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13,499 results for “researcher”
Lightning Assimilation in the Weather Research and Forecasting (WRF) Model: Technique Updates and Assessment of the Applications from Regional to Hemispheric Scales
<p>Figure 1. The data is proprietary, but it can be purchased from Vaisala Inc. (https:// <a href="http://www.vaisala.com/en/products/systems/lightning-detection">www.vaisala.com/en/products/systems/lightning-detection</a>), and the WWLLN raw data are also available for purchase at <a href="http://wwlln.net">http://wwlln.net</a>.</p> <p>Figure 2. Maps, data is not applicable.</p> <p>Figure 3. Data file: NLDN_WWLLN_Prism_Rainfall_Analysis.xlsx</p> <p>Figure 4. Data file: NLDN_WWLLN_METVARS_T2_Jul_2016.xlsx</p> <p>Figure 5. Data file: CONUSall_METOBS_q_Jul_2016.xlsx</p> <p>Figure 6. Data file: CONUSall_METOBS_ws_Jul_2016.xlsx</p> <p>Figure 7. Created using the R script: Hemi_Rain_ModelOnlyWGPM.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds, AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds, and GPM_WRF_Paired_rain2Hemispheric_July2016.rds.</p> <p>Figure 8. Created using the R script: Hemi_Rain_Aanlysis.R based on the R object files: AnnualRainFall_CFC_WRF_Hemi_BASE_*.rds and AnnualRainFall_CFC_WRF_Hemi_LTA_*.rds.</p> <p>Figure 9. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 10. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 11. Data file: CPC_Model_Monthly_Prep_Hemi_Stats.xlsx</p> <p>Figure 12. Created using the R script: CreateCPCdataforUSdomain_vs_Prism.R based on the R oject files: Prism_CFC_WRF*.rds</p> <p>Figure 13. Data file: Hemi_lta_METOBS_T2_Jul_2016.xlsx</p> <p>Figure 14. Data file: Hemi_lta_METOBS_q_Jul_2016.xlsx</p> <p> </p>
Research data: Counterfactual assessment of protected area avoided deforestation in Cambodia version 4
<p>This dataset includes the data, the R scripts used for analysis and results that are the basis of the journal article: Black, B., Anthony, B. In review. Counterfactual assessment of protected area avoided deforestation in Cambodia: Trends in effectiveness, spillover effects and the influence of establishment date. Global Ecology and Conservation.</p> <p>Each folder includes a specific readme file in .txt format which includes metadata and instructions for reproducing the research.</p> <p> </p>
Datasets and Codes for Purgar et al. 2022: Quantifying research waste in ecology
<p>This is a Data package containing Datasets and Codes to reproduce the analyses and create figures for the paper Purgar et al. 2022: Quantifying research waste in ecology. Datasets include the original effect sizes as extracted from studies and the final set of the effect sizes used in the meta-analysis. The code includes the code used to perform meta-analysis and to create plots (in the main article and the supplementary files). Description of the files and variables can be found in the Readme file.</p>
CaRCC Research Computing and Data (RCD) Workforce Survey Data 2021 - Part 1
<p>Datasets and analysis to accompany "Characterizing the US Research Computing and Data (RCD) Workforce"</p> <p>Paper: Christina Maimone, Scott Yockel, Timothy Middelkoop, Ashley Stauffer, and Chris Reidy. 2022. Characterizing the US Research<br> Computing and Data (RCD) Workforce. In Practice and Experience in Advanced Research Computing (PEARC ’22), July 10–14, 2022,<br> Boston, MA, USA. ACM, New York, NY, USA, 12 pages. https://doi.org/10.1145/3491418.3530289</p>
Historic machines from 'prams' to 'Parliament': new avenues for collaborative linguistic research
<p>Recording of presentation of long paper, DH Benelux 2022: RE-MIX. Creation and alteration in DH (Hybrid), 1-3 June 2022.</p> <p>Research in computational linguistics has made successful attempts at modelling word meaning at scale, but much remains to be done to put these computational models to the test of historical scholarship. More importantly, a lot of computational research looks at texts in a historical vacuum, 'synchronically', as linguists would say. <em>Living with Machines</em> is an interdisciplinary research project that rethinks the impact of technology on the lives of ordinary people during the Industrial Revolution. During this project, we decided to address a fundamental question: what did people mean by ‘machine’ and how has this meaning changed over time?</p> <p>This paper outlines how a simple research question like 'what was a machine?' can provide an opportunity to engage the public with our work while also generating data for analysis and new avenues of research in a radically collaborative way.</p>
Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters
<p>Simulation data for "Nonlinear electron phase-space dynamics in spontaneous excitation of falling-tone chorus" submitting to Geophysical Research Letters.</p> <p>Including the simulation input parameter file and the necessary output data for analysis described in the article. The output data consists of waveform data, wave intensity profile, binned phase space distribution, etc. A detailed guide to load the output data is included in the zipped file as well. </p>
