DataHammer/scimrc huggingface dataset:
Question: How to investigate personal recovery before?
Evidence: Mental health research can benefit increasingly fruitfully from computational linguistics methods, given the abundant availability of language data in the internet and advances of computational tools. This interdisciplinary project will collect and analyse social media data of individuals diagnosed with bipolar disorder with regard to their recovery experiences. Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries. Complementary to this evidence, computational linguistic methods allow us to analyse first-person accounts shared online in large quantities, representing unstructured settings and a more heterogeneous, multilingual population, to draw a more complete picture of the aspects and mechanisms of personal recovery in bipolar disorder.
Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries.
Answer: Personal recovery - living a satisfying and contributing life along symptoms of severe mental health issues - so far has only been investigated qualitatively with structured interviews and quantitatively with standardised questionnaires with mainly English-speaking participants in Western countries.
Question: What are the author's steps for converting pathology reports into feature vectors?
Evidence: We assembled a dataset of 1,949 cleaned pathology reports. Each report is associated with one of the 37 different primary diagnoses based on IDC-O codes. The reports are collected from four different body parts or primary sites from multiple patients. The distribution of reports across different primary diagnoses and primary sites is reported in tab:report-distribution. The dataset was developed in three steps as follows.
Collecting pathology reports: The total of 11,112 pathology reports were downloaded from NCI's Genomic Data Commons (GDC) dataset in PDF format BIBREF9 . Out of all PDF files, 1,949 reports were selected across multiple patients from four specific primary sites—thymus, testis, lung, and kidney. The selection was primarily made based on the quality of PDF files.
Cleaning reports: The next step was to extract the text content from these reports. Due to the significant time expense of manually re-typing all the pathology reports, we developed a new strategy to prepare our dataset. We applied an Optical Character Recognition (OCR) software to convert the PDF reports to text files. Then, we manually inspected all generated text files to fix any grammar/spelling issues and irrelevant characters as an artefact produced by the OCR system.
Splitting into training-testing data: We split the cleaned reports into 70% and 30% for training and testing, respectively. This split resulted in 1,364 training, and 585 testing reports.
We performed the following steps to transform a pathology report into a feature vector: 1) Create a set of vocabulary containing all unique words from all the pre-processed training reports.  2) Create a zero vector fr of the same length as the vocabulary. 3) For each word t in a report r, set the corresponding index in fr to T F − IDF(t, r). 4) The resultant fr is a feature vector for the report r and it is a highly sparse vector.
Answer: 1) Create a set of vocabulary containing all unique words from all the pre-processed training reports. 2) Create a zero vector fr of the same length as the vocabulary. 3) For each word t in a report r, set the corresponding index in fr to T F − IDF(t, r). 4) The resultant fr is a feature vector for the report r and it is a highly sparse vector.
Question: What is T20 cricket?
Answer: The T20 cricket is one of the three officially recognised formats of cricket game. In this format, each inning consists of 20 overs, i.e, a total of 120 legal ball deliveries. It is the shortest form of cricket among all the three formats.
Question: What was Canon EOS DCS 1 resolution?
Evidence: The Canon EOS DCS 1 was Kodak's third Canon-based Digital SLR camera (a rebranded Kodak EOS DCS-1). It was released in December 1995, following the cheaper EOS DCS 3, which was released earlier that year. Like that camera, it combined an EOS-1N body with a modified Kodak DCS 460 digital back. Despite offering a then-enormous resolution of 6 megapixels with a relatively large APS-H sensor, a number of technical issues (together with its 3.6 million yen price) meant that it was never a very popular camera other than for a few people with specialized roles.

Although the sensor was much larger than the EOS DCS 3, the DCS 1 had a lower fixed sensitivity of ISO 80. The large image size resulted in a burst rate of just over one image per second for two images, followed by an eight-second delay to clear the buffer. A typical contemporary 340MB PCMCIA card or IBM Microdrive could store 53 images. In line with the rest of the Kodak DCS range, the EOS DCS 1 could not produce JPEG files in camera.

The EOS DCS 1 was succeeded in 1998 by the EOS D6000 (a rebranded Kodak DCS 560).
Answer: The Canon EOS DCS 1 had a resolution of 6 megapixels, which at that time of release was considered to be a breakthrough in technology.
Question: Who is Vincent Paterson?
Evidence: Vincent Paterson (born May 4, 1950) is an American director and choreographer who has had an expansive career in many parts of the entertainment industry including film, Broadway, concert tours, opera, music videos, television and commercials. He has achieved success worldwide as a director and choreographer.
