These data provide additional demographic information about FSIS regulated establishments. Additional demographic data are also available in the FSIS Meat, Poultry, and Egg Inspection Directory (MPI). The Meat, Poultry and Egg Product Inspection Directory is a listing of establishments that produce meat, poultry, and/or egg products regulated by USDA's Food Safety and Inspection Service (FSIS).

Tagging programmes have been used to provide information on fish and fisheries to central government policy makers in New Zealand for many years. A wide variety of species have been the subject of such studies, including finfish, shellfish and rock lobsters. In New Zealand, the Ministry for Primary Industries (formerly the Ministry of Fisheries) has funded these programmes to aid with fisheries research and stock assessment. Data from these programme are held in the "tag" database, from which the data in this dataset are sourced.
Citation: Ministry for Primary Industries (2014). New Zealand research tagging database. Southwestern Pacific OBIS, National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand, 411926 records.Online: http://nzobisipt.niwa.co.nz/resource.do?r=mpi_tag Released on November 5, 2014.Bibliographic Citations: http://www.fish.govt.nz/NR/rdonlyres/E827F55F-0779-4599-8223-92538AC61725/0/research_database_tag_2011.pdf_Item Page Created: 2016-06-09 02:17 Item Page Last Modified: 2025-04-05 18:54Owner: NIWA_OpenDataMPI_tagNo data edit dates availableFields: id,type,modified,language,license,rightsHolder,accessRights,bibliographicCitation,institutionCode,collectionCode,datasetName,ownerInstitutionCode,basisOfRecord,dynamicProperties,occurrenceID,catalogNumber,occurrenceRemarks,individualCount,sex,lifeStage,occurrenceStatus,samplingProtocol,eventDate,startDayOfYear,year,month,day,fieldNumber,waterBody,country,stateProvince,county,locality,minimumDepthInMeters,maximumDepthInMeters,decimalLatitude,decimalLongitude,geodeticDatum,coordinateUncertaintyInMeters,footprintWKT,scientificNameID,scientificName,kingdom,phylum,class,order_,family,genus,subgenus,specificEpithet,infraspecificEpithet,scientificNameAuthorship,FID

The ability to communicate is one of the core aspects of human life. For this, we use not only verbal but also nonverbal signals of remarkable complexity. Among the latter, facial expressions belong to the most important information channels. Despite the large variety of facial expressions we use in daily life, research on facial expressions has so far mostly focused on the emotional aspect. Consequently, most databases of facial expressions available to the research community also include only emotional expressions, neglecting the largely unexplored aspect of conversational expressions. To fill this gap, we present the MPI facial expression database, which contains a large variety of natural emotional and conversational expressions. The database contains 55 different facial expressions performed by 19 German participants. Expressions were elicited with the help of a method-acting protocol, which guarantees both well-defined and natural facial expressions. The method-acting protocol was based on every-day scenarios, which are used to define the necessary context information for each expression. All facial expressions are available in three repetitions, in two intensities, as well as from three different camera angles. A detailed frame annotation is provided, from which a dynamic and a static version of the database have been created. In addition to describing the database in detail, we also present the results of an experiment with two conditions that serve to validate the context scenarios as well as the naturalness and recognizability of the video sequences. Our results provide clear evidence that conversational expressions can be recognized surprisingly well from visual information alone. The MPI facial expression database will enable researchers from different research fields (including the perceptual and cognitive sciences, but also affective computing, as well as computer vision) to investigate the processing of a wider range of natural facial expressions.

