Comprehensive dataset of 54 Database management companies in Michigan, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

.EMMET.MI Whois Database, discover comprehensive ownership details, registration dates, and more for .EMMET.MI TLD with Whois Data Center.

This is the back-end data file for the Michigan Islands Wilderness Character Monitoring Application. User interface and lookup databases are required for use (see reference code 5725). The Wilderness Act of 1964 mandated the preservation of wilderness character. The NWRS has 18% of designated wilderness, comprising 21 million acres. After over 40 years there is still a lack of consistent inventory and monitoring of wilderness as well as the ability to measure how it is affected by stewardship. To ensure the last remaining wilderness is not lost, monitoring can maintain the preservation and true wild nature of these areas for present and future generations. Monitoring provides an assessment of the trends of wilderness character to develop refuge wilderness stewardship. This also provides the ability to evaluate impacts of proposed actions on wilderness character and allows defensible stewardship decisions. By preserving wilderness character, the USFWS demonstrates leadership in wilderness stewardship across the National Wilderness Preservation System.

This digital data release presents contour data from multiple subsurface geologic horizons as presented in previously published summaries of the regional subsurface configuration of the Michigan and Illinois Basins. The original maps that served as the source of the digital data within this geodatabase are from the Geological Society of America’s Decade of North American Geology project series, “The Geology of North America” volume D-2, chapter 13 “The Michigan Basin” and chapter 14 “Illinois Basin Region”. Contour maps in the original published chapters were generated from geophysical well logs (generally gamma-ray) and adapted from previously published contour maps. The published contour maps illustrated the distribution sedimentary strata within the Illinois and Michigan Basin in the context of the broad 1st order supercycles of L.L. Sloss including the Sauk, Tippecanoe, Kaskaskia, Absaroka, Zuni, and Tejas supersequences. Because these maps represent time-transgressive surfaces, contours frequently delineate the composite of multiple named sedimentary formations at once. Structure contour maps on the top of the Precambrian basement surface in both the Michigan and Illinois basins illustrate the general structural geometry which undergirds the sedimentary cover. Isopach maps of the Sauk 2 and 3, Tippecanoe 1 and 2, Kaskaskia 1 and 2, Absaroka, and Zuni sequences illustrate the broad distribution of sedimentary units in the Michigan Basin, as do isopach maps of the Sauk, Upper Sauk, Tippecanoe 1 and 2, Lower Kaskaskia 1, Upper Kaskaskia 1-Lower Kaskaskia 2, Kaskaskia 2, and Absaroka supersequences in the Illinois Basins. Isopach contours and structure contours were formatted and attributed as GIS data sets for use in digital form as part of U.S. Geological Survey’s ongoing effort to inventory, catalog, and release subsurface geologic data in geospatial form. This effort is part of a broad directive to develop 2D and 3D geologic information at detailed, national, and continental scales. This data approximates, but does not strictly follow the USGS National Cooperative Geologic Mapping Program's GeMS data structure schema for geologic maps. Structure contour lines and isopach contours for each supersequence are stored within separate “IsoValueLine” feature classes. These are distributed within a geographic information system geodatabase and are also saved as shapefiles. Contour data is provided in both feet and meters to maintain consistency with the original publication and for ease of use. Nonspatial tables define the data sources used, define terms used in the dataset, and describe the geologic units referenced herein. A tabular data dictionary describes the entity and attribute information for all attributes of the geospatial data and accompanying nonspatial tables.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). In addition to the preceding, required text, the Abstract should also describe the projection and coordinate system as well as a general statement about horizontal accuracy.

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA).

United States Inventory: Multi-Family: Muskegon, MI data was reported at 10.000 Unit th in Jul 2020. This records an increase from the previous number of 7.000 Unit th for Jun 2020. United States Inventory: Multi-Family: Muskegon, MI data is updated monthly, averaging 16.000 Unit th from Feb 2012 (Median) to Jul 2020, with 95 observations. The data reached an all-time high of 35.000 Unit th in Mar 2015 and a record low of 7.000 Unit th in Jun 2020. United States Inventory: Multi-Family: Muskegon, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB025: Inventory of Home for Sale: by Metropolitan Areas.

