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stats.hackmag.com stats.hackmag.com
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В идеале не разрывать.
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stats.hackmag.com stats.hackmag.com
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Недавно
Во втором заголовке оставляем запятую?
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labuladong.online labuladong.online
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哈希集合 HashSet
什么情况下会选择使用 HashSet 而不是 HashMap：
- 只需要存储唯一元素时：
- HashSet 只存储元素本身，而 HashMap 存储键值对。
- 如果你只关心元素的唯一性，不需要与之关联的值，就应该使用 HashSet。
- 需要快速检查元素是否存在：
- HashSet 提供了高效的 contains() 方法，用于检查元素是否存在。
- 如果你经常需要检查某个元素是否在集合中，HashSet 是个好选择。
- 需要去重：
- HashSet 自动确保所有元素都是唯一的。
- 如果你有一个包含重复元素的集合，并且想要去除重复项，可以将其转换为 HashSet。
- 实现数学集合操作：
- HashSet 适合执行并集、交集、差集等集合操作。因为唯一性
- 不需要保持插入顺序：
- HashSet 不保证元素的顺序
- 内存效率：
- 当你只需要存储元素而不需要额外的键时，HashSet 比 HashMap 更节省内存。
- 只需要存储唯一元素时：
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就是一定要用字符串的 equals 方法比较两个字符串是否相同，不要用 == 比较
==是在比较是否是同一个对象
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type-writer.org type-writer.org
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666: “The Beast” by [[Mark Adams]]
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type-writer.org type-writer.org
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Only one like this in ALL my collection by [[Mark Adams]] on November 4, 2013
This one has a curiously wrapped decorative platen.
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wu-msds-capstones.github.io wu-msds-capstones.github.io
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Author Leann Kim and Kass Traieh
You can actually supply multiple authors!
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the
I think I'd remove this
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Introduction
I would just combine your introduction and background here. Your intro isn't long enough to really have its own section. Just make it the starting paragraph.
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our research question:
remove
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Historically, homeownership has been a cornerstone of wealth accumulation for American families as it provides stability and financial security across generations.
Do you have a source or citation for this?
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Research has shown that high levels of student debt can delay or reduce the likelihood of homeownership.
Needs a citation (or few)
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can lead to more informed policies, enhanced educational programs, and a more equitable and prosperous future
Some more specific examples here might be nice.
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proposed
It's not just proposed! Because you did it!
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recent
2024 (I think, or whatever year it actually is)
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generations
generations? Or just generation?
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This scarcity was exacerbated by the fact that the raw data needed for our analysis was not available on any of the websites we consulted, including government portals and academic repositories.
Isn't this kinda saying the same thing? The data was scarce, and thus it was hard to find...?
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predominantly aggregated
in what ways?
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Figure 1: Original Tables
Captions should be more expansive to describe exactly what you want a reader to be getting out of the image.
Also, I can think of 0 situations where you need year to be a bigint. A smallint in fact would be MORE than enough.
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Furthermore, many of the existing studies and reports that addressed related topics did not provide explicit citations or source details for their data, making it difficult to trace the origins and verify the reliability of the information used in previous research.
You only cited one or two things above. If you have a bigger trove of existing research on this, you should probably cite them.
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Original Tables
This is not a subsection by itself.
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Data Dictionary - Features, Descriptions, and Sources (Click to Expand)
This is really nice, but introduce it in the text. Don't just leave it up to a reader to randomly decide to click on it. Let them know it is there and what it contains.
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aggregated, ensuring no sensitive information was included within the datasets.
I'm not sure just aggregating data means no sensitive information is available. It does probably mean that no personally identifying information is available though.
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Data Cleaning
A lot of this feels like information that you would explain before showing your ERD?
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ease of joining the two tables together.
Probably still need to explain how you were doing that.
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the join conditions account for overlapping age ranges
I'd have to think of exactly how to show it, but a visual showing how your joined on the different overlapping regions might be very useful here.
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Additionally, overlaps or gaps in age ranges might lead to duplication of data, which would affect the overall quality and reliability of the dataset.
Oh! Are you getting duplicated data? How much? Which overlaps are resulting in that duplication?
