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Normalized Difference Vegetation Index (NDVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Normalized Difference Vegetation Index (NDVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as fifteen individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf, png, and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_NDVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf, png, and kml index map). Javascript code used to process NDVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2010 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2010 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2010-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2013 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2013 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2013-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2015 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2015 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2015-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
Soil-Adjusted Vegetation Index (SAVI) derived from 2017 National Agriculture Imagery Program (NAIP) data for the central Arizona region
This project calculates the Soil-adjusted Vegetation Index (SAVI) from 2017 National Agriculture Imagery Program (NAIP) imagery (1-meter resolution) for the central Arizona region. Because of their large size, data (as GeoTIFF files) for each survey year are provided as multiple individual tiles each comprising a portion of the overall coverage area. An index of the relative position of each tile in the coverage area is provided as a pdf and kml where the tile index contains a portion of the GeoTIFF file name (e.g., the relative position of the data file NAIP_SAVI_CAP2017-0000000000-0000000000.tif to the overall coverage area is identified by the index id 0000000000-0000000000 in the pdf and kml index maps). Javascript code used to process SAVI values is included with this dataset. This data set is one in a series of NDVI and SAVI (Soil Adjusted Vegetation Index) data sets for the central Arizona region spanning multiple years (2010-2017). Related data are available through the Environmental Data Initiative - see resouce listing in the methods of this data set for references.
CCE LTER process cruise, in the California Current region, event log records including date, time, position and activity for use in post-cruise data integration based on co-sampling indexes. From 2006 to 2019 CCE LTER used a locally developed event logging system. During P2107, CCE LTER started to utilize the R2R Event Logger on UNOL ships, 2006 - 2024 (ongoing).
The event logger program developed and maintained by the California Cooperative Oceanic Fisheries Investigations, SIO, program is used aboard CCE LTER process cruises to create indexes with temporal, spatial and activity information for post-cruise data integration. The event log is configured aboard the ship for the recording of sampling events by both ship crew personnel on the bridge, and research personnel in the lab. The event log is processed post-cruise to correct for various errors.
Hubbard Brook Experimental Forest: Leaf Area Index (LAI) Watershed 1
Leaf area index (LAI) of the mature deciduous forest on WS1 at Hubbard Brook Experimental Forest is estimated on the basis of leaf litterfall collections; the raw data for litterfall are posted in the EDI data package – Fine Litterfall Data at the Hubbard Brook Experimental Forest, 1992 – present (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=49). This watershed was treated with a calcium silicate mineral (wollastonite) in 1999 to gradually replace Ca lost as a result of acid deposition. Leaf litterfall is collected in 0.097 m2 litter traps raised 1.5 m above ground level and is sorted by species. The number of leaves of each species is counted. The counts are multiplied by the average area per leaf for each species in each plot to estimate LAI. Litter traps are located randomly within each of three plots that are arranged along the elevation gradient within the deciduous forest zone.
