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4,480 results for “initiative”
anonymized author dataset from the publication "Impact of the COVID-19 pandemic on publishing in astronomy in the initial two years"
<p>We provide the anonymized author dataset that was prepared for the research presented in <a href="https://arxiv.org/pdf/2203.15621.pdf">https://arxiv.org/pdf/2203.15621.pdf</a> . The data is provided as two pickled pandas data frames. The first data frame contains the number of publications each author has written in each year per author position (as 1st author etc.) and their assigned gender. The second file contains the corresponding country of affiliation for each author and year. The two data frames can be matched by author_id.</p> <p>Columns in "matched_author_dataset_publication_counts_zenodo.pkl":</p> <pre>['author_id', 'gender', 'P(gender)', 'pub1_tot', 'first_auth_pub_year', 'pub2_tot', 'auth_2_pub_year', 'pub3_tot', 'auth_3_pub_year', 'pub4_tot', 'auth_4_pub_year', 'pub5_tot', 'auth_5_pub_year', 'pub6_tot', 'auth_6_pub_year', 'pub7_tot', 'auth_7_pub_year', 'pub8_tot', 'auth_8_pub_year', 'pub9_tot', 'auth_9_pub_year', 'pub10_tot', 'auth_10_pub_year', 'pub11_tot', 'auth_11_pub_year', 'pub12_tot', 'auth_12_pub_year', 'pub13_tot', 'auth_13_pub_year', 'pub14_tot', 'auth_14_pub_year', 'pub15_tot', 'auth_15_pub_year', 'pub16_tot', 'auth_16_pub_year', 'tot_pub', 'tot_pub_year', 'last_pub', 'first_pub']</pre> <p>'author_id' is a unique id assigned to the author. 'gender' contains the author's most likely gender and 'P('gender')' the likelihood of correct assignment. 'pubX_tot' contains the total number of publications the author has written as Xth author, 'auth_X_pub_year' contains a list with one entry per year from 1950 to 2022 counting the number of papers the author has published as Xth author in that year. 'tot_pub' counts the total number of publications (summed over all years) and 'tot_pub_year' for each year/ 'last_pub' and 'first pub' contain the list indices of the years when the author last/first published.</p> <p>Columns in author_dataframe_country.pkl:</p> <pre>['author_id', 'aff_year', 'aff_country_author']</pre> <p>'aff_year' is a list of years for which country information could be inferred for this author. 'aff_country_author' is a list of countries for each entry in 'aff_year'.</p> <p> </p>
WRF initial condition files
<p>This dataset contains initial conditions files, namelist file and a piece of source code for running the WRF idealised baroclinic wave simulation. With these files you can reproduce the results which will be described in the upcoming paper by Rantanen et al.</p>
pyigm/pyigm: Initial release for publications
<p>This is the first release of pyigm to give it a DOI and thereby enable it to be cited.</p> <p>It is a combination of software and published datasets.</p>
Initiation of downward positive leader beneath the negative leader channel
<p>The data supports the manuscript entitled "Initiation of downward positive leader beneath the negative leader channel" and MATLAB can open these *.fig and *.mat files.</p> <p>MATLAB Code ReadTdms.m can open these *.tdms. The data of *.cine can be opened by Phantom Camera Control software that can be download from this link: </p> <p>https://www.phantomhighspeed.com/resourcesandsupport/phantomresources/pccsoftware </p> <p>The data can be used freely for scientific purposes with the appropriate citation. </p>
ScintiPi3 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives"
<p>ScintPi 3.0 data sets for "First observations of severe scintillation over low-to-mid latitudes driven by quiet-time extreme equatorial plasma bubbles: conjugate measurements enabled by citizen science initiatives" by Sousasantos et al. (2024).</p>
Data from: Great Lakes Restoration Initiative human wellbeing survey
<p>This dataset represents a comprehensive exploration of ecosystem restoration practices and their impacts on both ecological and human wellbeing indicators. Traditionally, ecosystem restoration efforts have focused on ecological benchmarks such as water and habitat quality, species abundance, and vegetation cover. However, there is an increasing recognition of the interplay between restoration and human communities, evidenced by positive socio-ecological connections like property value, natural hazard mitigation, recreation opportunities, and overall happiness. With the United Nations declaring 2021-2030 as the "Decade of Ecosystem Restoration" and a push for more socio-ecological goals in restoration, this