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3,172 results for “public”
HTML Status Codes of Publications citing ICPSR
<p>This dataset contains the HTML status codes of ICPSR citing literature, which were tested in 2020 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Public dataset to : Physiological and climate controls on foliar mercury uptake by European tree species
<p>Foliar Hg concentrations and daily foliar Hg uptake rates at > 200 forest plots of the ICP Forests Biomonitoring Network (http://icp-forests.net/) in 2015 and 2017.</p>
Dataset part two to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"
<p>Additional dataset to dataset part one (doi: 10.5281/zenodo.4719596) to the publication "CAL-1 as Cellular Model System to Study CCR7-Guided Human Dendritic Cell Migration"</p>
Dataset related to the publication "Thermal properties of nanocrystalline silicon nanobeams", DOI: 10.1002/adfm.202105767
<p>This folder contains the raw data from which the graphs in paper "Thermal properties of nanocrystalline silicon nanobeams", DOI: 10.1002/adfm.202105767, have been obtained.</p>
E-scooters: competition with shared bicycles and relationship to public transport (processed datasets)
<p>Processed datasets for article "E-scooters: competition with shared bicycles and relationship to public transport" by Łukasz Nawaro (University of Warsaw, Faculty of Economic Sciences).</p>
Dataset (n=1671) of the publication "Small-scale private forest owners and the European Natura 2000 conservation network: Perceived ecosystem services, management practices, and nature conservation attitudes"
<p>The excel file contains the replies of small-scale private forest owners in Lower Saxony, Germany, to certain questions of a survey analyzed within the following publication: "Small-scale private forest owners and the European Natura 2000 conservation network: Perceived ecosystem services, management practices, and nature conservation attitudes”.<br> <br> The questions included in this dataset cover their objectives, management activities, attitudes and forest stand structures. Further details can be found in the second sheet of the file called “Code explanation”.<br> <br> More information about the methods of data collection can be found in the before-mentioned publication.</p>
Education Services for the Deaf and Blind of the North Carolina Department of Public Instruction (ESDB dataset)
<p>The second dataset used in this study was collected by our group jointly with the Education Services for the Deaf and Blind (ESDB) of the North Carolina Department of Public Instruction under IRB 14046 of NC State University. For the ESDB data, the 14 sessions collected were grouped into pairs to ease a granular analysis, hence ending with 7 sessions. The characteristics of each dataset can be found in website.</p>
Dataset (VII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (VIII) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of compound <strong>1</strong> in MSM <strong>2-<em>S</em><sub>3</sub></strong> conformations of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (V) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>MD simulation data of <strong>SB203580</strong> related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Replication files for 'Are social and environmental clauses a tool for favoritism? Analysis of French public procurement contracts'
<p>This zip file contains the replication files for the manuscript 'Are social and environmental clauses a tool for favoritism? Analysis of French public procurement contracts'.</p> <p>It includes :</p> <ol> <li>one Rmd file providing an exhaustive R Code replicating the empirical results</li> <li>one md file providing Pyhton Code for the simulation</li> <li>two csv files containing all the data used in the paper</li> </ol>
Readme files in 16,000,000 public GitHub repositories (October 2016)
<p>Format</p> <p>index.csv.gz - CSV comma separated file with 3 columns: <repository name>, <flag>,<readme file name> For example: src-d/go-git,s,README.md</p> <p>The flag is either "s" (readme found) or "r" (readme does not exist on the root directory level). Readme file name may be any from the list:</p> <p>"README.md", "readme.md", "Readme.md", "README.MD", "README.txt", "readme.txt", "Readme.txt", "README.TXT", "README", "readme", "Readme", "README.rst", "readme.rst", "Readme.rst", "README.RST"</p> <p>100 part-r-00xxx files are in "new" Hadoop API format with the following settings:</p> <ol> <li> <p>inputFormatClass is org.apache.hadoop.mapreduce.lib.output.SequenceFileOutputFormat</p> </li> <li> <p>keyClass is org.apache.hadoop.io.Text - repository name</p> </li> <li> <p>valueClass is org.apache.hadoop.io.BytesWritable - gzipped readme file</p> </li> </ol>
Sets of mutually similar public GitHub repositories (October 2016)
<p>The format is JSON, the list of lists. Each list is the group of very similar repositories (Weighted Jaccard Similarity threshold 0.8~0.9).</p>
Programming language keyword frequencies extracted from 16,000,000 public GitHub repositories (October 2016)
<p>Origin</p> <p>16,000,000 repositories on GitHub as of October 2016, classified with github/linguist and parsed with Pygments. Token.Keyword tokens were filtered and MapReduce-d. Fuzzy duplicate repositories were discarded.</p> <p>Some languages, e.g. Haskell, are parsed wrong, resulting in <strong>many</strong> keywords. Still they were not removed since we are not familiar with such languages.</p> <p>Format</p> <p>Triples [language name]\t[keyword]\t[frequency]</p> <p>Tabs and new lines in keywords are escaped as \t and \n respectively.</p>
Data of publication Cavity-enhanced spectroscopy of a few-ion ensemble in Eu3+:Y2O3
<p>Data corresponding to the figures of the publication "Cavity-enhanced spectroscopy of a few-ion ensemble in Eu3+:Y2O3" by B. Casabone et al. (New J. Phys. 20(2018) 095006 https://doi.org/10.1088/1367-2630/aadf68). A text file describes data in each compressed folder, please refer to the caption in the publication for more details. </p> <p>These data can be also found together with the article manuscript at https://zenodo.org/record/1546139</p>
