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).

A dataset for magnetic particle imaging based on the MNIST dataset.

This dataset contains simulated MPI measurements along with ground truth phantoms selected from the https://yann.lecun.com/exdb/mnist/" target="_blank" rel="noopener">MNIST database of handwritten digits. A state-of-the-art model-based system matrix is used to simulate the MPI measurements of the MNIST phantoms. These measurements are equipped with noise perturbations captured by the preclinical MPI system (Bruker, Ettlingen, Germany). The dataset can be utilized in its provided form, while additional data is included to offer flexibility for creating customized versions.

MPI-MNIST features four different system matrices, each available in three spatial resolutions. The provided data is generated using a specified system matrix at highest spatial resolution. Reconstruction operations can be performed by using any of the provided system matrices at a lower resolution. This setup allows for simulating reconstructions from either an exact or an inexact forward operator. To cover further operator deviation setups, we provide additional noise data for the application of pixelwise noise to the reconstruction system matrix.

For supporting the development of learning-based methods, a large amount of further noise samples, captured by the Bruker scanner, is provided.

For a detailed description of the dataset, see arxiv.org/abs/2501.05583.

The Python-based GitHub repository available at https://github.com/meiraiske/MPI-MNIST" href="https://github.com/meiraiske/MPI-MNIST" target="_blank" rel="noopener">https://github.com/meiraiske/MPI-MNIST can be used for downloading the data from this website and preparing it for project use which includes an integration to PyTorch or PyTorch Lightning modules.

File Structure

All data, except for the phantoms, is provided in the MDF file format. This format is specifically tailored to store MPI data and contains metadata corresponding to the experimental setup. The ground truth phantoms are provided as HDF5 files since they do not require any metadata.

• SM: Contains twelve system matrices named SM_{physical model}_{resolution}.mdf. It covers four physical models given in three resolutions ('coarse', 'int' and 'fine'). The highest resolution ('fine') is used for data generation.
• large_noise: Contains large_NoiseMeas.mdf with 390060 noise measurements. Each noise measurement has been averaged over ten empty scanner measurements. This can be used e.g. for learning-based methods.

For dataset in ['train', 'test']:

• {dataset}_noise: Contains four noise matrices, where each noise measurement has been averaged over ten empty scanner measurements:
  1. NoiseMeas_phantom_{dataset}.mdf : Additive measurement noise for simulated measurements.
  2. NoiseMeas_phantom_bg_{dataset}.mdf : Unused noise reserved for background correction of 1.
  3. NoiseMeas_SM_{dataset}.mdf : System Matrix noise, that can be applied to each pixel of the reconstruction system matrix.
  4. NoiseMeas_SM_bg_{dataset}.mdf : Unused noise reserved for background correction of 3.
• {dataset}_gt: Contains {dataset}_gt.hdf5 with flattened and preprocessed ground truth MNIST phantoms given in coarse resolution (15x17=255 pixels) with pixel values in [0, 10].
• {dataset}_obs: Contains {dataset}_obs.mdf with noise free simulated measurements (observations) of {dataset}_gt.hdf5 using the system matrix stored in SM_fluid_opt_fine.mdf.
• {dataset}_obsnoisy: Contains {dataset}_obsnoisy.mdf with noise contained simulated measurements, resulting from {dataset}_obs.mdf and {dataset}_phantom_noise.mdf.

In line with MNIST, each MDF/HDF5 file in {dataset}_gt, {dataset}_obs, {dataset}_obsnoisy for dataset in ['train', 'test'] contains 60000 samples for 'train' and 10000 samples for 'test'. The data can be manually reproduced in the intermediate resolution (45x51=2295 pixels) from the files in this dataset using the system matrices in intermediate ('int') resolution for reconstruction and upsampling the ground truth phantoms by 3 pixels per dimension. This case is also implemented in the Github repository .

The PDF file MPI-MNIST_Metadata.pdf contains a list of meta information for each of the MDF files of this dataset.

Multidimensional Poverty Index (MPI): countries where the MPI is below 0.6. Pixels with a value lower than the specified threshold (0.6) were given a value of 1 (YES response)

The 2020 Global MPI data and publication "Charting pathways out of multidimensional poverty: Achieving the SDGs" released on 16 July 2020 by the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford and the Human Development Report Office of the United Nations Development Programme (UNDP). The global MPI measures the complexities of poor people’s lives, individually and collectively, each year. This report focuses on how multidimensional poverty has declined. It provides a comprehensive picture of global trends in multidimensional poverty, covering 5 billion people. It probes patterns between and within countries and by indicator, showcasing different ways of making progress. Together with data on the $1.90 a day poverty rate, the trends monitor global poverty in different forms.

