The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions,

Soft X-ray (SX) experimental data of SrTi(1-x)V(x)O3 thin films, including X-ray absorption spectroscopy (XAS) data, resonant inelastic X-ray scattering (RIXS) data, and accompanying density functional theory (DFT) + dynamical mean-field theory (DMFT) calculations. Complete download (zip, 299.1 KiB)

Ask Ubuntu temporal network

https://snap.stanford.edu/data/sx-askubuntu.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Ask Ubuntu (http://askubuntu.com/). There are three different types of
interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph sx-askubuntu-a2q) user u commented on user v's question at time t (in the graph
sx-askubuntu-c2q) user u commented on user v's answer at time t (in the
graph sx-askubuntu-c2a)

The graph sx-askubuntu contains the union of these graphs. These graphs
were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-askubuntu)
Nodes 159,316
Temporal Edges 964,437
Edges in static graph 596,933
Time span 2613 days

Dataset statistics (sx-askubuntu-a2q)
Nodes 137,517
Temporal Edges 280,102
Edges in static graph 262,106
Time span 2613 days

Dataset statistics (sx-askubuntu-c2q)
Nodes 79,155
Temporal Edges 327,513
Edges in static graph 198,852
Time span 2047 days

Dataset statistics (sx-askubuntu-c2a)
Nodes 75,555
Temporal Edges 356,822
Edges in static graph 178,210
Time span 2418 days

Source (citation)
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. "Motifs in Temporal Networks." In Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, 2017.

Files
File Description
sx-askubuntu.txt.gz All interactions
sx-askubuntu-a2q.txt.gz Answers to questions
sx-askubuntu-c2q.txt.gz Comments to questions
sx-askubuntu-c2a.txt.gz Comments to answers

Data format

SRC DST UNIXTS                             

where edges are separated by a new line and

SRC: id of the source node (a user)                  
TGT: id of the target node (a user)                  
UNIXTS: Unix timestamp (seconds since the epoch)            

Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:

The SNAP graph is 1-based, with nodes in all graphs numbered 1 to
n=515,280.

In the SuiteSparse Matrix Collection, the primary matrix, Problem.A, is
the overall static graph, with 596,993 edges, of size n-by-n with
n=159,316. These edges represent the 964,437 temporal edges. A(i,j) is
the number of times person u=nodeid(i) interacted with person v=nodeid(j), with a temporal edge (u,v,t), with any kind of interaction.
Problem.aux.nodeid is a list of the node id's that appear in the SNAP data set.

A2Q = Problem.aux.Q2A is the static sx-askubuntu-a2q graph.
C2Q = Problem.aux.C2Q is the static sx-askubuntu-c2q graph.
C2A = Problem.aux.C2A is the static sx-askubuntu-c2a graph.
These sum together to give the the overall graph. That is,
A = A2Q + C2Q + C2A.

A2Q(u,v) is the number of times person u answered v's questions.
C2Q(u,v) is the number of times person u commented on v's question.
C2A(u,v) is the number of times person u commented on v's answer.

The temporal edges are held in:
Problem.aux.temporal_edges: [964437x3]
Problem.aux.temporal_edges_a2q: [280102x3]
Problem.aux.temporal_edges_c2q: [327513x3]
Problem.aux.temporal_edges_c2a: [356822x3]

Each row in these matrices is a single temporal edge, (u,v,t). Summing up all entries in A gives 964,437, and likewise the sum of entries in the
other graphs gives the number of temporal edges they represent.

Math Overflow temporal network

https://snap.stanford.edu/data/sx-mathoverflow.html

Dataset information

This is a temporal network of interactions on the stack exchange web site
Math Overflow (http://mathoverflow.net/). There are three different types
of interactions represented by a directed edge (u, v, t):

user u answered user v's question at time t (in the graph
sx-mathoverflow-a2q) user u commented on user v's question at time t (in
the graph sx-mathoverflow-c2q) user u commented on user v's answer at time t (in the graph sx-mathoverflow-c2a)

The graph sx-mathoverflow contains the union of these graphs. These graphs were constructed from the Stack Exchange Data Dump. Node ID numbers
correspond to the 'OwnerUserId' tag in that data dump.

