Open Data for Resilience Initiative (OpenDRI) applies the concepts of the global open data movement to the challenges of reducing vulnerability to natural hazards and the impacts of climate change. OpenDRI supports World Bank Regional Disaster Risk Management Teams to build capacity and long-term ownership of open data projects with client countries that are tailored to meet specific needs and goals of stakeholders around three main areas of Sharing Data, Collecting Data, Using Data. All data is published under an open license. Projects include Open Cities Africa, with national projects in: Niger (flood hostpots and mitigation), Uganda (drought risk information and disaster risk financing), Zanzibar (vunlerability to natural disasters), Pacific Islands (Natural Disasters and Climate Change), Sri Lanka (evidence based methods for natural disaster response), Afghanistan (disaster risk decisionmaking), St Vincent and the Grenadines (hydroclimatic disasters), Saint Lucia (post disaster rehabilitation), Jamaica (storm even impact), Serbia (disaster preparedness), Indonesia (disaster management especially flooding), Seychelles (site specific risks of floods, earthquakes, cyclones, storm surge and tsunamis), Muaritius (under development), Madagascar (under development), Vietnam (natural hazards especially flood risks and climate change impacts), Bangladesh (under development), Pakistan (earthquakes and monsoon floods), Nepal (Seismic risk), Haiti (storms, flooding, landslides, environmental degradation), Guyana (under development), Grenada (under development), Dominica (extreme weather events), Colombia (flooding, landslides, increased vulnerability due to insufficient urban planning), Antigua and Barbuda (cyclones, fires and flooding), Belize (storm, flood and tsunami risks), Bolivia (natural hazards and climate change), Kyrgyz Republic (risk data on meteorological, geological, geophyical and boilogical hazards), Philippines (typhoones and monsoon floods recovery data), Tanzania (flood maps), Mozambique (flood, cyclone and windstorms), Comoros (flood, storm, volcanic eruption), Malawi (information to develop schools, healthcare and agriculture against floods and droughts), Armenia (earthquakes, drought, hailstorms, landslides)

The dataset contains tri-axial accelerometer and tri-axial gyroscope readings from the four IMUs and labels. There are three sub-datasets, which have different ground-truth labelling configurations, included in this dataset. Please note that the labelling is subjective to the mother's perception. The dataset, as a whole, contains recordings spanning 14 weeks from 26th week to 39th week and in total, about 71 hours of recordings.

The three sub-datasets included are:

Sub-dataset One: In this sub-dataset, only the occurrence of the particular type of fetal movement known as the fetal kick is considered for ground-truth labelling.

Sub-dataset Two: All types of fetal movement felt by the mother -- including trunk movement, isolated limb movement, and general body movement -- were considered for ground truth-labelling as fetal movements in this sub-dataset.

Sub-dataset Three: In this sub-dataset, the emphasis was given to the classification of different types of fetal movements. Three types of fetal movements are labelled: trunk movement, isolated limb movement, and general body movement.

Additional data are provided in three additional 'csv' files, which contains the record number, the Period of Amenorrhoea (POA), start time, and end time of each recording. Also, additional details about the mother and the baby are provided in the README file.

For more details, refer to the README.pdf.

The primary objective from this project was to acquire historical shoreline information for all of the Northern Ireland coastline. Having this detailed understanding of the coast’s shoreline position and geometry over annual to decadal time periods is essential in any management of the coast.The historical shoreline analysis was based on all available Ordnance Survey maps and aerial imagery information. Analysis looked at position and geometry over annual to decadal time periods, providing a dynamic picture of how the coastline has changed since the start of the early 1800s.Once all datasets were collated, data was interrogated using the ArcGIS package – Digital Shoreline Analysis System (DSAS). DSAS is a software package which enables a user to calculate rate-of-change statistics from multiple historical shoreline positions. Rate-of-change was collected at 25m intervals and displayed both statistically and spatially allowing for areas of retreat/accretion to be identified at any given stretch of coastline.The DSAS software will produce the following rate-of-change statistics:Net Shoreline Movement (NSM) – the distance between the oldest and the youngest shorelines.Shoreline Change Envelope (SCE) – a measure of the total change in shoreline movement considering all available shoreline positions and reporting their distances, without reference to their specific dates.End Point Rate (EPR) – derived by dividing the distance of shoreline movement by the time elapsed between the oldest and the youngest shoreline positions.Linear Regression Rate (LRR) – determines a rate of change statistic by fitting a least square regression to all shorelines at specific transects.Weighted Linear Regression Rate (WLR) - calculates a weighted linear regression of shoreline change on each transect. It considers the shoreline uncertainty giving more emphasis on shorelines with a smaller error.The end product provided by Ulster University is an invaluable tool and digital asset that has helped to visualise shoreline change and assess approximate rates of historical change at any given coastal stretch on the Northern Ireland coast.

