The index provides the only comprehensive measure available for non-income poverty, which has become a critical underpinning of the SDGs. Critically the MPI comprises variables that are already reported under the Demographic Health Surveys (DHS) and Multi-Indicator Cluster Surveys (MICS) The resources subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI methodology is detailed in Alkire, Kanagaratnam & Suppa (2023)

This dataset contains detailed Global Multidimensional Poverty Index (MPI) data at the sub-national level for 71 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 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.

Scottish Marine and Freshwater Science Vol 8 No 11 This report has been prepared to accompany the UK Climate Projections 2009 (UK CP09) layers added to 'Marine Scotland MAPS NMPi'. NMPi is Marine Scotland's on-line portal to provide spatial information and data to support national and regional marine planning and the state of the sea assessments required to support national and regional planning. This report briefly summarises the climate change data layers selected. A variety of time baselines are used in the climate change future projections (although all are generally for about 100 years in the future). Unfortunately the different time periods reflect how the source information was published, and this cannot be rectified in this report. Users can convert changes over the period published here to relative change per year, or per decade, and then estimate the size of changes for any specific year of their interest. When multiple emission scenarios have been used to give a range of future climate variable possibilities, the 50% probability solution has been used. For projections when only one emissions scenario is available, a medium emissions scenario of future societal change has been used in this report, although other emissions scenarios are available in the information sources listed.

This table contains subnational multidimensional poverty data from the data tables published by the Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. The global Multidimensional Poverty Index (MPI) measures multidimensional poverty in over 100 developing countries, using internationally comparable datasets and is updated annually. The measure captures the severe deprivations that each person faces at the same time using information from 10 indicators, which are grouped into three equally weighted dimensions: health, education, and living standards. The global MPI 2021 methodology is detailed in Alkire, Kanagaratnam & Suppa (2021).

The average manufacturing production index (MPI) of food products for the period between January to September 2020 in Thailand was 102.15 index points. This indicated a distinctive decrease when compared to the average manufacturing production index in that same duration in 2019.

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

As of October 2024, Thailand's manufacturing production index (MPI) of food products stood at around 95.6 points. In that period, the overall MPI in the country was over 96 index points.

As of October 2024, Thailand's manufacturing production index (MPI) of motor vehicles, trailers, and semi-trailers stood at around 92.13 points. In that period, the overall MPI in the country reached over 96 index points.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

As of October 2024, Thailand's manufacturing production index (MPI) of the coke and refined petroleum segment stood at around 119 points. In that period, the overall MPI in the country was over 96 index points.

Register of novel technologies and alternative premises and equipment designs that do not meet the requirements NZCP1 are deemed to be suitable for use in farm dairies if they have been assessed, confirmed as acceptable and listed as such on this MPI Register.Contains information: MPI Ref, Manufacturer/Supplier, Item, Status, Comment/Qualifier, Date of Determination, Review Date Novel technologies and alternative premises and equipment designs that do not meet the requirements of NZCP1 but have been assessed as to be suitable to use in farm dairies

MPI (Max Planck Institute) Sintel is a dataset for optical flow evaluation that has 1064 synthesized stereo images and ground truth data for disparity. Sintel is derived from open-source 3D animated short film Sintel. The dataset has 23 different scenes. The stereo images are RGB while the disparity is grayscale. Both have resolution of 1024×436 pixels and 8-bit per channel.

Mexico MPI: Non-metallic Mineral: Feldspar data was reported at 134.737 1993=100 in Mar 2015. This records a decrease from the previous number of 213.463 1993=100 for Feb 2015. Mexico MPI: Non-metallic Mineral: Feldspar data is updated monthly, averaging 213.463 1993=100 from Jan 1988 (Median) to Mar 2015, with 327 observations. The data reached an all-time high of 473.259 1993=100 in Mar 2006 and a record low of 57.400 1993=100 in Sep 1988. Mexico MPI: Non-metallic Mineral: Feldspar 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.

As of the fourth quarter of 2023, Thailand's MPI year-on-year growth rate contracted by 5.1 percent. The decline in the growth of the MPI was due to the drop in the manufacturing of products including computers, peripherals, furniture, and plastic and synthetic rubber.

In 2023, the return of the national NCREIF Property Index in the United States declined for the first time since 2009. The annualized total return of the index plummeted in 2023, followed by a slight increase in 2024. Just three years ago, in 2021, the rate of return of the index hit 17.7 percent. The NCREIF Property Index reflects the change in prices of commercial real estate for investment purposes in the United States. Property types with the highest cap rates Cap rates, which measure the expected return rate of a real estate asset, were the highest for retail properties in 2023. While a higher cap rate indicates a higher rate of return, it is also associated with higher risk: The multifamily sector, which has enjoyed steady and robust growth in recent years, had the lowest cap rate of all commercial property types. Commercial property area with the best development prospects In 2025, the real estate development opportunities for single-family housing were deemed to be the best when compared with other types of commercial property. Industrial real estate includes warehouses, factories, and big box distribution centers.

[ Derived from parent entry - See data hierarchy tab ]

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.

Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

The aim of the Human Development Report is to stimulate global, regional and national policy-relevant discussions on issues pertinent to human development. Accordingly, the data in the Report require the highest standards of data quality, consistency, international comparability and transparency. The Human Development Report Office (HDRO) fully subscribes to the Principles governing international statistical activities.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions.

The 2019 Global Multidimensional Poverty Index (MPI) data shed light on the number of people experiencing poverty at regional, national and subnational levels, and reveal inequalities across countries and among the poor themselves.Jointly developed by the United Nations Development Programme (UNDP) and the Oxford Poverty and Human Development Initiative (OPHI) at the University of Oxford, the 2019 global MPI offers data for 101 countries, covering 76 percent of the global population. The MPI provides a comprehensive and in-depth picture of global poverty – in all its dimensions – and monitors progress towards Sustainable Development Goal (SDG) 1 – to end poverty in all its forms. It also provides policymakers with the data to respond to the call of Target 1.2, which is to ‘reduce at least by half the proportion of men, women, and children of all ages living in poverty in all its dimensions according to national definition'.

Mexico MPI: Non-Metallic Mineral: Fluorite data was reported at 90.025 2013=100 in Mar 2019. This records a decrease from the previous number of 109.352 2013=100 for Feb 2019. Mexico MPI: Non-Metallic Mineral: Fluorite data is updated monthly, averaging 75.217 2013=100 from Jan 2000 (Median) to Mar 2019, with 231 observations. The data reached an all-time high of 122.190 2013=100 in May 2018 and a record low of 12.205 2013=100 in Oct 2002. Mexico MPI: Non-Metallic Mineral: Fluorite 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.

In August 2024, the manufacturing production index (MPI) of infusion sets in Thailand stood at around 119.42 index points. In the same period, syringes had an MPI of 84.75 index points.

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.