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Final Data Dictionary
All sections should start with text introducing the section, not a figure or table, etc.
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integrate
assign?
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Due to the limitations of our dataset,
In our dataset,
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we created a formula to manage overlaps by comparing the minimum and maximum birth years to determine the closest generational boundary alignment for each row. We also created new columns for minimum and maximum graduation years. This was done by assuming the average college graduation age to be 21 and adding this to the minimum and maximum birth years.
Barring a visual, a little example in here might be useful to confirm that a reader understands what you did and could replicate it themselves.
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However, despite these measures, the potential for bias remains a concern, as survey respondents may not fully represent the entire population, with certain demographics potentially being underrepresented.
Coming from the census data, surely they have some data on how well their sample mimics the true population?
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a less reliable source
I would just say that you were unable to trace back this data to the original source, and thus are having to rely on this particular analyst as trustworthy and reputable.
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Methods
Introduce each section with at least a short paragraph of text. Here you could be explaining what the following subsections will be discussing.
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Summary
You devolve a bit into a subheading swamp here. Ensure that you are only using headings when you have a significant amount of content in a that section. Otherwise paragraphs are perfectly fine!
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onsequently, we decided to exclude data for Generation Z from our analysi
is this where the 50 rows vanished from? Or is this on top of that?
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Materials List (Software used)
I think it is useful to state this up front as you did, but it needs some text introduction, and then I'd put everything in a table with the library and what you used that library for.
Also, did you do everything in Python here?
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was refined to 92 rows
what did we lose 50 rows to?
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homeownership data only dates back to 1989, it presents a challenge in directly comparing the homeownership rates of Baby Boomers with our graduation debt trend.
Oof, yeah it does
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slope values of each generation
Not 100% clear of what you mean by this. I could see this going a variety of ways.
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debt_at_grad, avg_debt_to_income, avg_start_salary, percent_mortgage, percent_education_loan, and generation_order
while these names are pretty decent, if you are going to be relying on these heavily going forward, you should probably take the time to explain each.
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Where: to = total_owner ts = total_surveyed hr = homeownership_rate
usually this would just be done in sentence structure. And then you need to follow it up with what you were using this equation for.
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formula
I'd usually number important equations.
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Shows No Strong Curvature of Patterns
No need to continue the caps! Also, you should mention your slight parabolic tendency here. And maybe changed Fitted values to home ownership percentage (because that is what it is isn't it?)
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These assumptions include linearity, independence, homoscedasticity, normality, and lack of multicollinearity.
At the end of this: "We investigate each in the following sections."
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very slight parabolic tendency present
Agreed
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we conclude that there is no statistically significant evidence of autocorrelation
Good!
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In practice, DW values close to 2 (within a range of about 1.5 to 2.5) are often considered acceptable.
Source/citation?
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no pronounced pattern of variance around the horizontal line, indicating that the homoscedasticity assumption is generally met.
Would another way to evaluate this be to bin the fitted values and then look at the variance of each of the bins to see how consistent it is?
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Feature Value debt_at_grad 26.534943 avg_start_salary 21.435287 avg_debt_to_income 28.121863 percent_mortgage 1.411813 percent_education_loan 7.590111 generation_order 7.403381
Tables should be labeled and captioned just like Figures (except with Table)
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Around the Horizontal Line
no caps!
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Figure 4
If you have multiple things on a plot, I think it is important to include a legend. Though good on you mentioning the 45 deg line in your caption.
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R-squared Adjusted R-squared Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE) F-statistic
You better be ready to explain why you are looking at each of these if you are going to list them here.
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Random Forest models, being ensemble methods based on decision trees, are less sensitive to multicollinearity and do not require the assumptions of linearity, homoscedasticity, or normality of residuals.
Very nice.
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2.41e+05
Write this out in the more classic human readable form.
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relatively high
compared to what?
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Although the other assumptions of the model are satisfied, the presence of multicollinearity necessitates cautious interpretation of our results.
Can you tell what they are collinear with? Should you have dropped some of these variables?
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library to train and test the dataset.
with what parameters? Using the same 70/30 split?
