Proportion of people claiming Universal Credit who are in employment. This is based on the count of the number of people on Universal Credit on the second Thursday of each month (completed the Universal Credit claim process and accepted their Claimant Commitment) and have not had a closure of their claim recorded for this spell. A closure of their claim would be recorded either at the request of the individual or if their entitlement to Universal Credit ends, for example, if they no longer satisfy the financial conditions to receive Universal Credit as they have capital over the threshold. To allow sufficient time for earnings information to be gathered on all claimants, figures for the latest month in the series will not be available until the next release. Figures provided for starts show the Jobcentre Plus office recorded at the start of the claim, whereas the figures for the number of people on Universal Credit are representative of the current Jobcentre Plus office that the claimant is attending. It is possible for people to have started on Universal Credit in one office and have moved to another office during their claim, and for this reason, the number of people on Universal Credit can be higher than the starts figure for any particular office, however it is more noticeable when numbers are low. You may be eligible to get Universal Credit if you’re on a low income or out of work, 18 or over (there are some exceptions if you’re 16 to 17), you’re under State Pension age (or your partner is), you and your partner have £16,000 or less in savings between you, and you live in the UK. Universal Credit has replaced Jobseeker’s Allowance (JSA) for most people. It is still possible to claim JSA if you are 18 or over and under State Pension age. As long as you are actively looking for a full-time job and are out of work, or are working less than 16 hours a week. These standalone JSA claims are separately reported. Statistical disclosure control has been applied with Stat-Xplore, which guards against the identification of an individual claimant.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

Proportion of people claiming Universal Credit who are in employment. This is based on the count of the number of people on Universal Credit on the second Thursday of each month (completed the Universal Credit claim process and accepted their Claimant Commitment) and have not had a closure of their claim recorded for this spell. A closure of their claim would be recorded either at the request of the individual or if their entitlement to Universal Credit ends, for example, if they no longer satisfy the financial conditions to receive Universal Credit as they have capital over the threshold. To allow sufficient time for earnings information to be gathered on all claimants, figures for the latest month in the series will not be available until the next release. Figures provided for starts show the Jobcentre Plus office recorded at the start of the claim, whereas the figures for the number of people on Universal Credit are representative of the current Jobcentre Plus office that the claimant is attending. It is possible for people to have started on Universal Credit in one office and have moved to another office during their claim, and for this reason, the number of people on Universal Credit can be higher than the starts figure for any particular office, however it is more noticeable when numbers are low. You may be eligible to get Universal Credit if you’re on a low income or out of work, 18 or over (there are some exceptions if you’re 16 to 17), you’re under State Pension age (or your partner is), you and your partner have £16,000 or less in savings between you, and you live in the UK. Universal Credit has replaced Jobseeker’s Allowance (JSA) for most people. It is still possible to claim JSA if you are 18 or over and under State Pension age. As long as you are actively looking for a full-time job and are out of work, or are working less than 16 hours a week. These standalone JSA claims are separately reported. Statistical disclosure control has been applied with Stat-Xplore, which guards against the identification of an individual claimant.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This table contains employment and unemployment rates (number of people who are employed or unemployed divided by the total number of people in the labour force) for the age groups 15 - 25, 25 - 44, 45 - 64 calculated from the 2011 Census for the AURIN Social Indicators project.

This book had its seeds in 2011, when I found myself at Esri’s annual User Conference in San Diego, California. There, with 15,000 self-proclaimed map geeks, I was astonished to discover a whole community of people who understood the power of visuals to create understanding and trust—and to solve the world’s pressing problems.

Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon.