Dataset statistics (sx-mathoverflow)
Nodes 24,818
Temporal Edges 506,550
Edges in static graph 239,978
Time span 2350 days

Dataset statistics (sx-mathoverflow-a2q)
Nodes 21,688
Temporal Edges 107,581
Edges in static graph 90,489
Time span 2350 days

Dataset statistics (sx-mathoverflow-c2q)
Nodes 16,836

Experimental estimates of the prevalence and duration of long COVID symptoms, and rates of adverse events for hospitalised coronavirus (COVID-19) patients compared with those for matched control patients.

As part of an ongoing partnership with the Census Bureau, the National Center for Health Statistics (NCHS) recently added questions to assess the prevalence of post-COVID-19 conditions (long COVID), on the experimental Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. Data collection began on April 23, 2020. Beginning in Phase 3.5 (on June 1, 2022), NCHS included questions about the presence of symptoms of COVID that lasted three months or longer. Phase 3.5 will continue with a two-weeks on, two-weeks off collection and dissemination approach. Estimates on this page are derived from the Household Pulse Survey and show the percentage of adults aged 18 and over who a) as a proportion of the U.S. population, the percentage of adults who EVER experienced post-COVID conditions (long COVID). These adults had COVID and had some symptoms that lasted three months or longer; b) as a proportion of adults who said they ever had COVID, the percentage who EVER experienced post-COVID conditions; c) as a proportion of the U.S. population, the percentage of adults who are CURRENTLY experiencing post-COVID conditions. These adults had COVID, had long-term symptoms, and are still experiencing symptoms; d) as a proportion of adults who said they ever had COVID, the percentage who are CURRENTLY experiencing post-COVID conditions; and e) as a proportion of the U.S. population, the percentage of adults who said they ever had COVID.

This data set has been obtained after processing original data from the ICP Forests data infrastructure. It includes "clean" defoliation and symptoms data from 19 countries over the period 1990-2022 after removing dubious cases of e.g. species attribution, dubious coding, long-standing dead trees. It is based on ICP Forests Level I plots. Defoliation is the relative loss (shed or not formed) of tree needles / leaves in relation to a hypothetical fully foliated optimum and is visually assessed using a sliding scale recorded in 5% steps (from 0%= no defoliation to 100%=standing dead tree). Occurrence of s^Symptoms attributable to damaging agents (e.g., insects, fungi, drought, hail, fire, direct action of men…) on each tree and associated to defoliation are also included in this data set. This data set includes a total of 2’688’512 observations from 219’854 trees in 12’104 plots.

Results from the COVID-19 and Respiratory Infections Survey.

This dataset contains the predicted prices of the asset SX Network over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Comprehensive dataset containing 11 verified Health and beauty shop businesses in SX with complete contact information, ratings, reviews, and location data.

Estimates of the prevalence of self-reported long COVID and associated activity limitation, using UK Coronavirus (COVID-19) Infection Survey data. Experimental Statistics.

Objective: To close gaps between research and clinical practice, tools are needed for efficient pragmatic trial recruitment and patient-reported outcome(PROM) collection. The objective was to assess feasibility and process measures for patient-reported outcome collection in a randomized trial comparing electronic health record(EHR) patient portal questionnaires to telephone interview among adults with epilepsy and anxiety or depression symptoms. Results: Participants were 60% women, 77% White/non-Hispanic, with mean age 42.5 years. Among 15 individuals randomized to EHR portal, 10(67%, CI 41.7-84.8%) met the 6-month retention endpoint, versus 100%(CI 79.6-100%) in the telephone group(p=0.04). EHR outcome collection at 6 months required 11.8 minutes less research staff time per participant than telephone (5.9, CI 3.3-7.7 vs. 17.7, CI 14.1-20.2). Subsequent telephone contact after unsuccessful EHR attempts enabled near complete data collection and still saved staff time. Discussion: Data from this randomized pilot study of pragmatic outcome collection methods for patients with anxiety or depression symptoms in epilepsy includes baseline participant characteristics, recruitment flow resulting from a novel EHR-based, care-embedded recruitment process, and data on retention along with various process measures at 6-months. Methods The dataset was collected via a combination of the following: 1. manual extraction of EHR-based data followed by entry into REDCap and then analysis and further processing in SAS 9.4; 2. Data pull of Epic EHR-based data from Clarity database using standard programming techniques, followed by processing in SAS 9.4 and merging with data from REDCap; 3. Collection of data directly from participants via telephone with entry into REDCap and further processing in SAS 9.4; 4. Collection of process measures from study team tracking records followed by entry into REDCap and further processing in SAS 9.4. One file in the dataset contains aggregate data generated following merging of Clarity data pull-origin dataset with a REDCap dataset and further manual processing. Recruitment for the randomized trial began at an epilepsy clinic visit, with EHR-embedded validated anxiety and depression instruments, followed by automated EHR-based research screening consent and eligibility assessment. Fully eligible individuals later completed telephone consent, enrollment and randomization. Thirty total participants were randomized 1:1 to EHR portal versus telephone outcome assessment, and patient-reported and process outcomes were collected at 3- and 6-months, with primary outcome 6-month retention in EHR arm(feasibility target: ≥11 participants retained). Variables in this dataset include recruitment flow diagram data, baseline participant sociodemographic and clinical characteristics, retention (successful PROM collection at 6 months), and process measures. The process measures included research staff time to collect outcomes, research staff time to collect outcomes and enter data, time from initial outcome collection reminder to outcome collection, and number of reminders sent to participants for outcome collection. PROMs were collected via the randomized method only at 3 months. At 6 months, if the criteria for retention was not met by the randomized method (failure to return outcomes by 1 week after 5 post-due date reminders for outcome collection), up to 3 additional attempts were made to collect outcomes by the alternative method, and process measures were also collected during this hybrid outcome collection method approach.