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R-squared Adjusted R-squared Mean Absolute Error (MAE) Mean Squared Error (MSE) Root Mean Squared Error (RMSE)
Same comment as above. You better be ready to use and explain each of these if you are bringing them up here.
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Rising Average Debt Levels at Graduation Across Successive Generations.
Don't just state what it is, but what a reader should conclude from looking at this image.
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Figure 5
What happened in 1977-78??
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Generation Slope
Presumably this is the slope of each of the above sections, but you don't introduce this part at all other than mentioning it in an earlier much earlier section. Introduce it, explain why it is interesting, etc. Why are you interested in slope here over raw average amount?
Also, this is a table, and should be listed and referenced as such.
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Coefficient Results
Why its own section? Is this not still about the model fit?
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approximately 65% by 2022. This recent increase might be due to various factors, including economic recovery and an increase in housing demand.
This is super interesting to me, and not necessarily what I'd have expected to see. So home ownership rates are at their highest ever despite also seemingly at their most expensive ever? And with the most debt ever?
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A Reflection of Economic Conditions and Financial Barriers.
no caps.
And reiterate some of what you had in the text about what a reader should observe on this image.
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The linear regression model demonstrates a relatively good fit, with an R-squared value of 0.708, indicating that 70.8% of the variability in homeownership rates is explained by the predictors. The adjusted R-squared of 0.681 suggests that after accounting for the number of predictors in the model, about 68.1% of the variance is explained, which is still substantial. The F-statistic for the model is 26.67 with a p-value of 6.99e-16, indicating that the model as a whole is statistically significant and provides a good fit for the data.
Well, you discussed some of them, but you still left off about half of them. Which isn't necessarily terrible, but you shouldn't make as big of a deal about what metrics you will be looking at earlier in that case.
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Metric Value
Table!
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This counterintuitive finding may be attributable to factors such as high multicollinearity, sampling issues, or specific economic conditions that could influence the observed relationship.
Could it be an indicator that getting a college education, despite the loans, ends up making more and thus still having the capacity to afford a home?
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The coefficient
Just mentioning it here, but you have a lot of very small paragraphs in this section. I suspect several can be combined, and would make reading this flow a bit better.
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not provide a definitive answer
Is there something that would give you a definitive answer?
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Feature Coefficient P-Value
A) This is a table. B) I'd strongly suggest ordering these in some way to make this table easier to understand at a glance.
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Feature Importance Results Feature Importance
Introduce and explain? Also, pretty sure this shouldn't be its own section, as it is part of the model fit.
Also, as far as the table is concerned, you have this exact data represented visually below, which I tend to think is the better way to showcase it. Add the exact numbers if you want to the table, but otherwise I'd go with that instead of this table.
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Model test statistics: Metric Value
Table!
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We excluded percent_mortgage from this visualization due to its dominance as the most significant feature.
ok, good comment
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substantial
Is substantial the correct word here? It is the dominant of what you have remaining, but I'd argue you already excluded the real substantial player...
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potentially delaying their entry into homeownership
And yet home ownership is up! So it is older individuals that are buying all the homes?
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Figure 7
reference properly in quarto to link
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percent_education_loan and debt_at_grad
you have been monospace font-ing these variables the entire paper. Don't change it up now!
Also, can you remind what the difference between these to is? Because they sound very similar.
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significantly
Be careful throwing this word around unless you directly showed this
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The prominence of these variables implies that higher levels of student loan debt could be a considerable barrier to homeownership.
I think you have this breakdown, but nowhere in this article do I recall seeing how home ownership is distributed across the generations. That feels like very useful context for understanding what you are trying to lay out here!
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has minimal impact on their likelihood of owning a home when compared to more direct financial factors, such as student loan debt.
I think it is just mostly collinear with these more direct measures
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This result indicates that, according to the model, the generational sequence alone does not substantially influence homeownership rates. While generational trends and experiences might shape broader economic and social patterns, the specific cohort to which an individual belongs appears less relevant in this context than the tangible financial burdens they face.
You have a lot of tiny paragraphs here again that can (and should) probably be combined.