The dataset includes movement sensor data from sensors placed on the collar and the harness of a dog and recorded while the dog is given tasks or activities to perform. The task are: galloping, lying on chest, sitting, sniffing, standing, trotting, and walking. The movement sensors used are: ActiGraph GT9X Link (ActiGraph LLC, Florida, USA) and they include 3D accelerometer and 3D gyroscope. The sampling rate used is 100 Hz.

The dataset is described in more detail in the data description article: Vehkaoja, A., Somppi, S., Törnqvist, H., Valldeoriola Cardó, A., Kumpulainen, P., Väätäjä, H., Majaranta, P., Surakka, V., Kujala, M. V., Vainio, O., Description of Movement Sensor Dataset for Dog Behavior Classification, Data in Brief, 2021. The behavior classification results obtained with the dataset are published in: Kumpulainen, P., Valldeoriola Cardó, A., Somppi, S., Törnqvist, H., Väätäjä, H., Majaranta, P., Gizatdinova, Y., Hoog Antink, C., Surakka, V., Kujala, M. V., Vainio, O., and Vehkaoja, A., Dog behaviour classification with movement sensors placed on the harness and the collar, Applied Animal Behavior Science, 2021.

The authors of the dataset request researchers to refer to the aforementioned publications when using the data and publishing results produced using it.

The Migration Data Explorer enables you to easily access migration data to address the overarching research themes and enduring questions that relate to migration in New Zealand.

Acknowledgements

This data set was originally created and contributed to PhysioBank by Gerwin Schalk (schalk at wadsworth dot org) and his colleagues at the BCI R&D Program, Wadsworth Center, New York State Department of Health, Albany, NY. W.A. Sarnacki collected the data. Aditya Joshi compiled the dataset and prepared the documentation. D.J. McFarland and J.R. Wolpaw were responsible for experimental design and project oversight, respectively. This work was supported by grants from NIH/NIBIB ((EB006356 (GS) and EB00856 (JRW and GS)).

To access the initial publication of this dataset, please visit this link to PhysioBank: https://physionet.org/content/eegmmidb/1.0.0/

Experiment Protocol

This data set consists of over 1500 one- and two-minute EEG recordings, obtained from 109 volunteers, as described below.

Subjects performed different motor/imagery tasks while 64-channel EEG were recorded using the BCI2000 system (http://www.bci2000.org). Each subject performed 14 experimental runs: two one-minute baseline runs (one with eyes open, one with eyes closed), and three two-minute runs of each of the four following tasks:

[Task 1] A target appears on either the left or the right side of the screen. The subject opens and closes the corresponding fist until the target disappears. Then the subject relaxes.
[Task 2] A target appears on either the left or the right side of the screen. The subject imagines opening and closing the corresponding fist until the target disappears. Then the subject relaxes.
[Task 3] A target appears on either the top or the bottom of the screen. The subject opens and closes either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.
[Task 4] A target appears on either the top or the bottom of the screen. The subject imagines opening and closing either both fists (if the target is on top) or both feet (if the target is on the bottom) until the target disappears. Then the subject relaxes.

In summary, the experimental runs were:
1. Baseline, eyes open
2. Baseline, eyes closed
3. Task 1 (open and close left or right fist)
4. Task 2 (imagine opening and closing left or right fist)
5. Task 3 (open and close both fists or both feet)
6. Task 4 (imagine opening and closing both fists or both feet)
7. Task 1
8. Task 2
9. Task 3
10. Task 4
11. Task 1
12. Task 2
13. Task 3
14. Task 4

Each event code includes an event type indicator (T0, T1, or T2) that is concatenated to the Task # it belongs with (i.e TASK1T2). The event type indicators change definition depending on the Task # it is associated with. For example, TASK1T2 would correspond to the onset of real motion in the right fist, while TASK3T2 would correspond to onset of real motion in both feet:

[T0] corresponds to rest

[T1] corresponds to onset of motion (real or imagined) of:

• the left fist (in runs 3, 4, 7, 8, 11, and 12; for Task 1 (real) and Task 2 (imagined))
• both fists (in runs 5, 6, 9, 10, 13, and 14; for Task 3 (real) and Task 4 (imagined))