Data revision: 2020-07-16

Contact points:

Contact: Admir Jahic UNDP

Metadata contact: OCB Environment FAO-UN

Resource constraints:

license

Online resources:

Global Multidimensional Poverty Index

Charting pathways out of multidimensional poverty: Achieving the SDGs

A document on how to access Fisheries New Zealand’s Commercial Fisheries Management Areas spatial data. This document provide a step-by-step guide to aid you with downloading the updated datasets listed above. You will be able to download the datasets in a variety of format.This document can be found by MPI staff here.

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

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.

This package contains data sets and scripts (in an Org-mode file) related to our submission to IPDPS 2017, under the title "Using Simulation to Evaluate and Tune the Performance of Dynamic Load Balancing of an Over-decomposed Geophysics Application".

The following contents are included:

IPDPS2017.org : Org mode (Emacs) file containing the shell (Bash) and R scripts used to:

run the load balancing simulation;

process the traces of both real executions (Tau traces) and simulation (Pajé traces);

generate the graphics.

lb_traces/: this directory contains the raw traces from real executions and SMPI emulations of the Ondes3D application.

processed_data/: this directory contains the results of the processing of the traces in the form of CSV format data files which are be used to generate the graphics.

img/: this directory contains the generate graphics, in PNG format.

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

1032 Global exporters importers export import shipment records of Mpi testing with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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: Leipzig Mind-Body-Brain Interactions (LEMON) and 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. Here we only list the tasks; more extenstive descriptions are available in the manuscripts.

LEMON QUESTIONNAIRES/TASKS [not yet released]

California Verbal Learning Test (CVLT) Testbatterie zur Aufmerksamkeitsprüfung (TAP Alertness, Incompatibility, Working Memory) Trail Marking Test (TMT) Wortschatztest (WST) Leistungsprüfungssystem 2 (LPS-2) Regensburger Wortflüssigkeitstest (RWT)

NEO Five-Factor Inventory (NEO-FFI) Impulsive Behavior Scale (UPPS) Behavioral Inhibition and Approach System (BISBAS) Cognitive Emotion Regulation Questionnaire (CERQ) Measure of Affective Style (MARS) Fragebogen zur Sozialen Unterstützung (F-SozU K) The Multidimensional Scale of Perceived Social Support (MSPSS) Coping Orientations to Problems Experienced (COPE) Life Orientation Test-Revised (LOT-R) Perceived Stress Questionnaire (PSQ) the Trier Inventory of Chronic Stress (TICS) The three-factor eating questionnaire (TFEQ) Yale Food Addiction Scale (YFAS) The Trait Emotional Intelligence Questionnaire (TEIQue-SF) Trait Scale of the State-Trait Anxiety Inventory (STAI) State-Trait Anger expression Inventory (STAXI) Toronto-Alexithymia Scale (TAS) Multidimensional Mood Questionnaire (MDMQ) New York Cognition Questionnaire (NYC-Q)

N&C QUESTIONNAIRES

Adult Self Report (ASR) Goldsmiths Musical Sophistication Index (Gold-MSI) Internet Addiction Test (IAT) Involuntary Musical Imagery Scale (IMIS) Multi-Gender Identity Questionnaire (MGIQ) Brief Self-Control Scale (SCS) Short Dark Triad (SD3) Social Desirability Scale-17 (SDS) Self-Esteem Scale (SE) Tuckman Procrastination Scale (TPS) Varieties of Inner Speech (VISQ) UPPS-P Impulsive Behavior Scale (UPPS-P) Attention Control Scale (ACS) Beck's Depression Inventory-II (BDI) Boredom Proneness Scale (BP) Esworth Sleepiness Scale (ESS) Hospital Anxiety and Depression Scale (HADS) Multimedia Multitasking Index (MMI) Mobile Phone Usage (MPU) Personality Style and Disorder Inventory (PSSI) Spontaneous and Deliberate Mind-Wandering (S-D-MW) Short New York Cognition Scale (Short-NYC-Q) New York Cognition Scale (NYC-Q) Abbreviated Math Anxiety Scale (AMAS) Behavioral Inhibition and Approach System (BIS/BAS) NEO Personality Inventory Revised (NEO-PI-R) Body Consciousness Questionnaire (BCQ) Creative achievement questionnaire (CAQ) Five facets of mindfulness (FFMQ) Metacognition (MCQ-30)