dataset delves into the degree to which restoration practitioners consider human wellbeing. It is based on a case study of the Great Lakes Restoration Initiative (GLRI), a federally funded program that has awarded over $3.5 billion to 5,300 projects. A total of 1,574 GLRI projects were surveyed, with 437 responses received, revealing that almost half of these projects set human wellbeing goals, and more than 70% believed they achieved them. In comparison, 90% of project managers believed they met their ecological goals. This dataset highlights the documented perceptions of positive impacts on both people and nature, suggesting that restoration efforts often go beyond traditional indicators. As such, it advocates for the adoption of a socio-ecological perspective in ecosystem restoration programs to comprehensively document the full extent of restoration outcomes. The data collection process included a survey methodology, and the dataset provides insights into project design, implementation, and success measurements. The data was collected between November 2020 and March 2021, with a maximum of three contact attempts for each project. It offers a unique perspective on the relationship between ecosystem restoration and human wellbeing, emphasizing the importance of capturing the often "unseen" benefits of these projects.</p>
Data from: Neuroprotection provided by hypothermia initiated with high transnasal flow with ambient air in a model of pediatric cardiac arrest
<p>Clinical trials of hypothermia after pediatric cardiac arrest have not seen robust improvement in functional outcome, possibly because of the long delay in achieving target temperature. Previous work in infant piglets showed that high nasal airflow, which induces evaporative cooling in the nasal mucosa, reduced regional brain temperature uniformly in half the time needed to reduce body temperature. The mouth is kept open to allow the high nasal airflow to easily exit. Here, we evaluated whether initiation of hypothermia with high transnasal airflow (32 L/min) provides neuroprotection without adverse effects in the setting of asphyxic cardiac arrest. Anesthetized, mechanically ventilated piglets (approximately 2-weeks-old) underwent sham-operated procedures (Group 1) or asphyxic cardiac arrest (Groups 2-6). The asphyxic insult consisted of reducing the inspired oxygen from 30% to 9.5-10% for 45 minutes (hypoxia period), then briefly increasing the inspired oxygen to 21% for 5 min (to improve the later success of cardiac resuscitation), and then completely stopping ventilation for 7 minutes. Cardiopulmonary resuscitation (CPR) commenced by re-establishing ventilation, performing chest compression, and injecting epinephrine as needed. The five cardiac arrest groups were further divided into those with normothermic recovery (38.5°C; Group 2), with mild hypothermia (34°C) initiated by surface cooling at 10 minutes (Group 3) or 120 minutes (Group 5) after resuscitation, or with mild hypothermia (34°C) initiated by transnasal cooling initiated at 10 minutes (Group 4) or 120 minutes (Group 6) after resuscitation. In the two transnasal cooling groups, the high nasal airflow continued for 2 hours and was then stopped; thereafter, surface cooling was used to maintain hypothermia. In all four groups with induced hypothermia, rectal temperature was sustained at the targeted temperature of 34°C with surface cooling until 20 hours after resuscitation, followed by 6 hours of gradual rewarming and cessation of fentanyl/70% nitrous oxide anesthesia.<strong> </strong>At four days of recovery, the piglets were euthanized and their brains were analyzed for the density of morphologically intact neurons in putamen, sensorimotor cortex, ventrolateral thalamus, and prefrontal cortex. The data sheet shows the density of viable neurons in these 4 brain regions for the 45 piglets that completed the study. The data sheet also shows the serial measurements of rectal temperature, mean arterial blood pressure, heart rate, and arterial blood measurements of the partial pressure of oxygen (PO2) and carbon dioxide (PCO2), oxyhemoglobin saturation, and pH obtained at baseline, during the period of hypoxia, at 4 minutes of ventilation with 21% O2, during the period of asphyxia, and during the first 24 hours of recovery. The piglets are assigned the same unique identifier number, labelled 1-45, for each set of measurements. Transnasal cooling initiated at 10 minutes after resuscitation was able to significantly rescue neurons in the highly vulnerable putamen without adverse effects.</p>