Please email arockhil@uoregon.edu before submitting a manuscript to be published in a peer-reviewed journal using this data, we wish to ensure that the data to be analyzed and interpreted with scientific integrity so as not to mislead the public about findings that may have clinical relevance. The purpose of this is to be responsible stewards of the data without an "available upon reasonable request" clause that we feel doesn't fully represent the open-source, reproducible ethos. The data is freely available to download so we cannot stop your publication if we don't support your methods and interpretation of findings, however, in being good data stewards, we would like to offer suggestions in the pre-publication stage so as to reduce conflict in published scientific literature. As far as credit, there is precedent for receiving a mention in the acknowledgements section for reading and providing feedback on the paper or, for more involved consulting, being included as an author may be warranted. The purpose of asking for this is not to inflate our number of authorships; we take ethical considerations of the best way to handle intellectual property in the form of manuscripts very seriously, and, again, sharing is at the discretion of the author although we strongly recommend it. Please be ethical and considerate in your use of this data and all open-source data and be sure to credit authors by citing them.

An example of an analysis that we could consider problematic and would strongly advice to be corrected before submission to a publication would be using machine learning to classify Parkinson's patients from healthy controls using this dataset. This is because there are far too few patients for proper statistics. Parkinson's disease presents heterogeneously across patients, and, with a proper test-training split, there would be fewer than 8 patients in the testing set. Statistics on 8 or fewer patients for such a complicated diease would be inaccurate due to having too small of a sample size. Furthermore, if multiple machine learning algorithms were desired to be tested, a third split would be required to choose the best method, further lowering the number of patients in the testing set. We strongly advise against using any such approach because it would mislead patients and people who are interested in knowing if they have Parkinson's disease.

Note that UPDRS rating scales were collected by laboratory personnel who had completed online training and not a board-certified neurologist. Results should be interpreted accordingly, especially that analyses based largely on these ratings should be taken with the appropriate amount of uncertainty.

In addition to contacting the aforementioned email, please cite the following papers:

Nicko Jackson, Scott R. Cole, Bradley Voytek, Nicole C. Swann. Characteristics of Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography. eNeuro 20 May 2019, 6 (3) ENEURO.0151-19.2019; DOI: 10.1523/ENEURO.0151-19.2019.

Swann NC, de Hemptinne C, Aron AR, Ostrem JL, Knight RT, Starr PA. Elevated synchrony in Parkinson disease detected with electroencephalography. Ann Neurol. 2015 Nov;78(5):742-50. doi: 10.1002/ana.24507. Epub 2015 Sep 2. PMID: 26290353; PMCID: PMC4623949.

George JS, Strunk J, Mak-McCully R, Houser M, Poizner H, Aron AR. Dopaminergic therapy in Parkinson's disease decreases cortical beta band coherence in the resting state and increases cortical beta band power during executive control. Neuroimage Clin. 2013 Aug 8;3:261-70. doi: 10.1016/j.nicl.2013.07.013. PMID: 24273711; PMCID: PMC3814961.

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103. https://doi.org/10.1038/s41597-019-0104-8.

Note: see this discussion on the structure of the json files that is sufficient but not optimal and will hopefully be changed in future versions of BIDS: https://neurostars.org/t/behavior-metadata-without-tsv-event-data-related-to-a-neuroimaging-data/6768/25.

This dataset presents the social and economic indicators for the indigenous population of Australia based on the 2016 Census and aggregated following the 2016 edition of the Australian Statistical Geography Standard (ASGS). The data has been provided by The National Centre for Social and Economic Modelling (NATSEM) and includes the following indicators: age, sex, employment, education level, occupation, school attendance, language, household relationships, family types, household tenure type, household income, motor vehicles and household family composition. All indicators were extracted from the ABS Tablebuilder system using the usual residence profile. For usual residence data, the ABS moves people back to where they live, rather than using the location the data were collected (place of enumeration). Usual residence data is preferred for individual level data because it removes the effect of respondents travelling or holidaying. All rates were calculated as a proportion of all Indigenous people in the area, excluding any Not Stated or Overseas Visitors. Therefore, summing the rates across all categories for an indicator will give a total of 100%. For more information please view the NATSEM Technical Report. Please note: AURIN has spatially enabled the original data provided directly from NATSEM. Where data values are NULL, the data is either unpublished or not applicable mathematically.

