The Dow Jones Industrial Average (DJIA) index dropped around ***** points in the four weeks from February 12 to March 11, 2020, but has since recovered and peaked at ********* points as of November 24, 2024. In February 2020 - just prior to the global coronavirus (COVID-19) pandemic, the DJIA index stood at a little over ****** points. U.S. markets suffer as virus spreads The COVID-19 pandemic triggered a turbulent period for stock markets – the S&P 500 and Nasdaq Composite also recorded dramatic drops. At the start of February, some analysts remained optimistic that the outbreak would ease. However, the increased spread of the virus started to hit investor confidence, prompting a record plunge in the stock markets. The Dow dropped by more than ***** points in the week from February 21 to February 28, which was a fall of **** percent – its worst percentage loss in a week since October 2008. Stock markets offer valuable economic insights The Dow Jones Industrial Average is a stock market index that monitors the share prices of the 30 largest companies in the United States. By studying the performance of the listed companies, analysts can gauge the strength of the domestic economy. If investors are confident in a company’s future, they will buy its stocks. The uncertainty of the coronavirus sparked fears of an economic crisis, and many traders decided that investment during the pandemic was too risky.

The value of the DJIA index amounted to ****** at the end of June 2025, up from ********* at the end of March 2020. Global panic about the coronavirus epidemic caused the drop in March 2020, which was the worst drop since the collapse of Lehman Brothers in 2008. Dow Jones Industrial Average index – additional information The Dow Jones Industrial Average index is a price-weighted average of 30 of the largest American publicly traded companies on New York Stock Exchange and NASDAQ, and includes companies like Goldman Sachs, IBM and Walt Disney. This index is considered to be a barometer of the state of the American economy. DJIA index was created in 1986 by Charles Dow. Along with the NASDAQ 100 and S&P 500 indices, it is amongst the most well-known and used stock indexes in the world. The year that the 2018 financial crisis unfolded was one of the worst years of the Dow. It was also in 2008 that some of the largest ever recorded losses of the Dow Jones Index based on single-day points were registered. On September 29, 2008, for instance, the Dow had a loss of ****** points, one of the largest single-day losses of all times. The best years in the history of the index still are 1915, when the index value increased by ***** percent in one year, and 1933, year when the index registered a growth of ***** percent.

Cash-and-Short-Term-Investments Time Series for iRhythm Technologies Inc. iRhythm Technologies, Inc., a digital healthcare company, engages in the design, development, and commercialization of device-based technology that provides ambulatory cardiac monitoring services to diagnose arrhythmias in the United States. The company offers Zio ambulatory cardiac monitoring services, including long-term and short-term continuous monitoring and mobile cardiac telemetry monitoring services. It also provides Zio Monitor System, a prescription-only remote electrocardiogram (ECG) monitoring system consisting a patch ECG monitor that records the electric signal from the heart continuously for up to 14 days; Zio XT System, a prescription-only remote ECG monitoring system that consists of the Zio XT patch that records the electric signal from the heart continuously for up to 14 days; and Zio ECG Utilization Software System, which supports the capture and analysis of ECG data. In addition, the company offers Zio AT System, a prescription-only remote ECG monitoring system, which consists of the Zio AT patch that records the electric signal from the heart continuously for up to 14 days, as well as incorporates the Zio AT wireless gateway to provide connectivity between the patch and the ZEUS System during the patient wear period. It has a development collaboration agreement with Verily Life Sciences LLC and Verity Ireland Limited to develop various next-generation atrial fibrillation screening, detection, or monitoring products. iRhythm Technologies, Inc. was incorporated in 2006 and is headquartered in San Francisco, California.

Graph and download economic data for Number of Commercial Paper Issues with a Maturity Between 1 and 4 Days (MKT14MKTVOL) from 2001-01-02 to 2025-07-29 about 1-4 days, issues, commercial paper, maturity, commercial, and USA.