Answer: Vincent Paterson (born May 4, 1950) is an American director and choreographer who has had an expansive career in many parts of the entertainment industry including film, Broadway, concert tours, opera, music videos, television and commercials. He has achieved success worldwide as a director and choreographer.
Paterson collaborated with Michael Jackson, George Harrison and Madonna on various projects, including directing and choreographing Madonna's Blond Ambition Tour, the centerpiece for the documentary Madonna: Truth or Dare (also known as In Bed with Madonna outside of North America). He created Madonna's "Marie Antoinette/Vogue" performance for the MTV Awards and her appearance on the Academy Awards. He also choreographed Madonna in the film Evita (1996). For Michael Jackson, he is one of the two fighter dancers in "Beat It" videoclip, and was assistant choreographer and a zombie dancer in "Thriller". He co-directed and co-choreographed Jackson's Bad tour as well as co-choreographing Jackson music videos "Smooth Criminal", "The Way You Make Me Feel", "Dirty Diana", "Speed Demon", "Black or White" and live performances for the Super Bowl XXVII Halftime show, the Grammys, and the MTV 10th Anniversary. He also co-directed and co-choreographed the music video for Jackson's hit "Blood on the Dance Floor".
Question: Can you please explain the offside rule in Soccer?
Answer: Soccer is a sport played between two teams with 11 players each. Out of the 11 players one is a goalkeeper. The offside rule is fundamental in soccer to avoid players from gaining unfair advantage over the opposition. A player is in an offside position if they are closer to the opposition's goal line than the last defender of the opposition team. 

The law states that a player is in an offside position if any of their body parts, except the hands and arms, are in the opponents' half of the pitch, and closer to the opponents' goal line than both the ball and the second-last defender.

Being in an offside position is not an offence in itself, but a player so positioned when the ball is played by a teammate can be judged guilty of an offside offence if they receive the ball or will otherwise become "involved in active play", will "interfere with an opponent", or will "gain an advantage" by being in that position. Offside is often considered one of the most difficult to understand aspects of the sport.
Question: Why was the show Curb Your Enthusiasm so popular?
Answer: Curb Your Enthusiasm was popular for its unique brand of humor and for its ability to capture the nuances of everyday life. The show was also well-received for its depiction of a wide range of different characters and situations, from the struggles of a struggling actor to the dysfunctional relationships of a married couple. The show also offered a unique take on the idea of success, as Larry David often explored the idea of what it means to achieve success in the modern world.
Question: What is your favorite ice cream flavor?
Answer: While everyone has different taste buds, there are many different flavors to enjoy.  Most of the basic flavors are used as a base for other flavors.  The most common being, chocolate, vanilla and strawberry.
Question: What does the sentiment analyzer base on?
Evidence: The most well-known publication in this area is by Bollen BIBREF10 . They investigated whether the collective mood states of public (Happy, calm, Anxiety) derived from twitter feeds are correlated to the value of the Dow Jones Industrial Index. They used a Fuzzy neural network for their prediction. Their results show that public mood states in twitter are strongly correlated with Dow Jones Industrial Index. Chen and Lazer BIBREF11 derived investment strategies by observing and classifying the twitter feeds. Bing et al. BIBREF12 studied the tweets and concluded the predictability of stock prices based on the type of industry like Finance, IT etc. Zhang BIBREF13 found out a high negative correlation between mood states like hope, fear and worry in tweets with the Dow Jones Average Index. Recently, Brian et al. BIBREF14 investigated the correlation of sentiments of public with stock increase and decreases using Pearson correlation coefficient for stocks. In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation. The core contribution of our work is the development of a sentiment analyzer which works better than the one in Brian's work and a novel approach to find the correlation. Sentiment analyzer is used to classify the sentiments in tweets extracted.The human annotated dataset in our work is also exhaustive. We have shown that a strong correlation exists between twitter sentiments and the next day stock prices in the results section. We did so by considering the tweets and stock opening and closing prices of Microsoft over a year.
In this paper, we took a novel approach of predicting rise and fall in stock prices based on the sentiments extracted from twitter to find the correlation.
Answer: Based on the sentiments extracted from twitter to find the correlation.
Question: What is the history of coffee?