This dataset presents the employment rate of the population in small regions of Australia based on the 2016 Census and aggregated following the 2016 edition of the Australian Statistical Geography Standard (ASGS). The data has been provided by The National Centre for Social and Economic Modelling (NATSEM). This indicator is the number and proportion of people employed. The rate is calculated as the number employed divided by the total number in that Age/Sex group (excluding Not Stated). Note that the denominator for the total employment rate is total population aged 15-64. All indicators were extracted from the ABS Tablebuilder system using the usual residence profile. For usual residence data, the ABS moves people back to where they live, rather than using the location the data were collected (place of enumeration). Usual residence data is preferred for individual level data because it removes the effect of respondents travelling or holidaying. For more information please view the NATSEM Technical Report.

Data Set Name: Hepatocellular Carcinoma Dataset (HCC dataset)

Abstract: Hepatocellular Carcinoma dataset (HCC dataset) was collected at a University Hospital in Portugal. It contains real clinical data of 165 patients diagnosed with HCC.

Donors: Miriam Seoane Santos (miriams@student.dei.uc.pt) and Pedro Henriques Abreu (pha@dei.uc.pt), Department of Informatics Engineering, Faculty of Sciences and Technology, University of Coimbra Armando Carvalho (aspcarvalho@gmail.com) and Adélia Simão (adeliasimao@gmail.com), Internal Medicine Service, Hospital and University Centre of Coimbra

Data Type: Multivariate Task: Classification, Regression, Clustering, Casual Discovery Attribute Type: Categorical, Integer and Real

Area: Life Sciences Format Type: Matrix Missing values: Yes

Instances and Attributes: Number of Instances (records in your data set): 165 Number of attributes (fields within each record): 49

Relevant Information: HCC dataset was obtained at a University Hospital in Portugal and contais several demographic, risk factors, laboratory and overall survival features of 165 real patients diagnosed with HCC. The dataset contains 49 features selected according to the EASL-EORTC (European Association for the Study of the Liver - European Organisation for Research and Treatment of Cancer) Clinical Practice Guidelines, which are the current state-of-the-art on the management of HCC.

This is an heterogeneous dataset, with 23 quantitative variables, and 26 qualitative variables. Overall, missing data represents 10.22% of the whole dataset and only eight patients have complete information in all fields (4.85%). The target variables is the survival at 1 year, and was encoded as a binary variable: 0 (dies) and 1 (lives). A certain degree of class-imbalance is also present (63 cases labeled as “dies” and 102 as “lives”).

A detailed description of the HCC dataset (feature’s type/scale, range, mean/mode and missing data percentages) is provided in Santos et al. “A new cluster-based oversampling method for improving survival prediction of hepatocellular carcinoma patients”, Journal of biomedical informatics, 58, 49-59, 2015.

Context

This data set has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine) : Travel Review Ratings Data Set. This data set is populated by capturing user ratings from Google reviews. Reviews on attractions from 24 categories across Europe are considered. Google user rating ranges from 1 to 5 and average user rating per category is calculated.

Content

Attribute 1 : Unique user id Attribute 2 : Average ratings on churches Attribute 3 : Average ratings on resorts Attribute 4 : Average ratings on beaches Attribute 5 : Average ratings on parks Attribute 6 : Average ratings on theatres Attribute 7 : Average ratings on museums Attribute 8 : Average ratings on malls Attribute 9 : Average ratings on zoo Attribute 10 : Average ratings on restaurants Attribute 11 : Average ratings on pubs/bars Attribute 12 : Average ratings on local services Attribute 13 : Average ratings on burger/pizza shops Attribute 14 : Average ratings on hotels/other lodgings Attribute 15 : Average ratings on juice bars Attribute 16 : Average ratings on art galleries Attribute 17 : Average ratings on dance clubs Attribute 18 : Average ratings on swimming pools Attribute 19 : Average ratings on gyms Attribute 20 : Average ratings on bakeries Attribute 21 : Average ratings on beauty & spas Attribute 22 : Average ratings on cafes Attribute 23 : Average ratings on view points Attribute 24 : Average ratings on monuments Attribute 25 : Average ratings on gardens

Acknowledgements

This data set has been sourced from the Machine Learning Repository of University of California, Irvine (UC Irvine) : Travel Review Ratings Data Set

The UCI page mentions the following publication as the original source of the data set: Renjith, Shini, A. Sreekumar, and M. Jathavedan. 2018. Evaluation of Partitioning Clustering Algorithms for Processing Social Media Data in Tourism Domain. In 2018 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 12731. IEEE

Inspiration

I'm kind of people who love traveling. But sometimes I've problems like where should I visit? Are there somewhere interesting places matched with my lifestyle? Often I spent hours to search for interesting place to go out. Such a waste of time.