N&C TASKS

Conjunctive continuous performance task (CCPT) Emotional task switching (ETS) Adaptive visual and auditory oddball target detection task (Oddball) Alternative uses task (AUT) Remote associates test (RAT) Synesthesia color picker test (SYN) Test of creative imagery abilities (TCIA)

Comments added by Openfmri Curators

===========================================

General Comments

Defacing

Pydeface was used on all anatomical images to ensure deindentification of subjects. The code can be found at https://github.com/poldracklab/pydeface

Where to discuss the dataset

1) www.openfmri.org/dataset/ds000221/ See the comments section at the bottom of the dataset page. 2) www.neurostars.org Please tag any discussion topics with the tags openfmri and ds000221. 3) Send an email to submissions@openfmri.org. Please include the accession number in your email.

Known Issues

N/A

Bids-validator Output

A verbose bids-validator output is under '/derivatives/bidsvalidatorOutput_long'. Short version of BIDS output is as follows:

1: This file is not part of the BIDS specification, make sure it isn't included in the dataset by accident. Data derivatives (processed data) should be placed in /derivatives folder. (code: 1 - NOT_INCLUDED)
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-1_mp2rage.json
    Evidence: sub-010001_ses-02_inv-1_mp2rage.json
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-1_mp2rage.nii.gz
    Evidence: sub-010001_ses-02_inv-1_mp2rage.nii.gz
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-2_mp2rage.json
    Evidence: sub-010001_ses-02_inv-2_mp2rage.json
  /sub-010001/ses-02/anat/sub-010001_ses-02_inv-2_mp2rage.nii.gz
    Evidence: sub-010001_ses-02_inv-2_mp2rage.nii.gz
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-1_mp2rage.json
    Evidence: sub-010002_ses-01_inv-1_mp2rage.json
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-1_mp2rage.nii.gz
    Evidence: sub-010002_ses-01_inv-1_mp2rage.nii.gz
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-2_mp2rage.json
    Evidence: sub-010002_ses-01_inv-2_mp2rage.json
  /sub-010002/ses-01/anat/sub-010002_ses-01_inv-2_mp2rage.nii.gz
    Evidence: sub-010002_ses-01_inv-2_mp2rage.nii.gz
  /sub-010003/ses-01/anat/sub-010003_ses-01_inv-1_mp2rage.json
    Evidence: sub-010003_ses-01_inv-1_mp2rage.json
  /sub-010003/ses-01/anat/sub-010003_ses-01_inv-1_mp2rage.nii.gz
    Evidence: sub-010003_ses-01_inv-1_mp2rage.nii.gz
  ... and 1710 more files having this issue (Use --verbose to see them all).

2: Not all subjects contain the same files. Each subject should contain the same number of files with the same naming unless some files are known to be missing. (code: 38 - INCONSISTENT_SUBJECTS)
  /sub-010001/ses-01/anat/sub-010001_ses-01_T2w.json
  /sub-010001/ses-01/anat/sub-010001_ses-01_T2w.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-highres_FLAIR.json
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-highres_FLAIR.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-lowres_FLAIR.json
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-lowres_FLAIR.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-mp2rage_T1map.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-mp2rage_T1w.nii.gz
  /sub-010001/ses-01/anat/sub-010001_ses-01_acq-mp2rage_defacemask.nii.gz
  /sub-010001/ses-01/dwi/sub-010001_ses-01_dwi.bval
  ... and 8624 more files having this issue (Use --verbose to see them all).

3: Not all subjects/sessions/runs have the same scanning parameters. (code: 39 - INCONSISTENT_PARAMETERS)
  /sub-010007/ses-02/anat/sub-010007_ses-02_acq-mp2rage_T1map.nii.gz
  /sub-010007/ses-02/anat/sub-010007_ses-02_acq-mp2rage_T1w.nii.gz
  /sub-010007/ses-02/anat/sub-010007_ses-02_acq-mp2rage_defacemask.nii.gz
  /sub-010045/ses-01/dwi/sub-010045_ses-01_dwi.nii.gz
  /sub-010087/ses-02/func/sub-010087_ses-02_task-rest_acq-PA_run-01_bold.nii.gz
  /sub-010189/ses-02/anat/sub-010189_ses-02_acq-lowres_FLAIR.nii.gz
  /sub-010201/ses-02/func/sub-010201_ses-02_task-rest_acq-PA_run-02_bold.nii.gz

  Summary:           Available Tasks:    Available Modalities:
  14714 Files, 390.74GB    Rest          FLAIR
  318 - Subjects                    T1map
  2 - Sessions                     T1w
                             defacemask
                             bold
                             T2w
                             dwi
                             fieldmap
                             fieldmap

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

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.