Books from the OpenAIRE Research Graph
<p>This dataset is the subset of the OpenAIRE Research Graph about research products of type "Book".</p> <p>The tar archive contains gz files, each with one json per line. Each json compliant to the schema available at <a href="http://doi.org/10.5281/zenodo.5799514">http://doi.org/10.5281/zenodo.5799514</a>. </p>
Alcohol and Drug Abuse Research Program (ADARP) Dataset
<p>The Alcohol and Drug Abuse Research Program (ADARP) dataset was collected as a part of a pilot study that aimed to discover how the daily experiences of patients diagnosed with alcohol use disorder (AUD) correspond with physiological biomarkers of stress. Each participant completed three components: 1) a daily diary study using ecological momentary assessment (EMA) of self-reported emotions, cravings, and stress via a web-based survey, prompted 4 times daily for up to 14 days; 2) continuous monitoring of stress with an Empatica E4 wristband that captured, in real-time, continuous physiological markers of stress, including heart rate (HR), skin conductance or electrodermal activity (EDA), skin temperature, and bodily movements; and 3) structured qualitative interviews to assess daily alcohol use, using a timeline follow-back calendar, and to validate self-reported and physiological markers of stress. With the proposed study, we aimed to address three research objectives. Aim 1 examines the fluctuations and associations among the self-reported emotions, alcohol-related cravings, and stress assessed in the EMA component. Analysis for Aim 1 centered on concurrent and lagged associations among negative affect, cravings, and stress. Aim 2 uses the continuous monitoring data acquired via the wearable device to visualize and describe fluctuations among the physiological measures of stress. Analysis for this aim focused on determining the degree of synchrony among the different physiological indicators. Aim 3 combines the EMA and continuous monitoring components in order to determine if self-reported changes in alcohol-related cravings can be predicted from physiological measures of stress. </p> <p>ADARP dataset was collected from participants suffering from alcohol use disorder (AUD) and receiving treatment at an outpatient treatment agency during 2019 - 2020, at Washington State University (WSU), Pullman, WA, USA. Funding for the original study was provided by the Alcohol and Drug Research Program (ADARP) of Washington State University. This investigation was supported in part by funds provided for medical and biological research by the State of Washington Initiative Measure No. 171.</p> <p>The sensor and EMA survey data are made public to facilitate further research into this topic. We have also set up a GitHub <a href="https://github.com/rameshKrSah/ADARP_Dataset">repository </a>with code that can be used to process the sensor data and extract meaningful information. Please cite the following papers if you use this dataset in your research. </p> <ol> <li>Ramesh Kumar Sah, Hassan Ghasemzadeh, Assal Habibi, Michael McDonell, Patricia Pendry, and Michael Cleveland. 2020. Poster: Mobile Health for Alcohol Recovery and Relapse Prevention. In 2020 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE). IEEE Press, 18–19. https://doi.org/10.1145/3384420.3431779</li> <li>Alinia P, Sah RK, McDonell M, Pendry P, Parent S, Ghasemzadeh H, Cleveland MJ. Associations Between Physiological Signals Captured Using Wearable Sensors and Self-reported Outcomes Among Adults in Alcohol Use Disorder Recovery: Development and Usability Study. JMIR Form Res. 2021 Jul 21;5(7):e27891. DOI: 10.2196/27891. PMID: 34287205; PMCID: PMC8339978.</li> <li>Ramesh Kumar Sah, Michael McDonell, Patricia Pendry, Sara Parent, Hassan Ghasemzadeh, Michael J Cleveland. ADARP: A Multi Modal Dataset for Stress and Alcohol Relapse Quantification in Real Life Setting. ArXiv, 2022, Jun. </li> </ol> <p>If you have any questions or suggestions, please feel free to reach Ramesh Sah at ramesh.sah@wsu.edu</p>
Core bibliometric Covid19 and comparable research dataset and code for the study "From intent to impact: Investigating the effects of open sharing commitments"
<p>This document provides the underlying dataset for the bibliometric component for the 2022 study "From intent to impact: Investigating the effects of open sharing commitments" by Research Consulting and Science-Metrix.</p> <p>Before reproducing the study findings or re-using the underlying datasets for other purposes, please cautiously review their limitations in the study's technical annex and main report, available at: https://zenodo.org/communities/data-sharing-in-public-health-emergencies/ </p> <p>Particularly, note that there is an error rate in attribution of signatory status to journal publications and preprints; in their location within specific thematic disease-based areas; or computing of dimension such as identification of data availability statement sections; identification of data depisition mentions within data availability statement sections; or matching of preprints and journal publications.