Capital-Expenditures Time Series for iRhythm Technologies Inc. iRhythm Technologies, Inc., a digital healthcare company, engages in the design, development, and commercialization of device-based technology that provides ambulatory cardiac monitoring services to diagnose arrhythmias in the United States. The company offers Zio ambulatory cardiac monitoring services, including long-term and short-term continuous monitoring and mobile cardiac telemetry monitoring services. It also provides Zio Monitor System, a prescription-only remote electrocardiogram (ECG) monitoring system consisting a patch ECG monitor that records the electric signal from the heart continuously for up to 14 days; Zio XT System, a prescription-only remote ECG monitoring system that consists of the Zio XT patch that records the electric signal from the heart continuously for up to 14 days; and Zio ECG Utilization Software System, which supports the capture and analysis of ECG data. In addition, the company offers Zio AT System, a prescription-only remote ECG monitoring system, which consists of the Zio AT patch that records the electric signal from the heart continuously for up to 14 days, as well as incorporates the Zio AT wireless gateway to provide connectivity between the patch and the ZEUS System during the patient wear period. It has a development collaboration agreement with Verily Life Sciences LLC and Verity Ireland Limited to develop various next-generation atrial fibrillation screening, detection, or monitoring products. iRhythm Technologies, Inc. was incorporated in 2006 and is headquartered in San Francisco, California.

14-Day Reverse Repo Rate in China remained unchanged at 1.65 percent in June. This dataset includes a chart with historical data for China 14-Day Reverse Repo Rate.

Sri Lanka's main stock market index, the ASPI, rose to 19914 points on August 1, 2025, gaining 1.38% from the previous session. Over the past month, the index has climbed 9.77% and is up 74.04% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Sri Lanka. Sri Lanka Stock Market (CSE All Share) - values, historical data, forecasts and news - updated on August of 2025.

On March 10, 2023, the Johns Hopkins Coronavirus Resource Center ceased its collecting and reporting of global COVID-19 data. For updated cases, deaths, and vaccine data please visit: World Health Organization (WHO)For more information, visit the Johns Hopkins Coronavirus Resource Center.COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.DOI: https://doi.org/10.6084/m9.figshare.125529863/7/2022 - Adjusted the rate of active cases calculation in the U.S. to reflect the rates of serious and severe cases due nearly completely dominant Omicron variant.6/24/2020 - Expanded Case Rates discussion to include fix on 6/23 for calculating active cases.6/22/2020 - Added Executive Summary and Subsequent Outbreaks sectionsRevisions on 6/10/2020 based on updated CDC reporting. This affects the estimate of active cases by revising the average duration of cases with hospital stays downward from 30 days to 25 days. The result shifted 76 U.S. counties out of Epidemic to Spreading trend and no change for national level trends.Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Correction on 6/1/2020Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Revisions added on 4/30/2020 are highlighted.Revisions added on 4/23/2020 are highlighted.Executive SummaryCOVID-19 Trends is a methodology for characterizing the current trend for places during the COVID-19 global pandemic. Each day we assign one of five trends: Emergent, Spreading, Epidemic, Controlled, or End Stage to geographic areas to geographic areas based on the number of new cases, the number of active cases, the total population, and an algorithm (described below) that contextualize the most recent fourteen days with the overall COVID-19 case history. Currently we analyze the countries of the world and the U.S. Counties. The purpose is to give policymakers, citizens, and analysts a fact-based data driven sense for the direction each place is currently going. When a place has the initial cases, they are assigned Emergent, and if that place controls the rate of new cases, they can move directly to Controlled, and even to End Stage in a short time. However, if the reporting or measures to curtail spread are not adequate and significant numbers of new cases continue, they are assigned to Spreading, and in cases where the spread is clearly uncontrolled, Epidemic trend.We analyze the data reported by Johns Hopkins University to produce the trends, and we report the rates of cases, spikes of new cases, the number of days since the last reported case, and number of deaths. We also make adjustments to the assignments based on population so rural areas are not assigned trends based solely on case rates, which can be quite high relative to local populations.Two key factors are not consistently known or available and should be taken into consideration with the assigned trend. First is the amount of resources, e.g., hospital beds, physicians, etc.that are currently available in each area. Second is the number of recoveries, which are often not tested or reported. On the latter, we provide a probable number of active cases based on CDC guidance for the typical duration of mild to severe cases.Reasons for undertaking this work in March of 2020:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-25 days + 5% from past 26-49 days - total deaths. On 3/17/2022, the U.S. calculation was adjusted to: Active Cases = 100% of new cases in past 14 days + 6% from past 15-25 days + 3% from past 26-49 days - total deaths. Sources: https://www.cdc.gov/mmwr/volumes/71/wr/mm7104e4.htm https://covid.cdc.gov/covid-data-tracker/#variant-proportions If a new variant arrives and appears to cause higher rates of serious cases, we will roll back this adjustment. We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source