Malaria is the leading cause of death in the African region. Data mining can help extract valuable knowledge from available data in the healthcare sector. This makes it possible to train models to predict patient health faster than in clinical trials. Implementations of various machine learning algorithms such as K-Nearest Neighbors, Bayes Theorem, Logistic Regression, Support Vector Machines, and Multinomial Naïve Bayes (MNB), etc., has been applied to malaria datasets in public hospitals, but there are still limitations in modeling using the Naive Bayes multinomial algorithm. This study applies the MNB model to explore the relationship between 15 relevant attributes of public hospitals data. The goal is to examine how the dependency between attributes affects the performance of the classifier. MNB creates transparent and reliable graphical representation between attributes with the ability to predict new situations. The model (MNB) has 97% accuracy. It is concluded that this model outperforms the GNB classifier which has 100% accuracy and the RF which also has 100% accuracy. Methods Prior to collection of data, the researcher was be guided by all ethical training certification on data collection, right to confidentiality and privacy reserved called Institutional Review Board (IRB). Data was be collected from the manual archive of the Hospitals purposively selected using stratified sampling technique, transform the data to electronic form and store in MYSQL database called malaria. Each patient file was extracted and review for signs and symptoms of malaria then check for laboratory confirmation result from diagnosis. The data was be divided into two tables: the first table was called data1 which contain data for use in phase 1 of the classification, while the second table data2 which contains data for use in phase 2 of the classification. Data Source Collection Malaria incidence data set is obtained from Public hospitals from 2017 to 2021. These are the data used for modeling and analysis. Also, putting in mind the geographical location and socio-economic factors inclusive which are available for patients inhabiting those areas. Naive Bayes (Multinomial) is the model used to analyze the collected data for malaria disease prediction and grading accordingly. Data Preprocessing: Data preprocessing shall be done to remove noise and outlier. Transformation: The data shall be transformed from analog to electronic record. Data Partitioning The data which shall be collected will be divided into two portions; one portion of the data shall be extracted as a training set, while the other portion will be used for testing. The training portion shall be taken from a table stored in a database and will be called data which is training set1, while the training portion taking from another table store in a database is shall be called data which is training set2. The dataset was split into two parts: a sample containing 70% of the training data and 30% for the purpose of this research. Then, using MNB classification algorithms implemented in Python, the models were trained on the training sample. On the 30% remaining data, the resulting models were tested, and the results were compared with the other Machine Learning models using the standard metrics. Classification and prediction: Base on the nature of variable in the dataset, this study will use Naïve Bayes (Multinomial) classification techniques; Classification phase 1 and Classification phase 2. The operation of the framework is illustrated as follows: i. Data collection and preprocessing shall be done. ii. Preprocess data shall be stored in a training set 1 and training set 2. These datasets shall be used during classification. iii. Test data set is shall be stored in database test data set. iv. Part of the test data set must be compared for classification using classifier 1 and the remaining part must be classified with classifier 2 as follows: Classifier phase 1: It classify into positive or negative classes. If the patient is having malaria, then the patient is classified as positive (P), while a patient is classified as negative (N) if the patient does not have malaria.
Classifier phase 2: It classify only data set that has been classified as positive by classifier 1, and then further classify them into complicated and uncomplicated class label. The classifier will also capture data on environmental factors, genetics, gender and age, cultural and socio-economic variables. The system will be designed such that the core parameters as a determining factor should supply their value.

