This image dataset contains 4,633 real and 4,060 simulated maize images. The real images were captured in the greenhouse of the University of Nebraska-Lincoln’s Greenhouse Innovation Center for the maize 282 association panel. All the real maize images were scored for leaf number using the Zooniverse crowdsourcing platform. The simulated maize images were produced from the PlantFactory and a customized Blender.

README

Description

This data set consists of two sessions of resting-state spinal cord fMRI data from 48 healthy participants. This data set was collected as a part of a larger methodological project (see https://doi.org/10.1002/hbm.26018). Data from these 48 participants have already been shared (https://openneuro.org/datasets/ds004068/versions/1.0.3), but here we provide previously unavailable resting-state fMRI data associated with the following manuscript: PREPRINT LINK. For each participant, we share i) a T2-weighted anatomical image, ii) two resting-state fMRI acquisitions of 250 volumes each (acquired with manual and automated slice-specific z-shimming), and iii) associated peripheral physiological data (ECG and respiratory recordings). For a detailed description, please see:

PREPRINT LINK

Citing this dataset

Should you make use of this data set in any publication, please cite the following article:

PREPRINT LINK

License

This data set is made available under the Creative Commons CC0 license. For more information, see https://creativecommons.org/share-your-work/public-domain/cc0/

Data set

This data set is organized according to the Brain Imaging Data Structure (BIDS) specification. For more information on BIDS, see https://bids-specification.readthedocs.io/en/stable/ Each participant’s data are in one subdirectory (e.g., sub-ZS001), which contains the raw NIfTI data (after DICOM to NIfTI conversion) for this particular participant, as well as the associated metadata. Raw and processed peripheral physiological data can be found in each participant’s subdirectory under the “derivatives” folder. Manually obtained or adjusted MRI-based derivatives (e.g., spinal cord masks, segmental labels) are also shared for each participant and can be found in each participant’s subdirectory of the “derivatives” folder. For more details about the preprocessing pipeline and the description of each derivative, please see the following links: https://github.com/eippertlab/restingstate-reliability-spinalcord/ and PREPRINT LINK. Please note that data from three participants (sub-ZS009, sub-ZS018, sub-ZS030) are excluded from all analyses due to technical errors in the acquisition of peripheral physiological data, but their datasets are still provided for the sake of completeness. Should you have any questions about this data set, please contact mkaptan@stanford.edu or eippert@cbs.mpg.de.

Citation metrics are widely used and misused. We have created a publicly available database of top-cited scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator (c-score). Separate data are shown for career-long and, separately, for single recent year impact. Metrics with and without self-citations and ratio of citations to citing papers are given and data on retracted papers (based on Retraction Watch database) as well as citations to/from retracted papers have been added in the most recent iteration. Scientists are classified into 22 scientific fields and 174 sub-fields according to the standard Science-Metrix classification. Field- and subfield-specific percentiles are also provided for all scientists with at least 5 papers. Career-long data are updated to end-of-2023 and single recent year data pertain to citations received during calendar year 2023. The selection is based on the top 100,000 scientists by c-score (with and without self-citations) or a percentile rank of 2% or above in the sub-field. This version (7) is based on the August 1, 2024 snapshot from Scopus, updated to end of citation year 2023. This work uses Scopus data. Calculations were performed using all Scopus author profiles as of August 1, 2024. If an author is not on the list it is simply because the composite indicator value was not high enough to appear on the list. It does not mean that the author does not do good work. PLEASE ALSO NOTE THAT THE DATABASE HAS BEEN PUBLISHED IN AN ARCHIVAL FORM AND WILL NOT BE CHANGED. The published version reflects Scopus author profiles at the time of calculation. We thus advise authors to ensure that their Scopus profiles are accurate. REQUESTS FOR CORRECIONS OF THE SCOPUS DATA (INCLUDING CORRECTIONS IN AFFILIATIONS) SHOULD NOT BE SENT TO US. They should be sent directly to Scopus, preferably by use of the Scopus to ORCID feedback wizard (https://orcid.scopusfeedback.com/) so that the correct data can be used in any future annual updates of the citation indicator databases. The c-score focuses on impact (citations) rather than productivity (number of publications) and it also incorporates information on co-authorship and author positions (single, first, last author). If you have additional questions, see attached file on FREQUENTLY ASKED QUESTIONS. Finally, we alert users that all citation metrics have limitations and their use should be tempered and judicious. For more reading, we refer to the Leiden manifesto: https://www.nature.com/articles/520429a