What if we can build a recommender system which can recommend you several interesting venue based on your preferences. With information from Google review, I'll try to divide Google review user into cluster of similar interest for further work of building recommender system based on thier preference.

Context

In Indonesia, election still employs a very manual voting and counting process. Voters would mark a paper ballot then place it in a box. At the end of the voting period, in each voting station, the ballots are counted manually and the result is recorded in a large form, first with tally marks, than with regular digits after counting finished. In order to get the complete election results, the result form from all voting stations must be manually digitized and recapitulated. Due to the large number of voting stations (for presidential election, there will be more than 800,000 voting stations), this is a very tedious and time consuming process.

Now with practically everybody carrying a camera in their pocket, people -- voting officials, candidate's team, or any interested public members -- like to take photo of the completed forms and share them. This gives the idea is to use handwritten digit recognition techniques to assist with the digitization. However due to varying quality of form photo, relying only on handwritten digits is not optimal. It is therefore desired to also be able to recognize counting result directly from the tally marks. Hence, the need for this tally mark dataset.

Content

Tally mark images in this dataset are taken from a subset of voting result form photos from governor election 2020 which is available to the public from election comittee's website (https://pilkada2020.kpu.go.id/). Around 4000 photos are used as the raw data source, resulting in more than 38,000 tally mark images.

https://drive.google.com/uc?id=1D_zxa9npxTWLuC19bmR7V7ytP6oF3wZG" alt="">

Original forms are aligned to match a form template. Then, each tally mark cell is cropped. The image is then converted to white on black. Preprocessing is then performed to isolated the mark from cell borders which may be included due to imperfect alignment process. The isolated mark is then resized while maintaining aspect ratio to fit 22x28 bounding box. 1-pixel border is then added to bring final image size to 24x30.

In addition to tally marks from 1 to 5, X (cross) marks which represent blank cells are also included in the dataset. Due to its nature, most tally marks are 5 and X. To increase the size of the dataset, each tally mark 1 to 4 are flipped horizontally, flipped vertically and rotated 180 degree. The name of each image files has indication of the transformation applied (o=original, h=horizontally flipped, v=vertically flipped, r=rotated).

Note that some images have quality issues such as: - The mark looks like a blob. This is either because of low quality of the form photo (blurry or very low contrast) or a thick marker was used to draw the tally mark (or both). This issue is mostly occurs on tally mark 4 or 5. https://drive.google.com/uc?id=1Jq_318LEV1pGDxz2QO4anYJS6PAaqox-" alt=""> https://drive.google.com/uc?id=12KdgX5JFg5Uo1Q85e0G3lGGCKV2ZvSwd" alt=""> - Some portion of the cell border is still included. As mentioned some preprocessing (with heuristics) was done to remove the borders. But this process is not perfect. https://drive.google.com/uc?id=1FZa2i88Qal6T7CGDxj1kcReJAkhu22RO" alt=""> https://drive.google.com/uc?id=16H-99YAAEVl3eSE_-AcO9HEeBAKr1i2t" alt="">

I did manual filtering to remove the worst samples, but some still slip through. Besides, I'm also conflicted regarding the blobs. If I remove them all, I'm worried the samples will be too perfect, hence will not reflect actual input. So basicaly if the blob is still somewhat recognizable by me, I tend to include it.

Also note: Since the dataset is saved as JPG images, due to lossy compression, the background is not trully black. Some has non-zero but low (<10) intensity.

Acknowledgements

Indonesia Election Committed (KPU - Komisi Pemilihan Umum) - https://pilkada2020.kpu.go.id/