Evidence: The history of coffee dates back to centuries of old oral tradition in modern day Ethiopia. However, neither where coffee was first cultivated nor direct evidence of its consumption prior to the 15th century have been found. Sufi monasteries in Yemen employed coffee as an aid to concentration during prayers. Coffee later spread to the Levant and Persia in the early 16th century; it caused some controversy on whether it was halal in Ottoman and Mamluk society. Coffee arrived in Italy the second half of the 16th century through commercial Mediterranean trade routes, while Central and Eastern Europeans learnt of coffee from the Ottomans. By the mid 17th century, it had reached India and the East Indies. Coffee houses would establish themselves in Western Europe by the late 17th century, especially in England and Germany. One of the earliest cultivations of coffee in the New World was when Gabriel de Clieu brought coffee seedlings to Martinique in 1720. These beans later sprouted 18,680 coffee trees which enabled its spread to other Caribbean islands like Saint-Domingue and also to Mexico. By 1788, Saint-Domingue supplied half the world's coffee. By 1852, globally, Brazil became the largest producer of coffee and has held that status ever since. The period since 1950 saw the widening of the playing field due to the emergence of several other major producers, notably Colombia, Ivory Coast, Ethiopia, and Vietnam; the latter overtook Colombia and became the second-largest producer in 1999. Modern production techniques along with the mass productization of coffee has made it a household item today.
Answer: Much of the early history of coffee was tied to oral tradition in modern-day Ethiopia. Presently, there is no evidence of coffee consumption and cultivation before the 15th century. Yemeni monks drank coffee to aid in concentration during prayers. In the early 16th century, coffee spread to the Levant and Persia. Later in the 16th-century coffee arrived in Italy via Mediterranean trade routes. The Ottomans brought it to Central and Eastern Europe. It reached India by the mid-17th century.England and Germany pioneered the coffee house concept that emerged in Western Europe. The Caribbean island of Martinique hosted one of the earliest ex-situ coffee cultivations in the New World - Gabriel de Clieu brought coffee seedlings here in 1720. These resulted in coffee plantations in other Caribbean islands like Saint-Domingue (which, by 1788, supplied half of the world's coffee) and also in Mexico.Brazil is currently the largest coffee producer globally, a status held since 1852. Since 1950, a number of other major producers emerged, including Colombia, Ivory Coast, Ethiopia, and Vietnam. Today, coffee is a household item as a result of modern production techniques and mass productization.
Question: What is the main source of electricity generation in France?
Evidence: According to the International Energy Agency, France has historically generated a very low level of carbon dioxide emissions compared to other G7 economies due to its reliance on nuclear energy. Energy in France is generated from five primary sources: coal, natural gas, liquid fuels, nuclear power, and renewables. In 2020, nuclear power made up the largest portion of electricity generation, at around 78%. Renewables accounted for 19.1% of energy consumption. France has the largest share of nuclear electricity in the world. The country is also among the world's biggest net exporters of electricity. The country is increasingly investing in renewable energy and has set a target of 32% by 2030.
Answer: The electricity production in France is dominated by nuclear power that accounted around 78% of electricity generation in 2020.
Question: How do you cook the perfect pizza?
Answer: The first thing you must do is preheat your oven up as high as it will go. It takes longer than you think to heat your oven up all the way! You want to give your oven at least 30 minutes to preheat. If you have a pizza stone, make sure it’s inside the preheating oven.
For highend ovens, this can be up to 550 degrees F. For regular ovens, they are not going above 475 F. Pizza is traditionally cooked in 900 or 1,000 degree ovens. We can’t get our ovens at home quite that hot, but we want to get as close as we can. Figure out what your oven can do and crank up the heat.
Start out with about 12-16 ounces of dough. Any pizza dough, homemade or store bought, will work for this recipe.
Place your ball of dough in the center of your work surface and use your hands to press it down, starting from the center. Keep gently patting out the dough, moving the dough outward from the center. I like to leave a pretty healthy crust on the edges of my dough.
Continue patting and stretching the dough gently with your hands until it is about 12 inches across. Of course you can use a rolling pin if you want! (Use your hands to lift up the edges of the pizza when you are done to get more a lip for the crust.)
At this point, transfer the dough to a square of parchment paper. I highly recommend using parchment paper because it’s SO much easier transferring your pizza into the oven.
Stretch and arrange the dough on the parchment paper. The dough shrinks a bit when you pick it up to transfer, just gently stretch it out again.
Drizzle a bit of olive oil over the top of your dough. Use your hands or a pastry brush to rub the oil all over the top of the dough, especially the edges of the crust. This layer of oil helps the toppings stay separate from the dough, helping it cook more evenly. It also tastes delicious, especially on your crust. 
Once the oven is up to temperature, we are going to do a 1 to 2 minute par bake.