</p> <p>These error rates are expected and have been estimated, please consult the technical report for full details.</p> <p> </p> <p>Definition of data fields is provided is the table below:</p> <table> <tbody> <tr> <td>Column name </td> <td>Definition</td> </tr> <tr> <td>document_type</td> <td>preprint or journal publication</td> </tr> <tr> <td>doi</td> <td>digital object identifier</td> </tr> <tr> <td>arxiv_id</td> <td>arXiv preprint server's unique identifier for its preprints</td> </tr> <tr> <td>ssrn_id</td> <td>SSRN preprint server's unique identifier for its preprints. Note that some of these IDs are contained within the DOIs also assigned to some (but not all) SSRN preprints , in the form of "10.2139/ssrn." + 'ssrn_id'</td> </tr> <tr> <td>coalesce_id</td> <td>coalesce function applied to the DOI, arxiv_id and ssrn_id. Redundant for journal publications.</td> </tr> <tr> <td>preprint_server</td> <td>Preprint platform on which a preprint has been published, restricted to arXiv, bioRxiv, medRxiv and SSRN for this study.</td> </tr> <tr> <td>journal_title</td> <td>Publishing journal name in the case of a journal publication.</td> </tr> <tr> <td>year</td> <td>The set is restricted to 2020 and 2021 for Covid19 preprints and journal publications. HVRD journal publications restricted to 2018-2019. HVRD preprints were restricted to 2020-2021 instead, to compensate for the lac of year-normalization for preprints, and generally better control findings against the launch of medRxiv in 2019.</td> </tr> <tr> <td>publication_title</td> <td>Title of the individual journal publication or preprint, not that of the publishing journal or preprint server.</td> </tr> <tr> <td>authors</td> <td>First 100 researchers that appear as authors of a preprint or journal publication. These are not parsed and provided for qualitative validation or assessments rather than for further quantitative treatment.</td> </tr> <tr> <td>Covid19</td> <td>Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>HVRD</td> <td>Human viral respiratory disease, the thematic area considered to be the closest to Covid19. Journal publications or preprints are coded 1 if they has been identified as falling into this thematic area through our queries (see the technical annex), 0 otherwise</td> </tr> <tr> <td>Journal_sig</td> <td>Journal publications where the publishing journal and/or its publishing house are Joint Statement signatories. Coded as 1 if they are signatories, 0 if not signatory, null if status could not be determined due to insufficient metadata. Not that all preprint servers included in this study are Joint Statement signatories. This category was fully removed from the models for preprints, rather than all preprints being assigned automatic signatory status.</td> </tr> <tr> <td>RPO_sig</td> <td>Journal publications and preprints where at least one author is affiliated with at least one research performing organization that is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata.</td> </tr> <tr> <td>Funder_sig</td> <td>Journal publications and preprints where at least one funder supporting the research is a Joint Statement signatory. Coded as 1 ifor signatory, 0 if not signatory, null if status could not be determined due to insufficient metadata. Although funding is attributed to researchers rather than publications, funding metadata is more readily available at the second level. This approach also captures the flexible usage of financial resources that researchers may make accross mulitple concurrently ongoing research projects.</td> </tr> <tr> <td>overton_norm</td> <td>Year and subfield-normalized binary score of whether the journal publications has been cited by one or more policy-related documents from the Overton database. Null scores for journal publications not covered by the database.</td> </tr> <tr> <td>overton</td> <td>Normalizations being unable for preprints, binary score of whether the preprint has been cited by one or more policy-ralated documents from the Overton database. Null scores for preprints not covered by the database.</td> </tr> <tr> <td>daswriting_binary</td> <td>Binary score capturing identification of a data availability statement in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>deposition_binary</td> <td>Binary score capturing identification of a data availability statement and data deposition mention therein in the journal publication or preprint using the queries presented in the technical annex. Null scores are for publications and preprints where records of full texts were unavailable for text mining, or were this analysis could not be performed due to licensing restrictions. </td> </tr> <tr> <td>is_oa</td> <td>Binary score capturing OA or free-to-read (also so-calleod "bronze OA" and "green OA") status of journal publications. Unpaywall categories have been used in a mutually exclusive implementation, with the best (gold > hybrid>bronze>green) possible applicable category being retained. Null scores for journal publications not covered in our Unpaywall dataset. Scores of 0 denote journal publications not available under an OA or free-to-read category.