Hubbard Brook Experimental Forest: Leaf Area Index (LAI) Bear Brook Watershed (West of Watershed 6)
Leaf area index (LAI) of the mature deciduous forest in the Bear Brook watershed (west of WS6) at Hubbard Brook Experimental Forest is estimated on the basis of leaf litterfall collections; the raw data for litterfall are posted in the EDI data package – Fine Litterfall Data at the Hubbard Brook Experimental Forest, 1992 – present (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=49). Leaf litterfall collected in 0.097 m2 litter traps is sorted by species. The number of leaves of each species is counted. The counts are multiplied by the average area per leaf for each species in each plot to estimate LAI. Litter traps are located randomly within each of four plots that are arranged along the elevation gradient within the deciduous forest zone. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Hubbard Brook Experimental Forest: Leaf Area Index (LAI) Throughfall Plots
Leaf area index (LAI) of the mature deciduous forest adjacent to WS6 at Hubbard Brook Experimental Forest is estimated on the basis of leaf litterfall collections; the raw data for litterfall are posted in the EDI data package – Fine Litterfall Data at the Hubbard Brook Experimental Forest, 1992 – present (https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=49). These plots are designated TF, referring to throughfall chemistry collections performed at these plots many years ago (Lovett et al. 1996). Leaf litterfall is collected in 0.097 m2 litter traps raised 1.5 m above ground level and is sorted by species. The number of leaves of each species is counted. The counts are multiplied by the average area per leaf for each species in each plot to estimate LAI. Litter traps are located randomly within each of three plots that are arranged along the elevation gradient within the deciduous forest zone. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station. Gary M. Lovett, Scott S. Nolan, Charles T. Driscoll, and Timothy J. Fahey. Factors regulating throughfall flux in a New Hampshire forested landscape. Canadian Journal of Forest Research. 26(12): 2134-2144. https://doi.org/10.1139/x26-242
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Leaf Area Index (LAI), 2004
Leaf area index (LAI) is commonly used to assess forest canopies, and is calculated as the area of all leaves per unit area of ground. In September 2004, LAI was measured in all Bartlett Experimental Forest stands (C1-C9) of the MELNHE study in New Hampshire, using an LAI-2000 Plant Canopy Analyzer. Variables reported are leaf area index (LAI), standard error of LAI (SEL), diffuse non-interceptance (DIFN), mean tip angle (MTA), standard error of mean tip angle (SEM), and sample size (SMP). Additional detail on the MELNHE project, including a data table of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
CBS03 Grasshopper sparrow surveys: densities, reproductive index, and locations of marked individuals on Konza Prairie
Data on the location, identity, and reproductive index (Vickery et al. 1992) of Grasshopper Sparrows prior to 2017, and after that, additionally many Dickcissels, Eastern Meadowlarks, Brown-headed Cowbirds and other songbirds within 10-ha plots on multiple watersheds units on Konza and on two adjoining units on the Rannells Preserve. Each plot was surveyed every ~7-10 days. These surveys documented individual sparrow, Dickcissel, and Eastern Meadowlark locations, and are used to calculate dispersal distances and territory densities and movements. Missing values in character fields denoted by NA, and in numeric fields, either -999 or -9.
Water Tower Index for Saddle Catchment, 2000 - 2018.
The Saddle Catchment of the Niwot Ridge LTER experiences significant spatial variation regarding snow distribution and, in turn, the timing and amount and surface water input generation (i.e., rainfall and snowmelt). In winter months and in areas that receive a high amount of redistributed snow (via wind), snow accumulation is large and the snowpack persists until the snowmelt season, creating a lag between the timing of precipitation (as snow) and the timing of surface water inputs (as snowmelt). Alternatively, areas that are scoured of snow, retain little snow. In these areas, rain is the primary source of surface water inputs, and thus there is little/no lag in the timing of precipitation and surface water inputs (as rain). The lag in the timing of precipitation and surface water inputs in the wind deposition zones, however, is critical to providing water to the surrounding and downstream environments later in the year. The snow in these areas thus act as a natural water tower to retain water (as snow) until the snowmelt season. To capture the timing and magnitude of the delay between precipitation and surface water inputs, a Water