This dataset contains detailed Global Multidimensional Poverty Index (MPI) data for 110 countries.The Global MPI reflects the combined simultaneous disadvantages poor people experience across different areas of their lives, including education, health and living standards. If people are deprived in at least one-third of ten weighted indicators, they are identified as multi-dimensionally poor. For further information on the MPI visit: http://www.ophi.org.uk/multidimensional-poverty-index/

The dataset includes main MPI results for each country, the proportion of people who are MPI poor and experience deprivations in each indicator of poverty, the percentage contribution of deprivations to the MPI for each country, and other measures of poverty and wellbeing at the national level. It is an appendix to OPHI's Methodological Note – Winter 2014/2015 (http://www.ophi.org.uk/multidimensional-poverty-index/mpi-2014-2015/mpi-methodology/)

Please cite the data as: Alkire, S., Conconi, A., Robles, G. and Seth, S. (2015). “Multidimensional Poverty Index, Winter 2014/2015: Brief Methodological Note and Results.” OPHI Briefing 27, University of Oxford, January.

Multidimensional Poverty Index. Data come from http://hdr.undp.org/en/data .

186 Global import shipment records of Mpi Power with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The distribution of total commercial catch is estimated for all fishing events reported in statutory catch and effort returns for the period 1 October 2007 to 30 September 2019.

The location of fishing events is reported by either start (or start and end) coordinates (precise to 1 nautical mile) or by large statistical areas. The total catch of all species from each fishing event is spread uniformly over a polygon of space estimated to be occupied by that fishing. Trawl fishing polygons are derived from the length and width of the door-spread for the duration of the tow. The path of each tow is taken as a straight line between start and end coordinates where these are reported, or between start and estimated end coordinates. Where not required to report end coordinates, (as is the case for most inshore trawling) tow end points are derived using the direction of the next tow start position or the direction of the landing point for the last tow of the day.Line fishing is attributed to a circle with the center at the reported start position and a radius of the reported length of line set. Set net fishing is attributed to a circle with the center at the reported start position and radius of 2 nm in accordance with the definition of a single set netting event prescribed in reporting regulations. Jig fishing reports a single nightly position and is assumed to occur within 5 nm of that position. Hand and Pot fishing reports by statistical area, and where available, information on habitat and depth or information supplied by fishers is used to define the parts of each statistical area where each type of fishing is likely to have occurred. In the case of lobster potting and paua diving, an informal map of reef area supplied by the Department of Conservation is used to estimate where this fishing may have occurred.Catch intensity (kg/ha) is mapped to a square kilometre grid for all fishing events and summed over gear types. The data is aggregated into grid squares of between 1 and 2500 km2 as required to give 12-year annual averages of data from at least three permit holders. Catch per unit area values are classified into ten intensity classes.

MPI has high confidence in the data on catch quantities used to create this data but the spatial distributions of those catches are only approximate and should be used with caution especially at large map scales (maps of small spatial extent). Nevertheless, the aggregation of a large number of fishing events tends to provide consistent patterns that have passed scrutiny when tested with groups of fishers.Grid squares with less than 3 permit holders present have removed in order to confidentialise the data. The data has been approved for public release by the data owner, Team Manager, Fisheries Data Management as permit holders and catch values have been aggregated as part of the confidentialisation process and to align with MPI's commitment to promote open data.Please contact the data owner for any questions in relation to the release of this data (RDM@mpi.govt.nz). The data custodian for this data is the Spatial Intelligence team (Spatial.Intelligence@mpi.govt.nz). This data is also displayed on the MPI website as the commercial fishing intensity map.You can also use the tile layer in your desktop GIS, which is here

Nepal Manufacturing Production Index (MPI) data was reported at 117.758 2014-2015=100 in Jul 2018. This records an increase from the previous number of 117.442 2014-2015=100 for Apr 2018. Nepal Manufacturing Production Index (MPI) data is updated quarterly, averaging 108.158 2014-2015=100 from Oct 2015 (Median) to Jul 2018, with 12 observations. The data reached an all-time high of 117.758 2014-2015=100 in Jul 2018 and a record low of 82.027 2014-2015=100 in Oct 2015. Nepal Manufacturing Production Index (MPI) data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Nepal – Table NP.B001: Manufacturing Production Index: 2014-15=100.