A Stochastically Generated meteorological forcings for the Laurentian Great Lakes to support the Great Lakes Restoration Initiative
<p>The dataset consists of three folders, each with time series for the Laurentian Great Lakes and their contributing watersheds: </p> <ol> <li>"observed_historical.zip" : containing daily forcings for Large Lake Thermodynamics Model and Large Basin Runoff Model and Overlake Precipitation time series from GLERL Hydrometeorological Database and consistent with the inputs of GLSGyFS software.</li> <li>"baseline.zip" : 1000-yr long simulated daily meteorological time series using the Stochastic Weather Generator, without any thermodynamic climate perturbations. </li> <li>"scenarios.zip" : 30 scenarios of each 1000-yr long simulated daily meteorological time series using the Stochastic Weather Generator with different thermodynamic perturbations, consistent with Coupled Model Intercomparison Project 6 (CMIP6) projections for the Great Lakes region. </li> </ol> <p> </p> <p><strong>Acknowledgements: </strong></p> <p>This work was funded by the Great Lakes Restoration Initiative (GLRI) Action Plan 3, Focus Area 5.2 Conduct Comprehensive Science Programs and Projects. Funding for this work was also provided through the National Science Foundation Grant No. CBET-2144332, and the U.S. Geological Survey Northeast Climate Adaptation Science Center, which is managed by the USGS National Climate Adaptation Science Center, under Grant/Cooperative Agreement No. G21AC10601-00. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the Northeast Climate Adaptation Science Center or the U.S. Geological Survey.</p>
Importance of Initial Condition on Bar Secular Evolution: Role of Halo Angular Momentum Discontinuity|Simulation Data
<p>This data includes the key snapshots of the simulations in GADGET-2 format. The folders, namely D0, D1, D2, and D3, correspond to the four galaxy models used in this study. The snapshots are printed from time=0 Gyr to 10Gyr in a timestep of 2 Gyr.</p>
Figure 4 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population
Figure 4. The overall abundance of Physa acuta on the eight collecting stations (CS).
Figure 5 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population
Figure 5. Relationship of shell height and width of Physa acuta from Ilha Grande.
Figure 2 in The introduction of Physa acuta (Gastropoda: Physidae) on Ilha Grande, Southeast Brazil, from initial stages to an established population
Figure 2. Variation of the total abundance of Physa acuta in an impacted stream of Ilha Grande.
Data underlying research paper: Exploring Governance Modes in Open Data Initiatives: Insights from France and Ireland
<p><span><span>The folder </span><span>contains</span> <span>the interview questions</span> <span>of</span><span> the research paper “</span></span><span><span>Exploring Governance Modes in Open Data Initiatives: I</span></span><span><span>nsights</span><span> from France and Ireland</span></span><span><span>” and consists of the interview questions. </span></span><span> </span></p> <div> <p><strong><span><span>Note about the interview </span><span>and the interview </span><span>questions:</span></span><span> </span></strong></p> </div> <div> <p><span><span>The </span><span>semi-structured </span><span>interviews were conducted with </span><span>(three) </span><span>experts selected through purposive</span> <span>sampling combined with snowballing approach to gain knowledge from</span> <span>key people who actively worked in the coordination bodies in France and Ireland. In</span> <span>the case of France, the first interviewee was reached following the reading of a blogpost</span> <span>on the governance of open data during the Covid-19 crisis. The first interviewee</span> <span>allowed the identification of a second expert who could complement his knowledge on</span> <span>the governance of the open data in France. In the case of Ireland, the expert was</span> <span>identified through the reading of an academic article that explained in great details the</span> <span>unfolding of the events that led to the Covid-19 open data strategy in Ireland. All</span> <span>the interviewees were approached through email exchange. Interviews took place in</span> <span>August 2022 online through the Microsoft Teams and Webinaire de l’État platforms,</span> <span>each lasting approximately forty-five minutes.</span></span><span> </span></p> <p><strong><span>Acknowledgment:</span></strong></p> </div> <p><span>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 955569. The opinions expressed in this document reflect only the author’s view and in no way reflect the European Commission’s opinions. The European Commission is not responsible for any use that may be made of the information it contains.</span></p>