Some of my favorite pizza toppings:
Just Cheese: Any will work: Mozzarella, Fontina, Parmesan, and Gorgonzola would be amazing.
Pepperoni
Meat Lovers: Pepperoni, salami, Italian sausage, ham, bacon
Hawaiian: Canadian bacon, pineapple
Margherita: fresh mozzarella, fresh basil, tomatoes
BBQ: Use barbecue sauce instead of tomato sauce, then rotisserie chicken, red onions, bacon
Veggie pizza: spinach, tomato, corn, peppers, red onion, mushrooms
Buffalo: Use Frank’s sauce instead of tomato sauce, then add rotisserie chicken, garlic, blue cheese, red onions
Once your pizza is in the oven, you need to cook it for about 8-12 minutes. This is of course going to depend on how hot your oven is, and how thick your pizza is. The crust should be golden brown, and the cheese should be bubbly and also starting to brown. If you don’t have a pizza stone, use a spatula to lift the edge of your pizza to make sure that it is browning all across the center on bottom. If the bottom is still white, you are looking at a doughy pizza. Leave it in longer.
Question: Name five countries in South America.
Answer: Five countries in South America are Chile, Argentina, Uruguay, Brazil, and Paraguay.
Question: How does the author determine whether the data is in fact biased?
Evidence: One interesting pattern is that the ethnicity/race of babies doesn't seem to be mentioned unless the baby is black or asian. In other words: white seems to be the default, and others seem to be marked. How can we tell whether or not the data is actually biased?
We don't know whether or not an entity belongs to a particular social class (in this case: ethnic group) until it is marked as such. But we can approximate the proportion by looking at all the images where the annotators have used a marker (in this case: adjectives like black, white, asian), and for those images count how many descriptions (out of five) contain a marker. This gives us an upper bound that tells us how often ethnicity is indicated by the annotators. Note that this upper bound lies somewhere between 20% (one description) and 100% (5 descriptions). Figure TABREF22 presents count data for the ethnic marking of babies. It includes two false positives (talking about a white baby stroller rather than a white baby). In the Asian group there is an additional complication: sometimes the mother gets marked rather than the baby. E.g. An Asian woman holds a baby girl. I have counted these occurrences as well.
The numbers in Table TABREF22 are striking: there seems to be a real, systematic difference in ethnicity marking between the groups. We can take one step further and look at all the 697 pictures with the word `baby' in it. If there turn out to be disproportionately many white babies, this strengthens the conclusion that the dataset is biased.
I have manually categorized each of the baby images. There are 504 white, 66 asian, and 36 black babies. 73 images do not contain a baby, and 18 images do not fall into any of the other categories. While this does bring down the average number of times each category was marked, it also increases the contrast between white babies (who get marked in less than 1% of the images) and asian/black babies (who get marked much more often). A next step would be to see whether these observations also hold for other age groups, i.e. children and adults. INLINEFORM0 
we can approximate the proportion by looking at all the images where the annotators have used a marker (in this case: adjectives like black, white, asian), and for those images count how many descriptions (out of five) contain a marker.
Answer: They can approximate the proportion by looking at all the images where the annotators have used a marker, and for those images count how many descriptions contain a marker.
Question: Identify which instrument is string or percussion: Rattle, Cak
Answer: Cak is string, Rattle is percussion.
Question: What happened to Bosch's mom?
Evidence: Bosch's mother was a prostitute in Hollywood who was murdered on October 28, 1961, when Bosch was 11 years old. His father, who he met later in life, was Mickey Haller Sr., a prominent defense attorney known for representing mobster Mickey Cohen, among other clients.
Answer: Bosch's mother was murdered in 1961.
Question: What computer science domains are involved in the paper?
Evidence: We present an unsupervised approach for discovering semantic representations of mathematical equations. Equations are challenging to analyze because each is unique, or nearly unique. Our method, which we call equation embeddings, finds good representations of equations by using the representations of their surrounding words. We used equation embeddings to analyze four collections of scientific articles from the arXiv, covering four computer science domains (NLP, IR, AI, and ML) and $\sim$98.5k equations. Quantitatively, we found that equation embeddings provide better models when compared to existing word embedding approaches. Qualitatively, we found that equation embeddings provide coherent semantic representations of equations and can capture semantic similarity to other equations and to words.
We used equation embeddings to analyze four collections of scientific articles from the arXiv, covering four computer science domains (NLP, IR, AI, and ML) and ∼98.5k equations.
Answer: NLP, IR, AI, and ML.