Graph and download economic data for Total Value of Issues, with a Maturity Between 1 and 4 Days, Used in Calculating the AA Asset-Backed Commercial Paper Rates (AB14AAAMT) from 2001-01-02 to 2025-07-11 about 1-4 days, asset-backed, used, AA, issues, commercial paper, maturity, commercial, rate, and USA.

Quantifying the influence of space on social group structure

Series of networks based on individual connections collected from a population of free-ranging house mice (Mus musculus domesticus). Mice are tagged with a passive integrated transponder (PIT) when they reach a minimum weight of 18 grams. The mice use 40 artificial nest boxes, fitted with radio-frequency identification (RFID) antennae, to rest and rear litters. The antennae automatically record when mice equipped with a PIT enter and exit a box. Based on these antennae data, we can determine which individuals share nest boxes and for how long. (For further information on the study system refer to König et al. 2015).

The series of networks is constructed based on the sharing of nest boxes. The series of networks consists of 14 days of antennae data over the duration of 2 years (population size during this time period ranged between 52 to 188 tagged adult house mice). Inactivity periods of the data collection system extended this time window, so that each time window consists of a similar period of active data collection (see also Liechti et al. 2020). We used total time spent sharing a nest box in seconds as our measure of association strength.

Authors:

- Julian Evans^1†*,
- Jonas I. Liechti^2†,
- Matthew J. Silk^3,4,
- Barbara König^1‡,
- Sebastian Bonhoeffer^2‡

^†: Joint first author; ^‡: Joint last author; ^*: Corresponding author: jevansbio@gmail.com

1: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, 8057 Zurich
2: Institute for Integrative Biology, ETH Zurich, Universitätsstrasse 16, 8092 Zurich, Switzerland
3: Centre for Ecology and Conservation, University of Exeter Penryn Campus, UK
4: Environment and Sustainability Institute, University of Exeter Penryn Campus, UK

Dataset
The raw data of the studied system is present in `graphs` folder in form of a
list of [GraphML](http://graphml.graphdrawing.org/) files.
The files containing the graphs are numbered according to their order of
appearance in the sequence of graphs.

Each file contains a single graph the can be imported for example into
[igraph](https://igraph.org/r/doc/).

The following attributes are stored in the graphml files:

Graph properties:

- start (xml tag: g_start) gives the starting timepoint of
aggregation period. The format is `'YYYY-MM-DD HH:MM:SS'`.
- stop (xml tag: g_stop) gives the ending timepoint of the
aggregation period.

Node properties:

- name (xml tag: v_name) a unique id for each individual that is consistent
across the sequence of graphs.
- x position( xml tag: v_x) is the x position [cm] of the barycentre from
all interactions of this individual.
- y position( xml tag: v_y) is the x position [cm] of the barycentre from
all interactions of this individual.

Edge properties:

- weight (xml tag: e_weight) is the weight [seconds] of an interaction.
The weight corresponds to the accumulated duration of interactions.

SCENIR - ICML-2025 - Preprocessed Dataset

This dataset is a preprocessed and refined version of the PSG dataset, specifically prepared for the research presented in our paper, "SCENIR: Visual Semantic Clarity through Unsupervised Scene Graph Retrieval". It aims to provide a ready-to-use resource for training our SCENIR model on semantic image retrieval using scene graphs.

  📊 Dataset Structure

The dataset contains 3 files:

final_train_graphs.pkl: 11054 scene graphs for… See the full description on the dataset page: https://huggingface.co/datasets/Nick2364/SCENIR-ICML2025-PSG.

The GraphCast Global Forecast System (GraphCastGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The horizontal resolution is a 0.25 degree latitude-longitude grid (about 28 km). The model runs 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, specific humidity, and vertical velocity, are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.