</td> </tr> <tr> <td>is_gold</td> <td>as above</td> </tr> <tr> <td>is_hybrid</td> <td>as above</td> </tr> <tr> <td>is_bronze</td> <td>as above</td> </tr> <tr> <td>is_green</td> <td>as above</td> </tr> <tr> <td>matched_journal_binary</td> <td>For preprints, whether one or more matching journal publications could be identified using the queries identified in the technical, or preprint servers' own lists of preprint-journal publication matches. Null scores for preprints with insufficient metadata information to perform the matching operation.</td> </tr> <tr> <td>matched_journal_doi</td> <td>For those preprints with or more matching journal publications, the DOI(s) of the matching journal publication(s). Note that some of the maching journal publications identified do not have DOIs.</td> </tr> <tr> <td>matched_preprint_binary</td> <td>For journal publications, whether one or more matching preceding preprints could be identified using the queries identified in the technical annex, or preprint servers' own lists of preprint-journal publication matches. Null scores for journal publications without sufficient metadata to run the analysis.</td> </tr> <tr> <td>matched_preprint_id</td> <td>For those journal publications preceded with one or more arXiv, bioRxiv, medRxiv or SSRN preprints, the DOI(s), arXiv ID and/or SSRN ID of the matching preprint(s). </td> </tr> <tr> <td>hasdoi</td> <td>Only journal publications with DOIs were retained in the core quantitative analyses.</td> </tr> <tr> <td>hasacknowledgements</td> <td>Only journal publications with funding acknowledgements (to determine funding-based signatory status) were retained in the core quantitative analyses.</td> </tr> <tr> <td>funder_array</td> <td>Array (but cast as string) of names of the funders on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>RPO_array</td> <td>Array (but cast as string) of names of the research performing organizations on the basis of whose idenitification signatory status has been attributed, where relevant. Null if non-signatory or unknown signatory status.</td> </tr> <tr> <td>DAS_excerpt</td> <td>Journal publication or preprint text excerpt on which succesful identifcation of data availability statements and/or data deposition mentions have been made. Null both where the query could not be run at all, or where the query was negative.</td> </tr> <tr> <td>big5</td> <td>Journal publication published in a journal owned by one of the following five publishing houses: Elsevier, Sage, Springer Nature, Taylor-Francis, Wiley.</td> </tr> <tr> <td>LMIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a lower-middle income country as defined by the World Bank</td> </tr> <tr> <td>LIC</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a low income country as defined by the World Bank</td> </tr> <tr> <td>SouthNorth</td> <td>Journal publication whose authors include at least one researcher affiliated with at least one institution located in a upper-middle income country, a lower-middle income country, or a low income country as defined by the World Bank; as well as at least one researcher affiliated with at least one institution located in a high income country. For the purpose of this indicator, Sicnece-Metrix exceptionally includes China and Bulgaria in the list of high income countries.</td> </tr> <tr> <td>DID_allauthors_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_OR</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as EITHER holding journal-based signatory status OR funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_authorcontrol_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where a filter has been applied to control for author-level biases.</td> </tr> <tr> <td>DID_allauthors_AND</td> <td>Journal publication is included in the difference-in-difference model defining signatory publication as holding journal-based signatory status AND funding-based signatory status, and where no filter has been applied to control for author-level biases.</td> </tr> <tr> <td>Preprint_authorcontrol</td> <td>Preprint is included in the the analytical breakdowns where a filter has been applied to control for author-level biases. Note that authors have been kept constant in preprints on the basis of their belonging to all analytical breakdowns in journal publications rather than in preprint-based groups.</td> </tr> </tbody> </table> <p> </p>
Data from Finnish Research Data Management Training Survey 2020-2021
<p>This is the survey data used in The Finnish Research Data Management Training Survey 2020-2021. The survey was sent to 74 Finnish research organizations of which 36 responded. The aim of the report was to gain a deeper understanding of what kind of research data management (RDM) training activities are provided by different Finnish organizations.</p> <p> </p>
Gregory-MS: list of research papers relevant/not relevant for Multiple Sclerosis research (for machine learning training)