[T2] corresponds to onset of motion (real or imagined) of:

• the right fist (in runs 3, 4, 7, 8, 11, and 12; Task 1 (real) and Task 2 (imagined))
• both feet (in runs 5, 6, 9, 10, 13, and 14; for Task 3 (real) and Task 4 (imagined))

Note: The data files in this dataset were converted into the .set format for EEGLAB. The event codes in the .set files of this dataset will contain the concatenated event codes above for all event files for clarity purposes. The non-converted .edf files along with the accompanying PhysioBank-compatible annotation files for all the runs of each subject can be found in the sourcedata folder. In the non-converted .edf files the event codes will only be shown as T0, T1, and T2 regardless of task type. All the Matlab scripts used for the .set conversion and renaming of event codes of the PhysioBank .edf files can be found in the code folder.

Montage

The EEGs were recorded from 64 electrodes as per

UI-PRMD is a data set of movements related to common exercises performed by patients in physical therapy and rehabilitation programs. The data set consists of 10 rehabilitation exercises. A sample of 10 healthy individuals repeated each exercise 10 times in front of two sensory systems for motion capturing: a Vicon optical tracker, and a Kinect camera. The data is presented as positions and angles of the body joints in the skeletal models provided by the Vicon and Kinect mocap systems.

Data generated as part of the PhD research of Tommy Ashby, investigating the effect of yaw-based head movement on the auditory perception of sound source elevation. Data comprise audio files, listening test interfaces, results and MATLAB code for result processing/analysis.

Further project details can be found at http://iosr.uk/elevation

Data/Publication Cross-Reference

The data archive is structured according to the thesis chapter describing the experiment(s) in which the data were generated/used. The following is a cross-reference to/from other publications in which data have been used. Please refer to the reference list below.

Data —> Publications

Thesis chapter 6 ICSV21

Thesis chapter 7 ICSV21

Thesis chapter 8 AES134 ICSV21

Thesis chapter 9 ICSV21

Thesis chapter 10 ICSV21

Thesis chapter 11 AES136 ICSV21

Thesis chapter 12 [n/a]

Publications —> Data

AES134 Thesis chapter 8

AES136 Thesis chapter 11

ICSV21 Thesis chapter 6 Thesis chapter 7 Thesis chapter 8 Thesis chapter 9 Thesis chapter 10 Thesis chapter 11

Animals navigate landscapes based on perceived risks vs. rewards, as inferred from features of the landscape. In the wild, knowing how strongly animal movement is directed by landscape features is difficult to ascertain, but widespread disturbances such as wildfires can serve as natural experiments. We tested the hypothesis that wildfires homogenize the risk/reward landscape, causing movement to become less directed, given that fires reduce landscape complexity as habitat structures (e.g., tree cover, dense brush) are burned. We used satellite imagery of a research reserve in Northern California to count and categorize paths made primarily by mule deer (Odocoileus hemionus) in grasslands. Specifically, we compared pre-wildfire (August 2014) and post-wildfire (September 2018) image history layers among locations that were or were not impacted by wildfire (i.e., a Before/After Control/Impact design). Wildfire significantly altered spatial patterns of deer movement: more new paths were gained and more old paths were lost in areas of the reserve that were impacted by wildfire; movement patterns became less directed in response to fire, suggesting that the risk/reward landscape became more homogenous, as hypothesized. We found evidence to suggest that wildfire affects deer populations at spatial scales beyond their scale of direct impact and raises the interesting possibility that deer perceive risks and rewards at different spatial scales. In conclusion, our study provides an example of how animals integrate spatial information from the environment to make movement decisions, setting the stage for future work on the broader ecological implications for populations, communities, and ecosystems, an emerging interest in ecology. Methods Animal paths were traced from satellite imagery before and after a wildfire, in areas impacted by fire and in unimpacted areas (i.e., a BACI design). Paths were then counted, categorized by size, and assessed to see whether paths were persistent or were formed or lost dynamically over time. This was a pandemic project.

Mass movement**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Upper water mass movement of ocean currents from the Irish Offshore Strategic Environmental Assessment (IOSEA 5). Large masses of moving water are called currents. In the oceans there are major surface currents, subsurface currents, and tidal currents. This data represents an analysis of the mass movement of water in the upper sections of the water column. Currents in the upper water column are known as the North Atlantic Current and Shelf edge current.