The Master Patient Index (MPI) software market is experiencing robust growth, driven by the increasing need for accurate and unified patient data across healthcare systems. The market's expansion is fueled by several key factors: the rising adoption of electronic health records (EHRs), the increasing prevalence of chronic diseases requiring comprehensive patient data management, and the growing emphasis on interoperability and data exchange between healthcare providers. Consolidation within the healthcare industry and the demand for improved patient care further contribute to the market's upward trajectory. While precise market sizing data is unavailable, considering a global market for healthcare IT solutions with a similar trajectory, we can estimate the 2025 market size at approximately $2 billion USD, and a Compound Annual Growth Rate (CAGR) of 10% based on conservative projections for the foreseeable future. This growth will be driven primarily by the implementation of advanced features such as data cleansing, deduplication, and real-time patient identification within the MPI systems. Furthermore, increased integration with other healthcare IT systems, including EHRs and patient portals, will drive further market expansion. The competitive landscape includes both established players like McKesson, Oracle, and Epic (inferred based on market presence) and smaller, specialized vendors. These companies are investing heavily in research and development to enhance the functionality and scalability of their MPI solutions. Challenges to growth include high implementation costs, data security concerns, and the complexity of integrating MPI systems with diverse legacy systems across different healthcare settings. Despite these challenges, the long-term outlook remains positive, with continued growth driven by technological advancements, increasing regulatory requirements for data interoperability, and the overarching goal of improving patient safety and care quality. The forecast period of 2025-2033 suggests a significant expansion of this market, with opportunities for both established companies and emerging players alike.

Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. These data include all datasets published for 'CMIP6.HighResMIP.MPI-M.MPI-ESM1-2-XR.hist-1950' 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-XR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T255; 768 x 384 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: 50 km, atmos: 50 km, land: 50 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.

Botswana MPI: Diamonds data was reported at 75.000 2013=100 in Dec 2024. This records an increase from the previous number of 70.900 2013=100 for Sep 2024. Botswana MPI: Diamonds data is updated quarterly, averaging 102.500 2013=100 from Mar 2003 (Median) to Dec 2024, with 87 observations. The data reached an all-time high of 166.100 2013=100 in Sep 2004 and a record low of 33.300 2013=100 in Jun 2020. Botswana MPI: Diamonds data remains active status in CEIC and is reported by Statistics Botswana. The data is categorized under Global Database’s Botswana – Table BW.B001: Mining Production Volume Index.

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

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.

This dataset details changes to multidimensional poverty over time for 34 countries and their sub-national regions where possible. The Global Multidimensional Poverty Index (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 is an appendix to the Methodological Note – Winter 2014/2015 (http://www.ophi.org.uk/multidimensional-poverty-index/mpi-2014-2015/mpi-methodology/) and Multidimensional Poverty Dynamics: Methodology and Results for 34 countries (http://www.ophi.org.uk/wp-content/uploads/OPHI-RP-41a.pdf?0a8fd7).

Please site the data as: Alkire, S., J. M. Roche and A. Vaz (2014): “Multidimensional Poverty Dynamics: Methodology and Results for 34 countries”, Oxford Poverty and Human Development Initiative, Oxford University. ophi.qeh.ox.ac.uk

Coupled Model Intercomparison Project Phase 6 (CMIP6) datasets. These data include all datasets published for 'CMIP6.PMIP.MPI-M.MPI-ESM1-2-LR.past2k' 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.

Mexico MPI: Non-metallic Mineral: Flourite data was reported at 422.812 1993=100 in Mar 2015. This records an increase from the previous number of 412.097 1993=100 for Feb 2015. Mexico MPI: Non-metallic Mineral: Flourite data is updated monthly, averaging 271.000 1993=100 from Jan 1980 (Median) to Mar 2015, with 423 observations. The data reached an all-time high of 482.191 1993=100 in Mar 2011 and a record low of 44.800 1993=100 in Dec 1998. Mexico MPI: Non-metallic Mineral: Flourite 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.