<p>This dataset represents the list of research papers' data that was used to train and test different machine learning algorithms in the Gregory-MS project. The list includes the title and abstract (when available) of the research papers and an annotated field (relevant) that specifies if the given research paper is relevant or not for multiple sclerosis research.</p>
Research Organization Registry (ROR) Data in Sqlite Database
<p>Version 1.1 of the Research Organization Registry data translated into a SQLite database using ROR2DB (https://github.com/Metadata-Game-Changers/ROR2DB).</p> <p>This version adds country, country_code, and status to the ror table.</p>
Mehrabi et al. 2022. Research priorities for global food security under extreme events. Supplementary data and code.
<p>Data and script for reproducing the final results shown in Mehrabi et al., Research priorities for global food security under extreme events, One Earth (2022), https://doi.org/10.1016/j.oneear.2022.06.008.</p> <p>Simply download and read the Mehrabi2022_EEGFS.pdf or the Mehrabi2022_EEGFS.Rmd file from which it was created.</p>
Model outputs of Wei et al. (2022): "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophysical research Letters.
<p>The .mat file includes all model data used in the study "Salt intrusion as a function of estuary length in periodically weakly stratified estuaries", published in Geophyscial Research Letters, 2022. The .txt file contains description of all physical variables contained in the .mat file.</p>
Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters
<p>This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters.</p> <p>Contact: Yohei Yamada (yoheiy@jamstec.go.jp)</p>
Research data for `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'
<p>Research data associated with the paper `Quantifying information scrambling via Classical Shadow Tomography on Programmable Quantum Simulators'. Contains raw data obtained from simulations run on the IBM quantum device ibm_lagos.</p>
Research Data Stewardship Survey - University College Cork
<p>This survey aimed to help us gain an understanding of research data stewardship activities in UCC, the scope of those activities, identify any gaps in current resources and skills and work out where the Research Data Service fits with related roles and services. We hoped this activity would also help with the development of a data stewardship network across UCC for support, skills sharing, peer learning and the development of tailored skills development programs within UCC. It would also provide an evidence base to inform the model UCC should adopt in meeting its future research data requirements.</p> <p><br>Funders and publishers increasingly require researchers to formally manage their data and encourage or mandate FAIR and/or Open Data outputs. Both the National and European Codes of Research Conduct recognise that data management is central to research integrity and the quality and trustworthiness of research outputs across all disciplines. Research infrastructures in Europe are currently in a phase of development with continued expansion of the European Open Science Cloud (EOSC) and related<br>services. Successive reports internationally (Realising the EOSC, 2016, Turning FAIR into a Reality, 2018) and our own recently compiled National Landscape Report (NORF, 2021) highlighted a resource and skills gap in meeting the expectations and potential of FAIR research data and related research<br>infrastructures. Specifically, in relation to FAIR and Open Data, a set of skills, competencies, and responsibilities have been identified and grouped together under the umbrella of a new “Research Data Steward” role. Research data stewardship encompasses all the various tasks and responsibilities that<br>relate to research data management throughout the entire research lifecycle. The role of data steward is not universally defined yet and is influenced by the context and the needs of the researcher or unit. Across Europe, Research Performing Organisations have taken concrete steps to address this gap, for example by appointing new data steward positions or by re-focusing existing institutional skills and supports into designated competency centres for research data supports. TU Delft is an exemplar where eight newly established embedded data stewards, with domain expertise in the relevant faculty, complement a similar number of support staff based in central services such as the Library and IT Services.