The Great Lakes Environmental Database (GLENDA) houses environmental data collected by EPA Great Lakes National Program Office (GLNPO) programs that sample water, aquatic life, sediments, and air to assess the health of the Great Lakes ecosystem. GLENDA is available to the public on the EPA Central Data Exchange (CDX). A CDX account is required, which anyone may create. GLENDA offers “Ready to Download Data Files” prepared by GLNPO or a “Query Data” interface that allows users to select from predefined parameters to create a customized query. Query results can be downloaded in .csv format. GLNPO programs providing data in GLENDA include the Great Lakes Water Quality Survey and Great Lakes Biology Monitoring Program (1983-present, biannual monitoring throughout the Great Lakes to assess water quality, chemical, nutrient, and physical parameters, and biota such as plankton and benthic invertebrates), the Great Lakes Fish Monitoring and Surveillance Program (1977-present, annual analysis of top predator fish composites to assess historic and emerging persistent, bioaccumulative, or toxic chemical contaminants), the Cooperative Science and Monitoring Initiative (2002-present, intensive water quality and biology sampling of one lake per year focusing on key challenges and data gaps), the Great Lakes Integrated Atmospheric Deposition Network (1990-present, monitoring Great Lakes air and precipitation for persistent toxic chemicals), the Lake Michigan Mass Balance Study (1993-1996, analyzed the atmosphere, tributaries, sediments, water column, and biota of Lake Michigan for nutrients, atrazine, PCBs, trans-nonachlor, and mercury modelling), and the Great Lakes Legacy Act (1996-present, evaluations of sediment contamination in Areas of Concern). GLENDA is updated frequently with new data.

.MI.TH Whois Database, discover comprehensive ownership details, registration dates, and more for .MI.TH TLD with Whois Data Center.

[Database for mi-faser]

mi-faser: microbiome - functional annotation of sequencing reads

A super-fast ( < 20min/10GB of reads ) and accurate ( > 90% precision ) method for annotation of molecular functionality encoded in sequencing read data without the need for assembly or gene finding.

Web Service: http://services.bromberglab.org/mifaser/|
Repository: https://bitbucket.org/bromberglab/mifaser_base/

Context : We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session. It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE). Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.

Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.

Duration : Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.

Documents Instructions: checklist read by experimenters during the experiments. Questionnaires: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version Performance: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires Scenarios/scripts : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI

Database : raw signals Dataset A : N=60 participants Dataset B : N=21 participants Dataset C : N=6 participants

• Entries for English-Spanish: Scientific research & mathematical sciences (906 entries), Geosciences (10,215), Computer science, electronics & telecommunications (70,580), Industry (47,578), Transport & Maintenance (12,291), Economy (145,572), Biological sciences (38,989), Communication & media (8,143), Chemical & physical sciences (27,467). * Entries for English-French-German-Spanish: Environment (36,658), Health (66,727), Agriculture & food (25,975), Construction & public works (8,429), Law & policy (56,578), Sports & Leisure (17,312) * Two specialized lexicons: Spanish-English and English-French-German without domain codes: electronics, telematics, law, taxes, customs, etc. (550,000 entries). * Two general lexicons: Spanish-English-French-German and Spanish-English-French-German-Portuguese-Italian (83,000 entries).This terminological database contains, for each domain, a sub-domain indication is given (from 2 sub-domains for Scientific research to 39 for Sports & leisure). Each entry consists of a definition, phraseological unit, abbreviation, usage information, grammatical labels. Format: ASCII

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The All Roads Shapefile includes all features within the MTDB Super Class "Road/Path Features" distinguished where the MAF/TIGER Feature Classification Code (MTFCC) for the feature in MTDB that begins with "S". This includes all primary, secondary, local neighborhood, and rural roads, city streets, vehicular trails (4wd), ramps, service drives, alleys, parking lot roads, private roads for service vehicles (logging, oil fields, ranches, etc.), bike paths or trails, bridle/horse paths, walkways/pedestrian trails, and stairways.

