Clinical Trials: Before, During, and After the PandemicResearch Question How did the COVID-19 pandemic reshape the likelihood that an interventional clinical trial registered on ClinicalTrials.gov would both begin and reach completion? More precisely, I track how yearly attempts (new trial registrations), successes (completed trials with posted results), and failures (terminated or withdrawn trials) shifted across three windows: pre-pandemic (2015-2019), pandemic onset (2020-2021), and early-recovery (2022-2025). I layered in sponsor type, participant age, and other study-design details, and I aim to reveal which corners of the research pipeline proved resilient and which still show scarring. Data & Key Variables The analysis draws on the Aggregate Analysis of ClinicalTrials.gov (AACT) relational database, a public mirror of every record on ClinicalTrials.gov maintained by the Clinical Trials Transformation Initiative and refreshed daily. I worked with the static snapshot dated 1 April 2025 from the same date in 2015. Core tables and fields include: Table → FieldWhat it representsHow I use itstudies.first_posted_dateDate the trial was first registeredCounts attempts per yearstudies.overall_statusCurrent status (Completed, Terminated, Withdrawn, etc.)Flags success vs. failurestudies.results_first_posted_dateDate results were postedVerifies that a “Completed” study reported outcomessponsors.nameLead sponsor (industry, academic, hospital, etc.)Breaks out trends by top sponsorseligibilities.minimum_age / maximum_ageAge range of eligi […] “Clinical Trials: Before, During, and After the Pandemic”

Keeping my Cool: A Month‑long View of My Air‑Conditioner Habits I began this project with a deceptively simple question: how often do I press the power button on my window air‑conditioner and what specific yet subjective personal or environmental cues persuade me to do so? Almost thirty spring days of meticulous logging every interaction between myself and my A/C between 28 March and 22 April 2025 provided the raw material for me to explore just how complex this simple question was by making three visualizations. Before interpreting the three following visualizations, readers need to understand the method of collection. I collected the data manually over seven consecutive days (28 March to 3 April 2025). Every single press of the ON/OFF button became a row in a Google Sheet, where I captured the exact date and time, whether the action turned the unit on or off, the fan speed or cooling mode chosen when the unit was activated, the indoor temperature read from a living‑room thermometer, the outdoor temperature taken from a local neighborhood weather app online, and the subjective reason I chose was from an eight‑item list that ranged from “Humidity” to “Too Cold.” I also grouped the timestamps into morning, afternoon, and evening blocks so that patterns by time of day would be simple to spot. var divElement = document.getElementById(‘viz1745380683996’); var vizElement = divElement.getElementsByTagName(‘object’)[0]; vizElement.style.width=’100%’;vizElement.style.height=(divElement.offsetWidth*0.75)+’px’; var scriptElement = document.createElement(‘script’); scriptElement.src = ‘https://public.tableau.com/javascripts/api/viz_v1.js’; vizElement.parentNode.insertBefore(scriptElement, vizElement); This bubble chart is the easiest way to introduce the data while also illustrating how vast the dataset is without overwhelming the reader with too many variables, although they are lurking beneath the visualization. The next visualization is the pie chart, which really flexes how much data we have here – this pie chart is actually animated and has four groupings for users to start to digest – this is the main part of the dataset that we “created” in a sense, the groups were decided and you could always say that it was arbitrarily done. But, “Afternoon” as 12-5pm, “Evening” as 6-9pm, “Late Night” as 10-11pm, and “Morning” as 12am-11pm makes sense because I am usually asleep during most of the “Morning.” I also think we are basically testing bubbles against pie charts here, but I also wanted to add the wrinkle of having four pie charts essentially through the animation. var divElement = document.getElementById(‘viz1745380437845’); var vizElement = divElement.getElementsByTagName(‘object’)[0]; vizElement.style.width=’100%’;vizElement.style.height=(divElement.offsetWidth*0.75)+’px’; var scriptElement = document.createElement(‘script’); scriptElement.src = ‘https://public.tableau.com/javascripts/api/viz_v1.js’; vizElement.parentNode.insertBefore(scriptElement, vizElement); The pie chart also shows you the human element to all of this in a colorful way, which is that the “Sleep” reason goes up during the Late Night grouping, which also tells you what time I go to sleep. The fact that I do not always or even a majority of the time use the sleep reason to turn off the A/C at night shows you that I do in fact sleep with it on, which is not recommended. var divElement = document.getElementById(‘viz1745380450314’); var vizElement = divElement.getElementsByTagName(‘object’)[0]; vizElement.style.width=’100%’;vizElement.style.height=(divElement.offsetWidth*0.75)+’px’; var scriptElement = document.createElement(‘script’); scriptElement.src = ‘https://public.tableau.com/javascripts/api/viz_v1.js’; vizElement.parentNode.insertBefore(scriptElement, vizElement); The bar chart towers at 26 toggles between 10 p.m. and 5 a.m., nearly half of the week’s total. I’m a light sleeper, and even a slight temperature drift or street noise makes me reach for the switch almost as a reflex. I also like the bar chart as the summary here because again, just seeing the grouping as separate pie charts does not allow you to compare the groupings, but then when we have the bar charts, that is the immediate clearest thing. We also did not show this in the bubble chart in the beginning so the user can decide which simple visualization impacted them more, the animated ones, or the simple ones. Even so, for the reader and myself, the exercise has practical value. I now know that a quieter fan mode might curb my noise‑inspired toggling, a small dehumidifier run overnight could reduce how many times I press it in the morning, and a smart plug could be used to automatically shut power between noon and five o’clock, which would eliminate wasted electricity. If I extend the study, I will automate data collection with a connected switch, pair it with continuous temperature and humidity sensors, and look at hourly electricity rates to see what the economics are behind these on/off decisions, since my background is in economics. Other researchers could expand the scope to multiple apartments or seasons, but for me, I think the overall message is pretty simple, which is that by paying attention to the tiny habitual decisions I make each day, I have uncovered real ways to save energy and improve everyday comfort and the evidence is sprinkled thr […] “Keeping my Cool: A Month‑long View of My Air‑Conditioner Habits”