The original COVID-19 dataset included information about tested patients, containing early-stage symptoms, comorbidities, demographics information, and symptoms description. The patients were tested by applying viral or rapid tests. The raw data was collected by the public health agency of the city of Campina Grande, Paraíba state, in Northeast Brazil. Such a public agency is informed by all the COVID-19 exams performed in the city of Campina Grande. The health agency employees removed patient identification, and the data made available were reused to enable this study.

We preprocessed the dataset by selecting only completed tests, being marked as positive or negative, applied string matching algorithms to correct some inconsistencies, and removed rows with duplicated instances and asymptomatic patients. We also focused on the most frequent and relevant demographics information and reported early-stage symptoms to select features, and balanced the data considering positive and negative cases by random undersampling using the NearMiss algorithm. We also use unbalanced datasets.

Using this dataset, we implemented and evaluated supervised machine learning models for COVID-19 detection in Brazil based on early-stage symptoms and basic personal information.

This dataset relates to the study entitled "Machine Learning Classification Models for COVID-19 Test Prioritization in Brazil".

1) Data introduction • Animal-disease dataset aims to develop a predictive model that determines whether an animal's condition is dangerous based on five distinct symptoms.

2) Data utilization (1)Animal-disease data has characteristics that: • This is a data set that indicates the type of animals such as dogs and cats, the degree of symptoms from 1 to 5, and includes whether the condition is dangerous. (2)Animal-disease data can be used to: • Animal health monitoring: Data supports the development of monitoring systems that track the health of animals over time and provide warnings of potentially hazardous situations. • Veterinary diagnostics: Using this data, veterinarians can develop tools to detect critical conditions in animals early and enable timely and effective therapeutic interventions.

This dataset contains the predicted prices of SX Network for the upcoming years based on user-defined projections.

310 Observations, 13 Attributes (12 Numeric Predictors, 1 Binary Class Attribute - No Demographics)

Lower back pain can be caused by a variety of problems with any parts of the complex, interconnected network of spinal muscles, nerves, bones, discs or tendons in the lumbar spine. Typical sources of low back pain include:

The large nerve roots in the low back that go to the legs may be irritated The smaller nerves that supply the low back may be irritated The large paired lower back muscles (erector spinae) may be strained The bones, ligaments or joints may be damaged An intervertebral disc may be degenerating An irritation or problem with any of these structures can cause lower back pain and/or pain that radiates or is referred to other parts of the body. Many lower back problems also cause back muscle spasms, which don't sound like much but can cause severe pain and disability.

While lower back pain is extremely common, the symptoms and severity of lower back pain vary greatly. A simple lower back muscle strain might be excruciating enough to necessitate an emergency room visit, while a degenerating disc might cause only mild, intermittent discomfort.

This data set is about to identify a person is abnormal or normal using collected physical spine details/data.

Note: P values are associated with Chi-square tests.aSD is standard deviation.bP value is associated with analysis of variance.cP value is associated with Cochran-Mantel-Haenszel statistics.

.SX.CN Whois Database, discover comprehensive ownership details, registration dates, and more for .SX.CN TLD with Whois Data Center.

This dataset contains symptoms and disease information. It contains total of 1325 symptoms covered with 391 disease.This dataset is refernced from website MedLinePlus. This dataset have training and testing dataset and can be used to train disease prediction algorithm . It is created on own for project disease prediction and do not involves any funding or promotional terms.

Three datasets described in manuscript are preprocessed data (imputed). One meta-data file for all (variables are the same), and R codes used for analyses can be found here. If you want to replicate main analyses of the findings, you can use the R script in 'Analyses_EMA_Data.html' and two pre-processed datasets: 'dfimp_smokers.csv' and 'dfimp_nonsmokers.csv'. The follow-up analyses were done on dataset dfimp_smokerswith.csv'. Please see 'Follow_up_Analyses_EMA_Data.html'. This should enable replication. R scripts from 'Preparation_descriptives_EMA_Data.html' were preparatory steps with some descriptive statistics on non-anonymized dataset that is not shared, this does provide some insight into construction and presented descriptives. Link to same and other materials is: https://doi.org/10.17605/OSF.IO/J2WQK