Speed in MR/m and Peak memory (in GB per process) for querying database AFS31RS90 and dataset KAL_D in Big Data cluster.

The Master Patient Index (MPI) Software market is booming, reaching an estimated $2 billion in 2025 and projected for strong growth through 2033. Learn about key market drivers, trends, challenges, and leading companies shaping this crucial sector of healthcare IT. Explore regional market share and growth projections in our comprehensive analysis.

The MPI-Leipzig Mind-Brain-Body dataset contains MRI and behavioral data from 318 participants. Datasets for all participants include at least a structural quantitative T1-weighted image and a single 15-minute eyes-open resting-state fMRI session.

The participants took part in one or two extended protocols: (1) Leipzig Mind-Body-Brain Interactions (LEMON) and (2) Neuroanatomy & Connectivity Protocol (N&C). The data from LEMON protocol is included in the ‘ses-01’ subfolder; the data from N&C protocol in ‘ses-02’ subfolder.

LEMON focuses on structural imaging. 228 participants were scanned. In addition to the quantitative T1-weighted image, the participants also have a structural T2-weighted image (226 participants), a diffusion-weighted image with 64 directions (228) and a 15-minute eyes-open resting-state session (228). New imaging sequences were introduced into the LEMON protocol after data acquisition for approximately 110 participants. Before the change, a low-resolution 2D FLAIR images were acquired for clinical purposes (110). After the change, 2D FLAIR was replaced with high-resolution 3D FLAIR (117). The second addition was the acquisition of gradient-echo images (112) that can be used for Susceptibility-Weighted Imaging (SWI) and Quantitative Susceptibility Mapping (QSM).

The N&C protocol focuses on resting-state fMRI data. 199 participants were scanned with this protocol; 109 participants also took part in the LEMON protocol. Structural data was not acquired for the overlapping LEMON participants. For the unique N&C participants, only a T1-weighted and a low-resolution FLAIR image were acquired. Four 15-minute runs of eyes-open resting-state are the main component of N&C; they are complete for 194 participants, three participants have 3 runs, one participant has 2 runs and one participant has a single run. Due to a bug in multiband sequence used in this protocol, the echo time for N&C resting-state is longer than in LEMON — 39.4 ms vs 30 ms.

Forty-five participants have complete imaging data: quantitative T1-weighted, T2-weighted, high-resolution 3D FLAIR, DWI, GRE and 75 minutes of resting-state. Both gradient-echo and spin-echo field maps are available in both datasets for all EPI-based sequences (rsfMRI and DWI).

Extensive behavioral data was acquired in both protocols. They include trait and state questionnaires, as well as behavioral tasks.

194 Global export shipment records of Mpi testing with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Master Patient Index Solutions Market Outlook

According to our latest research, the global Master Patient Index (MPI) Solutions market size reached USD 1.7 billion in 2024, reflecting the sector’s robust expansion and growing adoption across healthcare institutions worldwide. With a strong compound annual growth rate (CAGR) of 7.1% projected from 2025 to 2033, the market is expected to surge to USD 3.15 billion by 2033. This growth is primarily driven by the increasing need for accurate patient data management, interoperability, and the digital transformation of healthcare systems.

The primary growth driver for the Master Patient Index Solutions market is the global push towards digital health transformation and the increasing adoption of electronic health records (EHRs). Healthcare organizations are under mounting pressure to streamline patient data, ensuring that each patient is uniquely and correctly identified across disparate systems. This need is especially pronounced in large hospital networks and integrated delivery networks where patient data is often fragmented across multiple platforms. The rise in medical errors due to patient misidentification has underscored the necessity for robust MPI solutions, prompting healthcare providers to invest in advanced technologies that enhance data accuracy, patient safety, and operational efficiency. Additionally, regulatory mandates and government initiatives aimed at improving healthcare interoperability and patient outcomes are accelerating the adoption of MPI solutions globally.

