for development of NicheNet: </p> <p>- lr_network.rds: network containing ligand-receptor interactions</p> <p>- signaling_network.rds: network containing signaling interactions</p> <p>- gr_network.rds: network containing gene regulatory interactions</p> <p>- weighted networks.rds: integrated weighted ligand-signaling and gene regulatory network</p> <p>* data for use of NicheNet:</p> <p>- ligand_tf_matrix.rds: matrix containing ligand-gene signaling scores</p> <p>- tf_target_matrix.rds: matrix containing gene-gene regulatory scores</p> <p>- ligand_target_matrix.rds: matrix containing ligand-target gene regulatory potential scores</p> <p>* datasets for validation of NicheNet:</p> <p>- expression_settings.rds: 111 expression datasets profiling the transcriptional response to a ligand</p> <p>* datasets to demonstrate application possibilities of NicheNet:</p> <p>- pemt_signature.txt: p-EMT geneset for which upstream regulators will be predicted</p> <p>- hnscc_expression.rds: single-cell expression data of malignant and non-malignant cells from head and neck squamous cell carcinoma tumors</p>
HDAC11 target engagement assay development in cells - CETSA
<p><strong>Methods</strong></p> <p><strong>Permeabilized CETSA</strong></p> <p>HEK293T cells were seeded in 6-well plates (8 x 10<sup>5</sup> cells/well) in DMEM supplemented with 10% FBS , penicillin (100 U/mL) and streptomycin (100 µg/mL). 4 h after seeding cells were transfected with 0.2 µg of N-terminally NanoLuc-tagged HDAC11 and 1.8 µg of empty pcDNA3.1 vectors using X-tremeGENE™ HP transfection reagent, following manufacturer instructions. Next day cells were harvested using Trypsin-EDTA, and resuspended in pre-warmed Opti-MEM (no phenol-red) at a concentration of 1.2 x 10<sup>5</sup> cells/ml. A 10x stock solution of digitonin (500 µg/ml, Sigma) and 100x stock solution of protease inhibitor cocktail (cOmplete™, Mini, EDTA-free,<em> </em>Sigma<em>)</em> was prepared in Opti-MEM and DMSO, respectively. The protease inhibitor / DMSO stock solution was used as vehicle to prepare 100x stock solutions of all test compounds. The 10x digitonin stock solution and 100x test compound / vehicle stock solution were pre-dispensed into 96-well PCR reaction plates at 10 µl/well and 1 µl/well, respectively. Next, 89 µl/well of the cell suspension was added to the pre-loaded PCR plates. Plates were then covered with adhesive plate sealing tape and were incubated at 37°C for 1 h. Immediately prior to heating the PCR plates, a 5x substrate stock solution was prepared by diluting NanoBRET NanoGlo Substrate (Promega) 1:100 into Opti-MEM. The thermal cycler was allowed to reach the designated temperature prior to placement of the PCR plate into the heating block. For Tm determination experiments, duplicate plates containing a fixed concentration of test compounds (30 µM) as well as vehicle control were prepared to perform a 11 point temperature gradient spanning 37-68.5°C for 3 minutes. The plates were allowed to cool at room temperature for 3 minutes after the heat treatment followed by addition of 25 µl 5x substrate stock solution per well. To measure NanoLuc activity 100 µl of sample were transferred from the PCR plate to a white 96-well plate. Total luminescence was measured immediately after substrate addition with CLARIOstar microplate reader (BMG Labtech).</p> <p><strong>Non-permeabilized CETSA</strong></p> <p>HEK293T cells were seeded in 6 well plates (8 x 10<sup>5</sup> cells/well) in DMEM supplemented with 10% FBS , penicillin (100 U/mL) and streptomycin (100 µg/mL). 4 h after seeding cells were transfected with 0.2 µg of N-terminally NanoLuc tagged HDAC11 and 1.8 µg of empty pcDNA3.1 vectors using X-tremeGENE™ HP transfection reagent, following manufacturer instructions. Next day cells were tretaed with compounds/DMSO for 6 h. Cells were harvested using Trypsin-EDTA and resuspended in pre-warmed Opti-MEM (no phenol-red) at a concentration of 1 x 10<sup>5</sup> cells/ml. Next, 100 µl/well of the cell suspension was added to the pre-loaded PCR plates. Immediately prior to heating the PCR plates, a 5x substrate stock solution was prepared by diluting NanoBRET NanoGlo Substrate (Promega) 1:100 into Opti-MEM. The thermal cycler was allowed to reach the designated temperature prior to placement of the PCR plate into the heating block. For Tm determination experiments, duplicate plates containing a fixed concentration of test compounds (30 µM) as well as vehicle control were prepared to perform a 11 point temperature gradient spanning 37-72°C for 3 minutes. The plates were allowed to cool at room temperature for 3 minutes after the heat treatment followed by addition of 25 µl 5x substrate stock solution per well. To measure NanoLuc activity 100 µl of sample were transferred from the PCR plate to a white 96-well plate. Total luminescence was measured immediately after substrate addition with CLARIOstar microplate reader (BMG Labtech).</p> <p> </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.