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ebt at graduation (debt_at_grad) is a significant factor influencing homeownership rates. The data shows a clear increase in student loan debt across generations, with Millennials bearing the highest levels, which is likely contributing to the challenges they face in achieving homeownership. Despite the substantial debt load carried by recent graduates, the impact of debt at graduation on homeownership is relatively moderate.
You say it is significant, and then you downplay it to relatively moderate? Which is it?
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In addressing our research question—how do differences in student loan debt between older and current generations affect homeownership trends?—our study offers both insightful and nuanced perspectives. The historical context and the analysis reveal a complex relationship between student loan debt and homeownership rates. Our findings indicate that student loan debt at graduation (debt_at_grad) is a significant factor influencing homeownership rates. The data shows a clear increase in student loan debt across generations, with Millennials bearing the highest levels, which is likely contributing to the challenges they face in achieving homeownership. Despite the substantial debt load carried by recent graduates, the impact of debt at graduation on homeownership is relatively moderate. Through linear regression and Random Forest analyses, we confirmed that debt_at_grad has a less pronounced but still notable effect. Percent_education_loan, while less influential than mortgage debt, also plays a role, suggesting that education loan debt does impact homeownership trends to a degree. Interestingly, generation_order, representing the sequence of generations, was found to be the least significant predictor of homeownership rates. This indicates that, within the context of our model, generational cohort alone does not substantially explain variations in homeownership rates compared to the financial burden of student loans and mortgages.
This almost feels like the start of a conclusion, and then you can talk about other things?
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It is crucial to acknowledge that student loan debt is not the sole determinant of homeownership trends. Inflation, for instance, has affected purchasing power and contributed to rising housing costs, which can make homeownership more challenging for many.
Now this feels like the start of a new paragraph
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References
Look up how to properly cite websites in APA format. Or use a Bibtex entry and have Quarto do it for you.
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According to a study by the Federal Reserve Board, a $1,000 increase in student loan debt is associated with a 1.8 percent decrease in the homeownership rate for public four-year college graduates, resulting in a delay in purchasing a home.
This feels extremely relevant to your study here, and thus should probably be brought up much earlier (and you need a citation)
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Furthermore and as addressed above, despite our efforts to mitigate bias, the potential for underrepresentation of certain demographics in the survey samples remains a concern. Student loan debt can exacerbate racial disparities. Racial wealth and income gaps are rooted in historical discriminatory housing policies - meaning that Black students, in particular, may face greater financial risks in pursuing higher education. Not adequately capturing the experiences of these underrepresented groups can affect our research and must be taken into consideration when evaluating our results.
This also feels like a new paragraph
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We can reference this in our conclusion/limitations: https://housingmatters.urban.org/articles/how-student-loan-debt-affects-racial-homeownership-gap “Extensive evidence underscores how debt affects mortgage eligibility and credit score, erecting clear barriers to homeownership. A study by the Federal Reserve Board found that a $1,000 increase in student loan debt lowers the homeownership rate by about 1.8 percent for public four-year college goers; this amounts to an average delay in about four months for attaining homeownership. Student loan debt may reproduce and exacerbate the racial homeownership gap. Enduring racial disparities in wealth and income—which were, in part, created through decades of racist and discriminatory housing policies that blocked wealth building for many families—mean a greater proportion of Black students need to take on a greater and more enduring financial risk to pursue higher education. Therefore, reducing the impact of student loans on mortgage eligibility could be a critical component of ensuring a more equitable housing landscape.”
What just happened down here?
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But do thou leave as unlawful these winnings, and deal with Cause and Effect, the chancellors of God. In the Will work and acquire, and thou hast chained the wheel of Chance, and shalt sit hereafter out of fear from her rotations.
I take this simply as chance is temporary, or a "teach a man to fish" type of literary element. Winning the lottery is not as valuable as earning the title of a profession you have a passion for, possibly? I chose to believe it as this.
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There is no more deviation in the moral standard than in the standard of height or bulk. No greater men are now than ever were
I see this as more of a "in the present over all else", rather than simply saying one generation or time of men is greater than another. The time to make one's impact is, in fact, the greatest time there is.
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The populace think that your rejection of popular standards is a rejection of all standard, and mere antinomianism; and the bold sensualist will use the name of philosophy to gild his crimes. But the law of consciousness abides.