Research Incentive Programs for Closures of Public and Private Grade Crossings
<p>Corresponding data set for Tran-SET Project No. 17PPLSU13. Abstract of the final report is stated below for reference:</p> <p>"Vehicle-train crashes that occur at highway-rail crossings result in injuries, fatalities, and damage to equipment. In order to reduce the number of vehicle-train crashes, Federal Railroad Administration requires states which have challenges with crossing safety, including Louisiana, to develop a State Action Plan to improve grade crossing safety. Consequently, identification of specific solutions for improving safety at crossings was included in the 2015 Louisiana Transportation Plan as one of its objectives. This study seeks to identify and evaluate current and new incentive programs that encourage closure of at-grade railroad crossings to reduce the number of potential vehicle-train collision points, and hence improve safety. To accomplish this, a survey was designed and was distributed to personnel of state and railroad agencies. Survey responses from the DOTs revealed that currently, 16 states had no incentive programs for consolidation or closure of highway-rail crossings. Analysis of the survey data revealed that states without any incentive program had the least proportion of highway-rail crossing closures. The study revealed that cash incentives, while popular are not effective because although the Federal Government contributes to a state’s effort in offering cash incentives for closure of public grade crossings, the amount is not substantial enough to be considered a significant incentive by most local governments. Also, track relocation, while effective is mostly impractical due to the high costs involved. A literature review revealed that having a mathematical model to guide on the selection of crossings for consolidation or closure provides sound scientific basis and are usually welcomed by the community. The research team identified three potential new incentives that could work well for Louisiana namely crime rate reduction incentives, greenness improvement programs, and the development of a grade crossing consolidation model that considers safety, among a plethora of other factors, to be used to prioritize crossings to be closed. However, additional work is required to validate these programs for statewide deployment."</p>
The Smart City as a Field of Innovation: Effects of Public-Private Data Collaboration on the Innovative Performance of Small and Medium-Sized Enterprises in China
<p>Data is increasingly considered to be a key component in stimulating innovation (Cockburn, Henderson, and Stern, 2019). Numerous promising possibilities have been opened up by rapidly emerging, data-intensive technologies, including the Internet of Things (IoT) and artificial intelligence. The analysis and interpretation of big data are critical in the growth of technology firms in terms of AI training and computing capabilities (Allam and Dhunny, 2019). Small and medium-sized enterprises (SMEs), with their limited resources internally, particularly face a serious challenge of implementing innovation that depends upon data.</p> <p>The smart city provides an important opportunity for creating data-driven innovation. Significant amounts of data are increasingly available from various sources through sophisticated devices and equipment scattered in smart cities. Many smart city projects across the globe provide rich opportunities for SMEs to explore data-driven innovation (Bresciani, Ferraris, and Del Giudice, 2018). China, in particular, has recently been active in collecting and utilizing various kinds of data in smart cities. The availability of and access to data help to improve the software development of firms in China, where massive amounts of data resources are held by the government (Beraja, Yang, and Yuchtman, 2022).</p> <p>In the process of smart city development, there are also many tasks that are complementary to each other, including connecting databases, building online platforms that connect different data coming from different data sources, operating online platforms, and providing products and services to citizens. These diverse kinds of tasks involved in smart city projects initiated by local governments have brought about new business opportunities for innovative SMEs in China. To implement the policies of encouraging the development of SMEs by the central government, municipal governments have introduced policies that give priority to SMEs in participating in smart city projects (Ministry of Industry and Information Technology, 2016). Those companies that have access to the data held by government agencies are expected to benefit from utilizing the rich data for creating innovative products and services.</p> <p>There were few empirical studies conducted, however, to examine how data are actually managed and provided in smart cities and how they affect companies’ innovative activities. It remains unclear how public agencies and private enterprises collaborate on data and how that influences the innovation performance of SMEs in China. In smart cities, different types of public-private collaboration are involved, including hardware purchase, platform building, platform operation, and data analysis. It is not yet well-understood how these different types of collaboration influence the innovative performance of SMEs.</p> <p>In this study, we intend to address how data are managed through collaboration between the government and companies in smart cities and how the mode of collaboration influences firms’ performance on innovation. By focusing on the case of SMEs in China, this research aims to shed light on what kinds of data are available and used in smart cities and how the government and enterprises collaborate on data to facilitate innovation.