The Southeast Michigan Operational Data Environment (SEMI-ODE) is a real-time data acquisition and distribution software system that processes vehicle and infrastructure data collected from sources such as the Southeast Michigan testbed Situational Data Clearinghouse (SDC) and the Situational Data Warehouse (SDW), along with other non-connected vehicle sources of data. The ODE offers four core functions to supply tailored and custom-requested data from the SEMI Testbed to subscribing client software applications. The core functions are: 1) Valuation (V), 2) Integration (I), 3) Sanitization (S) (also called de-identification), and 4) Aggregation (A). These four VISA functions are critical to the field test as they enable the subscribing emulated applications to receive data tailored to support their operation. These functions also serve to increase the general usability of the data being generated in the SEMI Test Bed. This legacy dataset was created before data.transportation.gov and is only currently available via the attached file(s). Please contact the dataset owner if there is a need for users to work with this data using the data.transportation.gov analysis features (online viewing, API, graphing, etc.) and the USDOT will consider modifying the dataset to fully integrate in data.transportation.gov.

United States Inventory: Single Family: Iron Mountain, MI data was reported at 12.000 Unit th in Jul 2020. This records an increase from the previous number of 8.000 Unit th for May 2020. United States Inventory: Single Family: Iron Mountain, MI data is updated monthly, averaging 12.000 Unit th from Aug 2014 (Median) to Jul 2020, with 36 observations. The data reached an all-time high of 18.000 Unit th in Aug 2018 and a record low of 1.000 Unit th in Aug 2014. United States Inventory: Single Family: Iron Mountain, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB025: Inventory of Home for Sale: by Metropolitan Areas.

United States Avg Days on Market: Multi-Family: Jackson, MI data was reported at 259.000 Day in Jul 2020. This records an increase from the previous number of 138.000 Day for Jun 2020. United States Avg Days on Market: Multi-Family: Jackson, MI data is updated monthly, averaging 107.000 Day from Feb 2012 (Median) to Jul 2020, with 95 observations. The data reached an all-time high of 1,968.000 Day in Jul 2016 and a record low of 15.000 Day in May 2013. United States Avg Days on Market: Multi-Family: Jackson, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB006: Average Days on Market: by Metropolitan Areas.

United States Inventory: All Residential: Jackson, MI data was reported at 543.000 Unit th in Jul 2020. This records a decrease from the previous number of 578.000 Unit th for Jun 2020. United States Inventory: All Residential: Jackson, MI data is updated monthly, averaging 812.500 Unit th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 1,164.000 Unit th in Jul 2015 and a record low of 455.000 Unit th in Apr 2020. United States Inventory: All Residential: Jackson, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB025: Inventory of Home for Sale: by Metropolitan Areas.

United States Avg Days on Market: Multi-Family: Adrian, MI data was reported at 119.000 Day in Jul 2020. This records an increase from the previous number of 11.000 Day for May 2020. United States Avg Days on Market: Multi-Family: Adrian, MI data is updated monthly, averaging 80.000 Day from Feb 2012 (Median) to Jul 2020, with 84 observations. The data reached an all-time high of 2,367.000 Day in Jan 2017 and a record low of 2.000 Day in Jan 2020. United States Avg Days on Market: Multi-Family: Adrian, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB006: Average Days on Market: by Metropolitan Areas.

United States Price per Square: Single-Family: Muskegon, MI data was reported at 99.608 USD th in Jul 2020. This records a decrease from the previous number of 100.106 USD th for Jun 2020. United States Price per Square: Single-Family: Muskegon, MI data is updated monthly, averaging 68.340 USD th from Feb 2012 (Median) to Jul 2020, with 102 observations. The data reached an all-time high of 100.106 USD th in Jun 2020 and a record low of 34.144 USD th in Jan 2013. United States Price per Square: Single-Family: Muskegon, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB060: Price per Square: by Metropolitan Areas.

United States Months of Supply: Multi-Family: Muskegon, MI data was reported at 1.300 Month in Jul 2020. This records a decrease from the previous number of 1.800 Month for Jun 2020. United States Months of Supply: Multi-Family: Muskegon, MI data is updated monthly, averaging 5.300 Month from Feb 2012 (Median) to Jul 2020, with 95 observations. The data reached an all-time high of 35.000 Month in Mar 2015 and a record low of 1.200 Month in May 2020. United States Months of Supply: Multi-Family: Muskegon, MI data remains active status in CEIC and is reported by Redfin. The data is categorized under Global Database’s United States – Table US.EB029: Months of Supply: by Metropolitan Areas.