Another significant growth factor is the evolution of healthcare reimbursement models and the growing emphasis on value-based care. As payers and providers shift from volume-based to value-based models, the accurate aggregation and analysis of patient data become critical to demonstrating care quality and maximizing reimbursements. Master Patient Index Solutions play a pivotal role in supporting this shift by ensuring that patient records are accurately matched and consolidated, enabling comprehensive patient views and facilitating population health management. The integration of artificial intelligence and machine learning into MPI platforms has further enhanced their ability to detect duplicate records, manage complex data sets, and automate identity resolution, making them indispensable tools in the modern healthcare landscape.

The proliferation of mergers, acquisitions, and partnerships within the healthcare sector is also fueling demand for advanced MPI solutions. As healthcare organizations grow and consolidate, the integration of disparate patient databases becomes increasingly complex, often resulting in duplicate or incomplete records. MPI solutions address this challenge by providing a centralized, unified view of patient information across the enterprise, enabling seamless data exchange and supporting clinical, administrative, and financial operations. The growing trend of telemedicine and remote care, accelerated by the COVID-19 pandemic, has further amplified the need for reliable patient identification and data management solutions, as patients interact with healthcare systems through multiple digital touchpoints.

From a regional perspective, North America remains the dominant market for Master Patient Index Solutions, driven by the region’s advanced healthcare IT infrastructure, stringent regulatory requirements, and high adoption rates of EHRs. Europe is also witnessing significant growth, supported by government initiatives to improve healthcare interoperability and patient safety. The Asia Pacific region is emerging as a lucrative market, fueled by rapid healthcare digitization, expanding healthcare infrastructure, and increasing investments in health IT. Latin America and the Middle East & Africa are gradually adopting MPI solutions, with growth primarily concentrated in urban centers and large healthcare networks. Overall, the global outlook for the MPI Solutions market is highly positive, with sustained investment and innovation expected to drive continued expansion over the forecast period.

Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. These data include all datasets published for 'CMIP6.CMIP.MPI-M.MPI-ESM1-2-LR.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.

The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

Mpi Fisheries Inc Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. These data include all datasets published for 'CMIP6.CMIP.MPI-M.MPI-ESM1-2-HR.amip' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'.

The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions and serves as resource for authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6).

CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated on a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by the German Climate Computing Center (DKRZ).

The project includes simulations from about 120 global climate models and around 45 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

Mexico Mining Production Index (MPI) data was reported at 96.602 2013=100 in Mar 2019. This records an increase from the previous number of 82.133 2013=100 for Feb 2019. Mexico Mining Production Index (MPI) data is updated monthly, averaging 66.994 2013=100 from Jan 2000 (Median) to Mar 2019, with 231 observations. The data reached an all-time high of 114.637 2013=100 in Sep 2015 and a record low of 41.351 2013=100 in Feb 2009. Mexico Mining Production Index (MPI) data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.B023: Mining Production Index: 2013= 100.

Mexico MPI: Metallic Mineral: Silver data was reported at 206.876 1993=100 in Mar 2015. This records a decrease from the previous number of 213.105 1993=100 for Feb 2015. Mexico MPI: Metallic Mineral: Silver data is updated monthly, averaging 102.000 1993=100 from Jan 1980 (Median) to Mar 2015, with 423 observations. The data reached an all-time high of 216.656 1993=100 in May 2012 and a record low of 38.190 1993=100 in Feb 2009. Mexico MPI: Metallic Mineral: Silver data remains active status in CEIC and is reported by National Institute of Statistics and Geography. The data is categorized under Global Database’s Mexico – Table MX.B022: Mining Production Index: 1993= 100.

Linguistic and biblical resource data for Mpi

The archive contains over 1440 motion capture files in the .bvh format. The full interactive database with the same motion capture files in the .mvnx format together with the descriptibe metadata and the usage instructions can be found here: http://ebmdb.tuebingen.mpg.de