Tower Index (WTI) was generated. The WTI uses equations (see Methods) to fit a sine curve to annual precipitation (P) and annual surface water inputs (SWI). A third equation then compares to the phase and amplitude of the two sine curves, generating a metric between -1 and 1. Positive WTI values signify P and SWI out of temporal alignment (where WTI = 1 indicates strong (i.e., high amplitude) temporal misalignment between P and SWI). Negative WTI values signify P and SWI in temporal alignment (where WTI = -1 indicates strong (i.e., high amplitude) temporal alignment between P and SWI). P and SWI data were taken from DHSVM output run by Nels Bjarke, which included 19 years of information (WY2000-WY2018). The methodology was applied, spatially, across the Saddle Catchment of the Niwot Ridge LTER. In turn, this dataset reveals, importantl
MESINESP: Medical Semantic Indexing in Spanish - Development dataset
<p><em><strong>Please use the <a href="https://doi.org/10.5281/zenodo.4612274">MESINESP2 corpus (the second edition of the shared-task)</a> since it has a higher level of curation, quality and is organized by document type (scientific articles, patents and clinical trials).</strong></em></p> <p> </p> <p> </p> <p><strong>Introduction</strong></p> <p>The Mesinesp (Spanish BioASQ track, see https://temu.bsc.es/mesinesp) development set has a total of 750 records indexed manually by seven experienced medical literature indexers. Indexing is done using <em>DeCS codes, a sort of Spanish equivalent to MeSH terms</em>. Records were distributed in a way that each article was annotated, at least, by two different human indexers.</p> <p>The data annotation process consisted in two steps:</p> <ol> <li>Manual indexing step. DeCS codes were manually assigned to each record following the DeCS manual indexing guidelines.</li> <li>Manual validation and consensus. The joined set of manually indexed DeCS codes generated by both indexers were manually revised and corrections were done.</li> </ol> <p>These annotations were analyzed, resulting in an agreement using the Jaccard index.</p> <p>Records consisted basically in medical literature abstracts and titles from the IBECS and LILACS databases.</p> <p><strong>Zip structure</strong><br> The zip file contains two different development sets:</p> <ul> <li><em>Official development set</em>, which has the union of the annotations, with an agreement of macro = 0.6568 and micro = 0.6819. This set is composed by all the different (unique) DeCS codes that have been added by any annotator for each document; and</li> <li><em>Core-descriptors development set</em>, which has the intersection of the annotations, with an agreement of macro = 1.0 and micro = 1.0. This set is composed of the common DeCS codes that have been added by two or more annotators for each document.</li> </ul> <p><strong>Corpus format</strong></p> <p>Each dataset is a JSON object with one single key named "articles", which contains a list of documents. So, the raw format of the file is one line per document plus two additional lines (the first and the last) to enclose that list of documents and the expected type of data is as follows:</p> <pre><code class="language-json">{"articles":[ {"abstractText":str,"db":str,"decsCodes":list,"id":str,"journal":str,"title":str,"year":int}, ... ]}</code></pre> <p>To clarify, the order of appearance of the fields in each document is as follows (note that this example it is pretty printed for readability purposes):</p> <pre><code class="language-json">{ "articles": [ { "abstractText": "Content of the abstract", "db": "Name of the source database", "decsCodes": [ "code1", "code2", "code3" ], "id": "Id of the document", "journal": "Name of the journal", "title": "Title of the document", "year": 2019 } ] }</code></pre> <p>Note: The fields "db", "journal" and "year" might be null.</p> <p>Copyright (c) 2020 Secretaría de Estado de Digitalización e Inteligencia Artificial</p>
A 30m Topographic Wetness Index Dataset for the Continental United States
<p>The topographic wetness index was computed at a 30m resolution for the continental United States. This dataset was computed using the triangular multiple flow direction algorithm for computing upslope areas (see https://doi.org/10.1029/2006WR005128). We used the Shuttle Radar Topography Mission (SRTM; https://doi.org/10.1029/2005RG000183) digital elevation model (DEM) for all computations. This analysis was conducted in the R programming language using the System for Automated Geoscientific Analyses (SAGA; https://doi.org/10.5194/gmd-8-1991-2015) for back end computations. This dataset is in the NAD83(NSRS2007) / Conus Albers projection system, EPSG 5072.</p>
Improvement of editorial quality of journals indexed in DOAJ