Data Visualization Project 1 by Rohan RamnarainMapping New York’s Barks: Tracking NYC Dog-Barking Complaints (2010–2025) Here is our research question for this project and visualization: How did dog-barking noise complaints filed with NYC’s 311 service (specifically those labeled “Noise, Barking Dog (NR5)”) evolve between 2010 and 2025 in the five boroughs and were there noticeable shifts before, during, and after the height of the COVID-19 pandemic? The data originate from the NYC 311 Service Requests dataset, encompassing 2010 to 2025. I filtered specifically for complaints whose Descriptor field is “Noise, Barking Dog (NR5),” ensuring only barking‐dog incidents are included. Key variables include: the “Created Date”, which is the timestamp of each complaint (year, month, day, and time), the common “Descriptor (Noise, Barking Dog (NR5))” which is our only complaint being studied, and the “Borough”, “Latitude”, and “Longitude” for each of these complaints. One limitation is that 311 data capture reported noise disturbances only and this of course masks the true incidence of barking, which might be higher, especially when you consider that against the backdrop of the COVID-19 pandemic, which could have added another layer of obfuscation and skewed the data in either direction, but most likely upwards – i.e, people spending more time at home may have noticed barking more often. But I digress… let’s take a look at these visualizations to get a feel for the data and the underlying trends that may help us answer our research question. Graduated Symbol Map (2010–2025) This map uses circles of varying size to represent the volume of barking-dog complaints within each area over the entire 2010–2025 span. Large circles indicate more complaints and the smaller circles indicate fewer complaints – but note that space and population density must be considered when looking at bubble sizes. The key thing that jumps out at you on first glance is how Brooklyn and Queens host numerous large circles, suggesting consistently high complaint volumes in certain neighborhoods. Manhattan shows many medium-sized circles in a smaller physical area, which shows how dense housing can amplify noise disturbances. Choropleth Map This choropleth map shades neighborhoods by the total count of “Noise, Barking Dog (NR5)” complaints from 2010–2025 with darker shades corresponding to higher complaint frequencies, and areas in deep blue or green shades being shown as hotspots for barking complaints. This helps pinpoint neighborhoods with persistent or widespread issues, potentially guiding targeted noise policy or outreach efforts. Everything is summed for that particular zipcode – that is how this map differs from the graduated symbol aside from aesthetics. This map is my ideal map for analyzing the dataset because I can spot my own zipcode and the zipcodes of my family members who may or may not own dogs and therefore may be the ideal audience for this particular visualization. The soft colors are also a plus in immediately identifying the areas of extreme complaint amounts. Street Map with Colored Clusters Here, each complaint is plotted as a distinct dot, color-coded by borough, which is a more granular visualization of exactly where—and how densely—barking complaints occur and density is the key to this visualization because the dense patches of orange in Brooklyn and teal in Queens reveal high concentrations of dog-barking complaints. Manhattan (red) is more compact but still dotted extensively, reflecting high population density and possibly greater sensitivity to noise as we saw in the first visualization. This has the added benefit of including commonly known and lived on streets, parks, and landmarks throughout the city. Out of all the visualizations, this one is perhaps the most inviting and the most interactive – I actually went and spotted the complaints right on the street outside my house, and the audience can do the same – this is also the most immediately related visualization. Heat Map on Street Map This final map applies a heat layer over the city, highlighting areas of the highest complaint density in darker or more intense “hot zones.”, a way to focus on the intense regions down to the local level in neighborhoods like Williamsburg, Greenpoint, and western Queens – these mirror the clusters we saw in previous maps. The heat map on the street level perhaps underscores how closely packed housing correlates with higher complaint density. As we have seen, most of these “Noise, Barking Dog (NR5)” complaints are concentrated in Brooklyn and Queens, with certain Manhattan neighborhoods also showing high densities and the Bronx and Staten Island being the boroughs with the least complaints. The next steps would be normalization of each zipcode and each region against the population density of the area, as well as the number of parks nearby, and the distance to the nearest park for each complaint, as a way to test that as a correlating variable. We also need a time-series based way of looking at this, perhaps through animations through the years in the dataset – 2010 to 2025. The final purpose of this data visualization would be to motivate politicians and local community boards to address the dog complaint […] “Data Visualization Project 1 by Rohan Ramnarain”