</p> <p>The analysis of this study utilizes data on more than eight million contracts extracted from the official procurement database of the government. Data on companies are assembled with regard to the registered capital, industry, software products, and patents in 1990-2021 from the Tianyancha website. A panel data is established with key characteristics of SMEs, software and patents outputs, and their record on obtaining different government contracts annually. The government contracts are divided into three categories, namely, data analysis, platform building, and equipment supply, based on keyword identifications. To deal with the unbalance between the treatment group (the companies that obtained government contracts) and the control group, we use propensity score matching (one-to-one nearest neighbor matching) to narrow down the sample size of the control group to that of the treatment group. Then we apply the traditional difference-in-difference (DID) and DID with multiple time periods (Callaway & Sant’Anna, 2021) methods to examine whether there are significant differences in innovative outputs of software products and patents before and after the companies receive government contracts. We also compare how the innovation performance of companies differs based on the types of contracts these companies obtain. That makes it possible to identify what kinds of data collaboration would be effective in improving the innovative performance of SMEs.</p> <p>Our preliminary analysis of average treatment effect suggests that obtaining the government contracts, especially research contracts and platform building type of contracts, can effectively help improve the innovation performance when comparing between the control and treatment group. After checking the common support graph and doing a balance test, the outcome is solid. In general, based on the average treatment effect on the treated group, innovation performance, as measured by the number of patents and software productions, has shown a significant increase for those companies that obtain a research type of contract, whereas the increase is smaller for the platform building contractor and slight for the equipment purchase contractor. Government purchase of equipment that contains data-intensive technologies promotes the use of these products in smart cities. That, however, does not involve any substantive exchange or transfer of data possessed by the government and would not significantly contribute to stimulating innovation at firms.</p> <p>This study will have useful implications for establishing public-private collaboration on data for facilitating innovation in smart cities. Specifically, government procurement should also be viewed as a policy tool for improving China's technological innovation. According to our findings, research and platform development can successfully assist firms in improving their innovation performance. As a result, the government should grant enterprises access to additional data resources and data management related tasks if they want to see more industry structural transformation, as stated in the government documents.</p> <p> </p>
CPSV-AP 2.0 based public service descriptions of the Region of Epirus (Greece) as linked data
<p>It comprises a set of 45 CPSV-AP 2.0 based public service descriptions as linked data. The fields of the included public service descriptions were adapted from a regional public service catalogue, namely "The Citizen's Guide of the Region of Epirus", in order to comply with CPSV-AP 2.0 data model. The public service descriptions that are included in the linked dataset are not updated as the intended usage of the linked dataset is for research or educational activities and not for providing valid information to citizens.</p>
Development of the multi-dimensional Mobility Divide Index as a methodology to assess the accessibility level of public transport systems
<p>This paper presents the development of a multi-dimensional Mobility Divide Index (MDI) for assessing the accessibility of public transport developed using a co-design approach, directly involving end-users in the index design process. The index measures the gap that persons with disabilities feel they need to over-come to use public transport in the same way non-disabled citizens do. The MDI covers six accessibility-related dimensions: 1) safety, 2) convenience, 3) comfort, 4) affordability, 5) travel time, 6) autonomy. The method paper describes the step-by-step approach to create the MDI as a set of indicators to be rated by people with different access needs to a) provide evidence of the main criticalities to be addressed through the design and implementation of new inclusive mobility solutions, b) guide the design of new inclusive mobility solutions and measure their impacts and c) inform the transport sector encouraging positive changes in transport by providing recommendations for policy-making, new directions for service innovation, improvements and practical advice or highlighting investment priorities to pave the way for a more inclusive mobility. We present our findings in ways that can inform universal design and provide actionable information to researchers, policymakers, transport and urban planners, operators and stakeholders’ representatives to promote inclusive and equitable mobility solutions for all. Finally, we suggest follow up research and innovation, as well as recommendations for its uptake and utilisation in the pursuit of European accessibility standards and requirements for products and services in the mobility sector. </p>
Data supporting the publication "Enhanced nonlinear optomechanics in a coupled-mode photonic crystal device"
<p>Data supporting the publication "Enhanced nonlinear optomechanics in a coupled-mode photonic crystal device". Set contains raw measured data and plotted data points for all figures in the main text.</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.