<p>In 2013, Directory of Open Access Journals (DOAJ) completely changed the inclusion criteria and journal evaluation process, starting to remove journals that not comply with these criteria. The present dataset contains 12.577 journals included in DOAJ since the launch of the Directory in 2002 until May 15th, 2016 that was examined and enriched with other data, in order to examinatethe results of the new process and its capability to improve the quality of the directory and the reliability of the contained information.</p>
Indexed scientific articles for the seven journals listed on the Design Society website and all papers indexed for the DESIGN and ICED conferences
<p>Includes all articles indexed by Scopus® for the seven journals listed on the Design Society website and all papers indexed for DESIGN and ICED (accessed on 05/Nov/2016).</p> <p>The full search query used to extract the articles is:</p> <p>"( SRCTITLE ( "Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM" OR "Journal of Engineering Design" OR "Design Studies" OR "Research in Engineering Design" OR "CoDesign" OR "Journal of Design Research" OR "Design Science Journal" OR "The International Journal of Design Creativity and Innovation" OR "International Conference on Engineering Design" OR "INTERNATIONAL DESIGN CONFERENCE" ) ) AND ( "data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*" ) AND ( EXCLUDE ( EXACTSRCTITLE , "Hardware Software Codesign Proceedings Of The International Workshop" ) OR EXCLUDE ( EXACTSRCTITLE , "Journal Of Engineering Design And Technology" ) OR EXCLUDE ( EXACTSRCTITLE , "Research In Engineering Design Theory Applications And Concurrent Engineering" ) OR EXCLUDE ( EXACTSRCTITLE , "Chinese Journal Of Engineering Design" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2005 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 12 Proceedings Of The 10th ACM International Conference On Hardware Software Codesign And System Synthesis Co Located With Esweek" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2006 Proceedings Of The 4th International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Codes Isss 2007 International Conference On Hardware Software Codesign And System Synthesis" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2008 Proceedings Of The 6th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2008" ) OR EXCLUDE ( EXACTSRCTITLE , "Second IEEE ACM IFIP International Conference On Hardware Software Codesign And Systems Synthesis Codes Isss 2004" ) OR EXCLUDE ( EXACTSRCTITLE , "Embedded Systems Week 2011 Esweek 2011 Proceedings Of The 9th IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 11" ) OR EXCLUDE ( EXACTSRCTITLE , "2010 IEEE ACM IFIP International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2010" ) OR EXCLUDE ( EXACTSRCTITLE , "2013 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "2014 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2014" ) OR EXCLUDE ( EXACTSRCTITLE , "2015 ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "8th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2010" ) ) AND ( EXCLUDE ( EXACTSRCTITLE , "2015 International Conference On Hardware Software Codesign And System Synthesis Codes Isss 2015" ) OR EXCLUDE ( EXACTSRCTITLE , "9th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2011" ) OR EXCLUDE ( EXACTSRCTITLE , "11th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2013" ) OR EXCLUDE ( EXACTSRCTITLE , "10th ACM IEEE International Conference On Formal Methods And Models For Codesign Memocode 2012" ) OR EXCLUDE ( EXACTSRCTITLE , "A Practical Introduction To Hardware Software Codesign" ) )"</p> <p>The keywords used in the article "DATA-DRIVEN ENGINEERING DESIGN RESEARCH: OPPORTUNITIES USING OPEN DATA" are:</p> <p>"data collection" OR "data acquisition" OR "data source" OR database OR "empirical data" OR "empirical grounding" OR interview OR documents OR "data logs" OR "case study" OR observation OR experiment* OR "empirical finding*" OR "empirical result*"</p>
Lightning Potential Index Using ICON Simulation at the km-scale over the Third Pole Region: ISS-LIS events and ICON-CLM simulated LPI
<p>This dataset contains records of lightning events recorded by the International Space Station (ISS) Lightning Imaging Sensor (LIS) from October 2019 to September 2022 in the Third Pole region. Furthermore, the Icosahedral Nonhydrostatic Weather and Climate Model in Climate Limited-Area Mode (ICON-CLM) was utilized to simulate the hourly Lightning Potential Index (LPI) over the Third Pole region for the same duration. The aforementioned dataset was utilized in the creation of the research article titled "Modeling Lightning Activity in the Third Pole Region: Performance of a km-scale ICON-CLM Simulation" authored by Prashant Singh and Bodo Ahrens. The paper has been submitted to the journal Atmosphere. In CORDEX-FPS-CPTP contribution no. 17 (GUF), you can find more data from ICON-CLM, such as precipitation, CAPE, wind vectors, and more.</p>