The migration of data to public cloud services continues to evolve, with varying approaches across different data types. A 2024 survey reveals that 37 percent of organizations plan to move all their nonsensitive analytics data to cloud or software as a service (SaaS) platforms. However, sensitive information like corporate financial data remains more firmly rooted on-premises, with 20 percent of organizations keeping it entirely local. Cloud service market growth The cloud services market is experiencing significant expansion, particularly in infrastructure as a service (IaaS). Forecasts indicate a 33 percent growth in IaaS for 2024 compared to 2022, outpacing the overall public cloud services market's expected 20 percent growth. This trend is reflected in the robust performance of major tech companies, with Microsoft leading the pack as the top SaaS company by market capitalization at 3 trillion U.S. dollars in May 2024. Enterprise cloud adoption strategies Organizations are increasingly focused on optimizing their cloud usage, with 60 percent of respondents in 2024 prioritizing cost savings in their cloud initiatives. Simultaneously, 58 percent are committed to migrating more workloads to the cloud, indicating a continued shift towards cloud-based operations. This strategic approach aligns with the diverse cloud adoption patterns observed across different data types, as companies balance the benefits of cloud services with the need for data security and compliance.

The data set comprises raw and processed lower extremity gait kinematics and kinetics signals of 39 subjects in different file formats (c3d and txt). A file of metadata (in txt and xls formats), including demographics, running characteristics, foot-strike patterns, and muscle strength and flexibility measurements is provided. In addition, a model file (mdh) and a pipeline file (v3s) for the Visual 3D software program are also provided. The data were collected using a three-dimensional (3D) motion-capture system and an instrumented treadmill while the subjects ran at 2.5 m/s, 3.5 m/s, and 4.5 m/s wearing standard neutral shoes.

This dataset contains key characteristics about the data described in the Data Descriptor An Asian-centric human movement database capturing activities of daily living. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

The USGS National Hydrography Dataset (NHD) Downloadable Data Collection from The National Map (TNM) is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. For additional information on NHD, go to https://www.usgs.gov/core-science-systems/ngp/national-hydrography.

DWR was the steward for NHD and Watershed Boundary Dataset (WBD) in California. We worked with other organizations to edit and improve NHD and WBD, using the business rules for California. California's NHD improvements were sent to USGS for incorporation into the national database. The most up-to-date products are accessible from the USGS website. Please note that the California portion of the National Hydrography Dataset is appropriate for use at the 1:24,000 scale.

For additional derivative products and resources, including the major features in geopackage format, please go to this page: https://data.cnra.ca.gov/dataset/nhd-major-features Archives of previous statewide extracts of the NHD going back to 2018 may be found at https://data.cnra.ca.gov/dataset/nhd-archive.

In September 2022, USGS officially notified DWR that the NHD would become static as USGS resources will be devoted to the transition to the new 3D Hydrography Program (3DHP). 3DHP will consist of LiDAR-derived hydrography at a higher resolution than NHD. Upon completion, 3DHP data will be easier to maintain, based on a modern data model and architecture, and better meet the requirements of users that were documented in the Hydrography Requirements and Benefits Study (2016). The initial releases of 3DHP will be the NHD data cross-walked into the 3DHP data model. It will take several years for the 3DHP to be built out for California. Please refer to the resources on this page for more information.

The FINAL,STATIC version of the National Hydrography Dataset for California was published for download by USGS on December 27, 2023. This dataset can no longer be edited by the state stewards.

Questions about the California stewardship of these datasets may be directed to nhd_stewardship@water.ca.gov.

NOTE: We plan to no longer update this dataset after May 22 2022.

These data sets are intended to inform researchers and public health experts about how populations are responding to physical distancing measures. In particular, there are two metrics, Change in Movement and Stay Put, that provide a slightly different perspective on movement trends. Change in Movement looks at how much people are moving around and compares it with a baseline period that predates most social distancing measures, while Stay Put looks at the fraction of the population that appear to stay within a small area during an entire day.