Emerson criticizes those who misuse philosophy to justify immoral behavior (the bold sensualist), emphasizing that true guidance comes from the "law of consciousness," or one's inner moral compass, which remains constant and true regardless of external pressures or justifications.
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I hope in these days we have heard the last of conformity and consistency. Let the words be gazetted and ridiculous henceforward. Instead of the gong for dinner, let us hear a whistle from the Spartan fife. Let us never bow and apologize more. A great man is coming to eat at my house. I do not wish to please him; I wish that he should wish to please me.
In this metaphorical scenario, the great man represents societal expectations, influential figures, or those deemed important by society. Emerson rejects the idea of altering oneself to please others, no matter how powerful or influential they may be. Instead, he suggests that true greatness lies in being so self-assured and authentic that others feel compelled to conform to your way of being, or rid of the conformist concept altogether.
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Whoso would be a man must be a nonconformist. He who would gather immortal palms must not be hindered by the name of goodness, but must explore if it be goodness.
Emerson asserts this central tenet of "Self-Reliance", that which underscores the conflict between individual authenticity and societal pressures. Emerson argues that true greatness arises from independence of thought and action, rather than blind adherence to norms.
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Local file Local file
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add .
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Change to: .
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having
have
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1.
Sentence. Wrong, no conjugated verb.
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by
conjuction❌. Common mistake for me, by is not a conjuction but a proposition as it doesn't really link any clauses (subordinating or coordinating)
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internationally
adverb. This time it does tell you how she was acclaimed (the international is modifiyng as it's before the verb)
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emmarshall.github.io emmarshall.github.io
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Shaping
I have never thought about this kind of process and it makes me rethink the way I have been taught things. I am curious to know if parents or teachers consciously use this idea with children. I am also curious where else this idea could be applied in a useful way.
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emmarshall.github.io emmarshall.github.io
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Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group
This article talks about where prejudice may stem from. It explains that our brains feel the needs to simplify complex things, and one way of doing that is placing people in categories. Those mental categories help people to subconsciously build stereotypes and prejudices. https://www.psychologicalscience.org/news/releases/research-states-that-prejudice-comes-from-a-basic-human-need-and-way-of-thinking.html
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emmarshall.github.io emmarshall.github.io
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High congruence leads to a greater sense of self-worth and a healthy, productive life.
How do you view yourself? How do you think about who you want to be? How similar are these two things? The closer they are, the better you will feel! Being what you think the best version of yourself is will help you stay healthy and happy.
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emmarshall.github.io emmarshall.github.io
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Obsessive-Compulsive Disorder
I think this article does a good job of showing parts of OCD beyond the stereotype. Usually, people think of OCD as needing things neat and clean or counting all the time, but there is a whole other side to OCD besides the compulsions, half of this disorder is the thought process behind it, the obsessions. https://www.healthactionresearch.org.uk/selected-blogs/how-ocd-is-portrayed/
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digitalpasts.github.io digitalpasts.github.io
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Eblaite
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www.omekademo.dhcbarnard.org www.omekademo.dhcbarnard.org
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The Text Annotation plugin lets you use Hypothesis within the Omeka interface.
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This page demonstrates the plugin for using Hypothesis within an Omeka website.
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Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat
Yep, this sentence is in Latin.
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some text
Annotating this phrase to indicate that "some" is not a concise unit of measure.