The dataset of initial seed selection study
<p>The benchmarks include the three corpora used in the study.</p> <p>The JVMs include the 12 JVMs used in the study: 3 latest versions of OpenJDK8, 3 older versions of OpenJDK8, 3 latest versions of OpenJDK11, and 3 latest versions of OpenJDK17.</p> <p>UnprocessedData represents the data that has not yet been processed and needs to be converted into the format required for initial seed selection using the data processing module.</p> <p>ProcessedData represents the processed data, which can be directly used by the seed selection module to choose the subset of initial seeds.</p>
Data for "effects of fault contact heterogeneity on laboratory earthquake initiation and dynamic rupture"
<p>The second column in the files named by "Time_and_dLP", "Time_and_Mu0", "Time_and_Sigma0", and "Time_and_Tau0" indicate the along-fault loading point displacement (dLP), macroscopic friction coefficient (Mu0), macroscopic normal stress (Sigma0), and macroscopic shear stress (Tau0), respectively, measured in the loading apparatus. The first column in these files are time.</p> <p>Local fault displacement data are named by the form of, for example, "Event101_FaultDisplacement_L1(x=270mm)", which means that the fault displacement measured by Sensor L1 located at x=270 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_FaultDisplacement_Time".</p> <p>Local shear stress data are named by the form of, for example, "Event101_ShearStress_S1(x=-323.95mm)", which means that the shear stress measured by Sensor S1 located at x=-323.95 mm during stick-slip Event 101. Their corresponding time is save in the file named by "Event101_ShearStress_Time".</p>
openwashdata/ugabore: Initial package release
<p>Borehole repair data from central Uganda associated with a project report completed by Joseph Lwere for the "data science for openwashdata" course</p>
Initial model and Molecular Dynamics trajectory of WGR domain of PARP2 with ZN ions
<div>Files:</div> <div>1. Initial model of PARP2 WGR domain (based on PDB entry: 6F5B) with two sites potentially capable of binding zinc ions: one formed by residues E97, C98, H160 and another - by residues H106, C109, E138</div> <div>2. PARP2 WGR domain with Zn ions bound after energy minimization.</div> <div>3. 400 ns MD trajectory of PARP2 WGR domain with Zn ions.</div>
7PartidasDigital/7Partidas-autor: Initial release
<p>Data and script as used in the corrresponding paper</p>
Fig. 2 in Potential herbicidal effect of synthetic chalcones on the initial growth of sesame, Sesamum indicum L., and brachiaria, Urochloa decumbens (Stapf) R. D. Webster
Fig. 2. Chemical structures of synthesized chalcones and their respective yields.
Toward a better understanding of curve number and initial abstraction ratio values from a large-dataset of watersheds perspective
<p>The code within this repository is founded on the analysis of the Natural Resources Conservation Service Curve Number (NRCS-CN) method and its parameters. The analyses make use of the [CARAVAN dataset and your extensios] (<a href="../records/6578598" rel="nofollow">https://zenodo.org/records/6578598</a>). The separation of streamflow into baseflow and runoff is based on the methodology established by Xie et al. (2020) (<a href="https://doi.org/10.1016/j.jhydrol.2020.124628" rel="nofollow">https://doi.org/10.1016/j.jhydrol.2020.124628</a>).</p> <p>Contained within this repository are 5 Jupyter Notebooks, each focused on the application of the NRCS-CN method using distinct data analysis approaches:</p> <ul> <li>Principal Components Analysis</li> <li>Random Forest</li> <li>NRCS-NEH</li> <li>NRCS-ASYMPTOTIC </li> <li>NRCS-LEAST-SQUARES</li> </ul> <p>Additionally, the results of the metrics from applying these methods can be found in the files Metrics_runoff_G1 and Metrics_runoff_G2. The folder Times_series contains the time series of simulated runoff using the NRCS methods.</p> <p>Channel Log: </p> <p> Version v2 - Version of the official paper release. No changes in the data but added a static copy of the accompanying code of the paper. </p> <p>Version 1.0 - In this version, the Asymptotic method was considered with natural data</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.