The GraphCastGFS system is an experimental weather forecast model built upon the pre-trained Google DeepMind’s GraphCast Machine Learning Weather Prediction (MLWP) model. The GraphCast model is implemented as a message-passing graph neural network (GNN) architecture with “encoder-processor-decoder” configuration. It uses an icosahedron grid with multiscale edges and has around 37 million parameters. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The GraphCastGFSl takes two model states as initial conditions (current and 6-hr previous states) from NCEP 0.25 degree GDAS analysis data and runs GraphCast (37 levels) and GraphCast_operational (13 levels) with a pre-trained model provided by GraphCast. Unit conversion to the GDAS data is conducted to match the input data required by GraphCast and to generate forecast products consistent with GFS from GraphCastGFS’ native forecast data.

The GraphCastGFS version 2 made the following changes from the GraphcastCastGFS version 1.

1. The 37 vertical levels model is removed due to the storage restriction and limited accuracy.
   
2. The 13 levels graphcast ML model was fine-tuned with NCEP’s GDAS data as inputs and ECMWF ERA5 data as ground truth from 20210323 to 20220901, validated from 20220901 to 20230101. Evaluation is done with forecasts from 20230101-20240101. The new weights created from the training are used to create global forecasts. It is important to note that the GraphCastGFS v1 model weights obtained from Google’s DeepMInd were provided based on 12 timesteps training with ERA5 data, while the GraphCastGFS v2 model weights resulted from training with 14 timesteps with GDAS and ERA5 data that significantly increased the accuracy of the forecasts compared with GraphCastGFS V1.
   
   The input data generated from the GDAS data as GraphCast input is provided under input/ directory. An example of file names is shown below
   
   source-gdas_date-2024022000_res-0.25_levels-13_steps-2.nc
   
   The files are under forecasts_13_levels/. There are 40 files under each directory covering a 10 day forecast. An example of file name is listed below
   
   graphcastgfs.t00z.pgrb2.0p25.f006
   
   

1. Starting from 06 cycle on 20240710, the forecast length is increased from 10 days to 16 days.
   
   Please note that this NOAA GraphCastGFS Model was produced using a code package released by Google DeepMind. For information on Google DeepMind, please visit their github page listed in the documentation and license sections of this page.

HLO Feature Dataset for Deep Learning Resource Estimation

  Dataset Summary

The HLO Feature Dataset is a collection of compiler-level graph features (HLO graphs) extracted from deep learning training workloads. Alongside detailed metadata (model configs, GPU stats), this dataset enables machine learning approaches for:

⏱️ Training Time Prediction 📉 Resource Consumption Estimation ⚡ HPC and GPU Scheduling Optimization 🧩 Graph-based Neural Architecture Analysis

This… See the full description on the dataset page: https://huggingface.co/datasets/ICICLE-AI/ResourceEstimation_HLOGenCNN.

Australia's main stock market index, the ASX200, fell to 8662 points on August 1, 2025, losing 0.92% from the previous session. Over the past month, the index has climbed 0.75% and is up 9.05% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Australia. Australia Stock Market Index - values, historical data, forecasts and news - updated on August of 2025.

Current price of Pork Belly Composite. Daily U.S. Pork Cuts prices per pound, based on negotiated prices and volume of boxed pork cuts delivered within 0-14 days and on average industry cutting yields.

Graph and download economic data for Large Bank Consumer Mortgage Balances: 90 or More Days Past Due by Property Type: Accounts Based: 2-4 Units (RCMFLBACTDPDPCT90PPROP3) from Q3 2012 to Q1 2025 about 2-4 unit structures, 90 days +, accounts, FR Y-14M, large, balance, mortgage, consumer, banks, depository institutions, and USA.

Graph and download economic data for Large Bank Consumer Mortgage Balances: 60 or More Days Past Due by Property Type: Balances Based: Townhouse / Planned (RCMFLBBALDPDPCT60PPROP4) from Q3 2012 to Q1 2025 about 60 days +, FR Y-14M, large, balance, mortgage, consumer, banks, depository institutions, and USA.

Current price of Pork Cutout. Daily U.S. Pork Cuts prices per pound, based on negotiated prices and volume of boxed pork cuts delivered within 0-14 days and on average industry cutting yields.