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www.sciencedirect.com www.sciencedirect.com
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RRID:AB_2536180
DOI: 10.1016/j.celrep.2021.108895
Resource: (Thermo Fisher Scientific Cat# A-31570, RRID:AB_2536180)
Curator: @Naa003
SciCrunch record: RRID:AB_2536180
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RRID:AB_162543
DOI: 10.1016/j.celrep.2021.108895
Resource: (Thermo Fisher Scientific Cat# A-31572, RRID:AB_162543)
Curator: @Naa003
SciCrunch record: RRID:AB_162543
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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31170
DOI: 10.3390/cells13151253
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Curator: @anisehay
SciCrunch record: RRID:BDSC_31170
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8442
DOI: 10.3390/cells13151253
Resource: RRID:BDSC_8442
Curator: @anisehay
SciCrunch record: RRID:BDSC_8442
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6903
DOI: 10.3390/cells13151253
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Curator: @anisehay
SciCrunch record: RRID:BDSC_6903
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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24356
DOI: 10.17912/micropub.biology.001236
Resource: RRID:BDSC_24356
Curator: @anisehay
SciCrunch record: RRID:BDSC_24356
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9278
DOI: 10.17912/micropub.biology.001236
Resource: RRID:BDSC_9278
Curator: @anisehay
SciCrunch record: RRID:BDSC_9278
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www.biorxiv.org www.biorxiv.org
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antibodies
DOI: 10.1101/2024.02.25.581951
Resource: (Cell Signaling Technology Cat# 13969, RRID:AB_2798355)
Curator: @vtello
SciCrunch record: RRID:AB_2798355
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www.biorxiv.org www.biorxiv.org
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caspase 3
DOI: 10.1101/2024.01.31.578288
Resource: (Abcam Cat# ab13847, RRID:AB_443014)
Curator: @vtello
SciCrunch record: RRID:AB_443014
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www.biorxiv.org www.biorxiv.org
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C57Bl6
DOI: 10.1101/2024.01.29.577706
Resource: RRID:IMSR_JAX:000664
Curator: @vtello
SciCrunch record: RRID:IMSR_JAX:000664
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Socs3fl/fl
DOI: 10.1101/2024.01.29.577706
Resource: (IMSR Cat# JAX_010944,RRID:IMSR_JAX:010944)
Curator: @vtello
SciCrunch record: RRID:IMSR_JAX:010944
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Ptenfl/fl
DOI: 10.1101/2024.01.29.577706
Resource: (IMSR Cat# JAX_006440,RRID:IMSR_JAX:006440)
Curator: @vtello
SciCrunch record: RRID:IMSR_JAX:006440
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www.biorxiv.org www.biorxiv.org
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AAV1-CaMKIIα-eNpHR 3.0-EYFP
DOI: 10.1101/2024.01.06.574489
Resource: RRID:Addgene_26971
Curator: @vtello
SciCrunch record: RRID:Addgene_26971
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AAV9.CaMkIIα-EGFP
DOI: 10.1101/2024.01.06.574489
Resource: RRID:Addgene_50469
Curator: @vtello
SciCrunch record: RRID:Addgene_50469
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www.biorxiv.org www.biorxiv.org
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L-WRN
DOI: 10.1101/2024.01.01.573838
Resource: (ATCC Cat# CRL-3276, RRID:CVCL_DA06)
Curator: @vtello
SciCrunch record: RRID:CVCL_DA06
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www.biorxiv.org www.biorxiv.org
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Cat No. MOB-0361MC
DOI: 10.1101/2023.07.12.548630
Resource: AB_3111475
Curator: @bpowell22
SciCrunch record: RRID:AB_3111475
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www.science.org www.science.org
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catalog no. HPAB0330CQ
DOI: 10.1126/sciadv.abq3104
Resource: AB_3111386
Curator: @bpowell22
SciCrunch record: RRID:AB_3111386
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Annotators
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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TAB-418MZ
DOI: 10.1158/2326-6066.CIR-19-0514
Resource: AB_2941837
Curator: @bpowell22
SciCrunch record: RRID:AB_2941837
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Annotators