Inspiration

Can Machine learning predict the food prices? train data contain data collected before Covid-19. test data contain data collected after Covid-19.

License: Creative Commons Attribution for Intergovernmental Organisations .

Data extraction table for the manuscript "Quick review: Monitoring the presence and infectivity of SARS-CoV-2 and other coronaviruses in wastewater."

Real, long-term data collected from three participants using a Place Lab client, from which the authors extract significant places. ; jhkang@cs.washington.edu

This dataset contains the digitized treatments in Plazi based on the original journal article Waqa-Sakiti, Hilda, Winder, Linton, Lingafelter, Steven W. (2015): Review of the genus Ceresium Newman, 1842 (Coleoptera, Cerambycidae) in Fiji. ZooKeys 532: 15-53, DOI: http://dx.doi.org/10.3897/zookeys.532.6070, URL: http://dx.doi.org/10.3897/zookeys.532.6070

Legal Speed Limit (State and Local Roads)The maximum speed limit permitted under the provisions of the State Traffic Act and Regulations. Road speed limits are used to regulate the speed of vehicles and may define maximum (which may be variable), minimum, or no speed limit. These limits are indicated using traffic signs.Speed limits are set by the Commissioner of Main Roads Western Australia (Regulation 297 of the Road Traffic Code 2000) and enforced by national or regional police and/or judicial bodies. The speed limit displayed in this layer may not be current or accurate. The only enforceable source of speed limits is the road sign at the relevant physical location.Update FrequencyUpdates to the ArcGIS layer are triggered by changes in IRIS data and refreshed weekly.DisclaimerThis layer shows the location of legal speed limits on all public access roads included in the Integrated Road Information System (IRIS) and is provided for informational purposes only.Please note that you are accessing this data under a Creative Commons Attribution Licence, which includes a disclaimer of warranties and a limitation of liability. You acknowledge that the data provided under this licence is subject to change and may not be current or accurate. The only enforceable source of speed limits is the road sign at the relevant physical location.Important Usage NoticeWhile this dataset may be used under the terms of the Creative Commons Licence, Main Roads WA does not recommend its use in navigation systems or applications requiring real-time or precise speed limit data. The dataset is not maintained for operational purposes and may not reflect current or signed speed limits. Main Roads WA is not liable for the use of this data, including in navigation or compliance tools.Licence NoticePursuant to Section 3 of the Licence, you are provided with the following notice to include when sharing the licensed material:The Commissioner of Main Roads is the creator and owner of the data and licensed material, which is accessed under a Creative Commons Attribution Licence. This licence includes a disclaimer of warranties and a limitation of liability. The data may not be current or accurate. The only enforceable source of speed limits is the road sign at the relevant physical location.LicenceCreative Commons CC BY 4.0https://creativecommons.org/licenses/by/4.0/Data Domain StewardData Planning and Standards ManagerData CustodianData and Systems ManagerOperational Data StewardData Planning and Standards ManagerCoordinate System TypeGeographic (unprojected, EPSG:4283 – GDA94)ReferencesSigns – Regulatoryhttps://portal-mainroads.opendata.arcgis.com/datasets/3768b2d6a8fe4e3fa9c8b53197ba3b5e_22/To explore the meaning of regulatory signs, refer to the Panel_01_design_meaning attribute.For speed-related signage, filter or search this attribute using the keyword "speed".