</p> <p><br>In UCC the Research Data Service provides a range of data stewardship supports to the research community from advisory to tailored training. The Research Data Service and Research Data Coordinator work closely with related services and roles to provide holistic advice on research data management to the UCC research community. The Clinical Research Facility–Cork has also developed a data stewardship service which is available on a consultancy basis to funded human focused research projects. However, the ask of researchers in terms of funder mandated data management plans and commitments to FAIR and Open Data continues to increase. Certainly in the case of the Research Data Service full capacity is fast approaching. As funders embed Open Science, and by extension data management, FAIR, and Open Data more firmly in their policies and requirements there is a risk that this will impact the competitiveness of our funding applications and the reach, impact and quality of our research outputs if we cannot meet researchers increasingly complex needs for research data stewardship support.</p> <p>We know that there are those engaged in research data stewardship activities throughout UCC although this may not be reflected in their job title. Those who engage in research data stewardship activities do not always identify as Data Stewards but contribute significantly to the data management lifecycle associated with research projects. Each stage of a research project can have specialist data stewardship requirements - these tasks are performed by people in a range of roles and positions including researchers, project managers, data managers, statisticians and data analysts, research assistants, technicians, systems administrators, or research software engineers to name but a few. To develop a holistic and coordinated approach data stewardship and research data management we needed to hear from the whole research ecosystem, those engaged in research and those facilitating it.</p>
Ethics of Research Funding: pilot study dataset
<p>Anonymized data set of the pilot study for the ethics of research funding survey. To anonymize the data, the responses to question C2 (What is the main location of your professional activities) have been removed. The publication of these anonymized data has been approved by the ethics committee of KU Leuven, and was indicated clearly in the informed consent of the survey.</p>
Research data in support of: An Interplay of Mechanical and Structural Properties of DNA Determines Its Electrostatic Interactions with Lipids
<p>The data collected and reported for the publication entitled: An Interplay of Mechanical and Structural Properties of DNA Determines Its Electrostatic Interactions with Lipids. by Diana Morzy et al.</p> <p>Data is divided by the technique used, with folders named accordingly. Each dataset has a readme file, explaining the basic technical details, as well as how to open each file type.</p> <p>Please do not hesitate to contact the corresponding author (MMCB) for further details.</p>
The research of River Morphology transition and Sediment variation: Shule River, Northwest of China
<p>The data acquisition in this study is mainly divided into two parts: indoor statistics and field measurements to obtain. In the indoor work, satellite image data and radar digital elevation data were primarily used to measure and count the river width (Fig.1c), sinuosity, gradient, and elevation every 500m along the top of the Shule River downward (Tab.1). The river width was calculated as the distance between the outer banks, measured at a 90° angle to the river axis, including the channel bar and point bar (Mcglue et al., 2016). The classification of river morphology is mainly based on the size of sinuosity (Rust, 1978). The sinuosity greater than 1.5 is defined as a meandering river, and less than 1.5 is defined as a braided river (Fig.1c). The river gradient is counted for every two adjacent measurement points. According to the above measurement criteria, there are 237 river morphology data within the alluvial fan of the Shule River (Tab.1).</p> <p>In the field measurement process, due to the limited accuracy of satellite images in portraying river morphology. We also use UAV aerial photography to refine further the river's morphological characteristics based on satellite images, which mainly included the channel bar and point bar description. Under the guidance of sedimentological theory, we measured and sampled the gravel in the modern riverbed of Shule River (Fig.2). By measuring the grain size and orientation parameters of gravel(Fig.2a), we research the refinement characteristics of sediments from the apex to the toe (Folk, 1954). Among them, gravel grain size and orientation were measured by the quantitative characterization method of gravel orientation proposed by Huang YuanGuang et al. (Fig.2a, b), and grain size was determined by the long flat axis of gravels (Huang et al., 2018), gravel orientation was measured by the rose diagram of the relative apparent dip (Fig.2b) (Huang et al., 2018; Tao et al., 2018). A total of five gravel statistical points were included within the alluvial fan of the Shule River, and a total of 1862 gravel grain size parameters were measured (Tab.2). We also use the hand-hold X-ray fluorescence spectrometer to measure the element characteristics of each sampling point (Fig.2c, d; Tab.3), which uses intelligent one-button testing and intelligent judgment functions for elements between atomic numbers 12-92 (Mg-U) (Fig.2e; Tab.3).</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.