Full details, including the privacy protections in this data, are available here: https://research.fb.com/blog/2020/06/protecting-privacy-in-facebook-mobility-data-during-the-covid-19-response/

Overview —————————————————— This data is from the paper "Capacity for movement is a major organisational principle in object representations". This is the data of Experiment 3 (EEG: movement). Access the preprint here: https://psyarxiv.com/3x2qh/

Abstract: The ability to perceive moving objects is crucial for survival and threat identification. Recent neuroimaging evidence has shown that the visual system processes objects on a spectrum according to their ability to engage in self-propelled, goal-directed movement. The association between the ability to move and being alive is learned early in childhood, yet evidently not all moving objects are alive. Natural, non-agentive movement (e.g., in clouds, or fire) cause confusion in children and adults under time pressure. In the current study, we investigated the relationship between movement and aliveness using both behavioural and neural measures. We examined electroencephalographic (EEG) data recorded while participants viewed static images of moving or non-moving objects that were either natural or artificial. Participants classified the images according to aliveness, or according to capacity for movement. Behavioural classification showed two key categorisation biases: moving natural things were often mistaken to be alive, and often classified as not moving. Movement explained significant variance in the EEG data, during both a classification task and passive viewing. These results highlight that capacity for movement is an important dimension in the structure of human visual object representations.

In this experiment, participants completed two tasks - classification and passive viewing. In the classification task, participants classified single images that appeared on the screen as "can move" or "still". This task was time-pressured, and trials timed out after 1 second. In the passive viewing task, participants viewed rapid (RSVP) streams of images, and pressed a button to indicate when the fixation cross changed colour.

Contents of the dataset: - Raw EEG data is available in individual subject folders (BrainVision raw formats .eeg, .vmrk, .vhdr). Pre-processed EEG data is available in the derivatives folders in EEGlab (.set, .fdt) and cosmoMVPA dataset (.mat) format. This experiment has 24 subjects. - Scripts for data analysis and running the experiment are available in the code folder. Note that all code runs on both EEG experiments together, so you must download both this and the movement experiment data in order to replicate analyses. - Stimuli are also available (400 CC0 images) - Results of decoding analyses are available in the derivatives folder.

Further notes:

Note that the code is designed to run analyses for data and its partner data (experiments 2 and 3 of the paper). Copies in both folders are identical. Scripts need to be run in a particular order (detailed at the top of each script)

Further explanations of the code:

1. Run pre-processing of EEG (analyse_EEG_preprocessing.m), and behavioural data (analyse_behavioural_EEG.m)
2. Ensure that the MTurk data has been run (analyse_behavioural_MTurk.m), from the Experiment 1 folder.
3. Run RSA (analyse_rsa.m; reliant on behavioural data and pre-processed EEG data), and run decoding (analyse_decoding.m; reliant on pre-processed EEG data)
4. Run GLMs (analyse_glms.m; reliant on RSA, behavioural)

To only look at the results, the results for each of these analyses is saved in the derivatives already, so there is no need to run any of them again.

Each file named plot_X.m will create a graph as in the paper. Each is reliant on saved data from the above analyses, which are saved in the derivatives folder.

Citing this dataset ——————————————————— If using this data, please cite the associated paper:

Contact ———————————————————

Contact Sophia Shatek (sophia.shatek@sydney.edu.au) for additional information. ORCID: 0000-0002-7787-1379

This dataset contains a list of sales and movement data by item and department appended monthly. Update Frequency : Monthly

Global matrices of bilateral migrant stocks spanning the period 1960-2000, disaggregated by gender and based primarily on the foreign-born concept are presented. Over one thousand census and population register records are combined to construct decennial matrices corresponding to the last five completed census rounds.For the first time, a comprehensive picture of bilateral global migration over the last half of the twentieth century emerges. The data reveal that the global migrant stock increased from 92 to 165 million between 1960 and 2000. South-North migration is the fastest growing component of international migration in both absolute and relative terms. The United States remains the most important migrant destination in the world, home to one fifth of the world™s migrants and the top destination for migrants from no less than sixty sending countries. Migration to Western Europe remains largely from elsewhere in Europe. The oil-rich Persian Gulf countries emerge as important destinations for migrants from the Middle East, North Africa and South and South-East Asia. Finally, although the global migrant stock is still predominantly male, the proportion of women increased noticeably between 1960 and 2000.

This project is to determine horizontal and vertical movement patterns of two jellyfish species in Hood Canal, in relation to environmental variables. It is being conducted by NMFS scientists in collaboration with a NOAA Hollings Scholar; we also are making use of publicly available oceanographic data from the University of Washington. We used acoustic tags and receivers to track jellyfish movement patterns and correlated their movements with oceanographic data. This project will produce peer reviewed manuscripts. The target audience is fisheries and marine resource managers in Puget Sound and along the West Coast. This is a one-time, standalone project without a firm deadline. This data set contains acoustic telemetry data for lions mane and fried egg jellyfish.