BST/NOAA PSL Level 2 UAS Soil Moisture, Digital Elevation, Normalized Difference Vegetative Index, and Surface Temperature for SPLASH
<p>This dataset contains uncrewed aircraft systems (UAS) high-resolution data of soil moisture at the 0-5 cm soil depth, normalized difference vegetation index (NDVI), surface temperature, and digital elevation for the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from a series of flights starting on June 1st, 2022 and ending October 18th, 2023. Soil moisture measurements were retrieved using the Lobe Differencing Correlation Radiometer (LDCR) which is a L-Band (1-2 GHz) microwave radiometer and was flown on the E2 and S2 aerial platforms operated by Black Swift Technologies LLC. </p> <p> </p> <p>Each zip file contains a set of four Level 2 NetCDF files which provides the highest spatial resolution available for each of four products for a given flight location. With the Level 2 data, each flight location and variable can have different spatial resolutions depending on the sensor type, retrieval algorithm, and flight altitude. The file name convention for the zip files is as follows.</p> <p> </p> <p>uas_L2_yyyymmdd_hhmmss_vX.X.zip </p> <p>where</p> <p>L2 = Level 2 data </p> <p>yyyymmdd = year,month,day</p> <p>hhmmss = hour,minute,second</p> <p>vX.X = version number</p> <p>Time is the flight start time in UTC.</p> <p> </p> <p>The NetCDF file format contained in the zip files has a similar format to the zip files with convention</p> <p> </p> <p>uas_<var>_L2_yyyymmdd_hhmmss.nc </p> <p>where</p> <p><var> = vsm, dem, ndvi, or stmp</p> <p>vsm = volumetric soil moisture</p> <p>dem = digital elevation</p> <p>ndvi = normalized difference vegetation index</p> <p>stmp = surface temperature</p> <p> </p> <p>Note that each flight location using the E2 aerial platform required two flights so starting flight times for the soil moisture NetCDF files are different from the other three products.</p> <p><strong>November 2023 update</strong>: Version 2.0 added flight data from 2023. Version 2.0 includes an updated calibration of the soil moisture retrieval that has been applied to 2023 data, and a mask was applied to the soil moisture retrieval over water surfaces for both 2022 and 2023 data.</p> <p><strong>December 2023 update</strong>: Version 2.1 updated soil moisture data with a wet bias in v2.0 for flights #2 (17:40:35 UTC) and #3 (19:24:45 UTC) on July 27, 2022.</p>
IL24 index
<p>The IL24 index is computed by using 24 magnetometer station <span>from Sodankylä Geophysical Observatory in Finland (SOD), Tromso Geophysical Observatory in Norway (TRO), Abisco in Sweden, and magnetic observations collected by INTERMAGNET and IMAGE. The stations from north to south are the following: NOR, SOR, KEV, TRO, MAS, AND, KIL, IVA, MUO, KIR, SOD, PEL, RAN, JCK, RST, DON, RVK, OUJ, MEK, HAN, DOB, SOL, NUR and KAR. </span></p>
Law Indexes: New York (Local Laws)
<p>Under <a href="https://www.nysenate.gov/legislation/laws/CNS/A9">Article IX (Local Governments) of the Constitution of the State of New York</a>, adopted in 1963, local governments in the State of New York have the power to adopt local laws. To effectuate the article, the Legislature enacted the <a href="https://www.nysenate.gov/legislation/laws/MHR/">Municipal Home Rule Law</a> (<a href="https://hdl.handle.net/2027/uc1.a0001834712?urlappend=%3Bseq=893%3Bownerid=113368669-899">1963 N.Y. Laws 2697, Ch. 843</a>), which outlines the process that local governments must follow to adopt local laws. <a href="https://www.nysenate.gov/legislation/laws/MHR/27">Section 27 of the Law</a> requires local governments to file local laws with the Secretary of State before they can become effective.</p> <p>This data set contains index records for over 130,000 local laws filed with the Secretary of State, mostly between 1969 and 2003. Pursuant to Freedom of Information Law (FOIL) Request No. DOS-22-02-052, a copy of the index database, created using DataPerfect, was released by the State on March 17, 2022. These records were requested in an effort to expand the geographic scope and detail of information in the Local Geohistory Project, which aims to educate users and disseminate information concerning the geographic history and structure of political subdivisions and local government.</p> <p>This data set complements local law volumes published with session laws through 1973, along with printed indexes covering the years 1974 through 1982. Original local law filings for this time period have been accessioned by the New York State Archives as part of <a href="https://iarchives.nysed.gov/xtf/view?docId=ead/findingaids/13241.xml;query=">Series Number 13241</a>.</p>
ScienceDex guides
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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.