URL
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www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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PABL-202
DOI: 10.1038/s41467-022-35252-y
Resource: AB_2877637
Curator: @bpowell22
SciCrunch record: RRID:AB_2877637
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Annotators
URL
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52.53.155.43 52.53.155.43
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HMC3
DOI: 10.1093/neuonc/noae135
Resource: (ATCC Cat# CRL-3304, RRID:CVCL_II76)
Curator: @vtello
SciCrunch record: RRID:CVCL_II76
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CT-2A
DOI: 10.1093/neuonc/noae135
Resource: (Millipore Cat# SCC194, RRID:CVCL_ZJ44)
Curator: @vtello
SciCrunch record: RRID:CVCL_ZJ44
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
Cat. No. TAB-611CT
Tags
Annotators
URL
-
-
aacrjournals.org aacrjournals.org
-
clone 103.2
DOI: 10.1158/1078-0432.CCR-20-4576
Resource: AB_3111655
Curator: @bpowell22
SciCrunch record: RRID:AB_3111655
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-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
RRID: N/A
DOI: 10.1016/j.cmet.2017.10.006
Resource: (Creative Biolabs Cat# NABL-079, RRID:AB_3111560)
Curator: @bpowell22
SciCrunch record: RRID:AB_3111560
Tags
Annotators
URL
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
MRO-1214LC
DOI: 10.1172/jci.insight.146148
Resource: AB_3111526
Curator: @bpowell22
SciCrunch record: RRID:AB_3111526
Tags
Annotators
URL
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
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MRO-1214LC, CR3022
DOI: 10.1016/j.jviromet.2021.114111
Resource: AB_3111526
Curator: @bpowell22
SciCrunch record: RRID:AB_3111526
Tags
Annotators
URL
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
Creative BioLabs
DOI: 10.1007/s10858-020-00328-8
Resource: AB_3111481
Curator: @bpowell22
SciCrunch record: RRID:AB_3111481
Tags
Annotators
URL
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
FI6
DOI: 10.3389/fimmu.2020.00902
Resource: AB_3111435
Curator: @bpowell22
SciCrunch record: RRID:AB_3111435
Tags
Annotators
URL
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
mCAIX, clone MSC3, Creative Biolabs
DOI: 10.1021/acs.molpharmaceut.3c00045
Resource: AB_3111834
Curator: @bpowell22
SciCrunch record: RRID:AB_3111834
Tags
Annotators
URL
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
CR3022, Creative Biolabs
DOI: 10.1016/j.jaci.2020.12.003
Resource: AB_3111526
Curator: @bpowell22
SciCrunch record: RRID:AB_3111526
Tags
Annotators
URL
-
-
www.sciencedirect.com www.sciencedirect.com
-
ZZUNEUi030-A
DOI: 10.1016/j.scr.2024.103515
Resource: CVCL_D6KC
Curator: @vtello
SciCrunch record: RRID:CVCL_D6KC
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-
www.sciencedirect.com www.sciencedirect.com
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No. 42-6600
DOI: 10.1016/j.scr.2024.103532
Resource: (Thermo Fisher Scientific Cat# 42-6600, RRID:AB_2533534)
Curator: @vtello
SciCrunch record: RRID:AB_2533534
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No. A24881
DOI: 10.1016/j.scr.2024.103532
Resource: (Thermo Fisher Scientific Cat# A24881, RRID:AB_2889933)
Curator: @vtello
SciCrunch record: RRID:AB_2889933
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-
www.sciencedirect.com www.sciencedirect.com
-
Addgene#86708
DOI: 10.1016/j.scr.2024.103523
Resource: RRID:Addgene_86708
Curator: @vtello
SciCrunch record: RRID:Addgene_86708
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-
www.sciencedirect.com www.sciencedirect.com
-
RRID:CVCL_0547
DOI: 10.1016/j.isci.2024.110658
Resource: (BCRC Cat# 60343, RRID:CVCL_0547)
Curator: @vtello
SciCrunch record: RRID:CVCL_0547
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-
www.sciencedirect.com www.sciencedirect.com
-
Cat# 12259
DOI: 10.1016/j.stem.2024.07.006
Resource: RRID:Addgene_12259
Curator: @vtello
SciCrunch record: RRID:Addgene_12259
-
Cat# 12260
DOI: 10.1016/j.stem.2024.07.006
Resource: RRID:Addgene_12260
Curator: @vtello
SciCrunch record: RRID:Addgene_12260
-
Cat# WA01
DOI: 10.1016/j.stem.2024.07.006
Resource: (RRID:CVCL_9771)
Curator: @vtello
SciCrunch record: RRID:CVCL_9771
-
-
www.sciencedirect.com www.sciencedirect.com
-
Charles River UK028
DOI: 10.1016/j.celrep.2024.114630
Resource: (IMSR Cat# CRL_028,RRID:IMSR_CRL:028)
Curator: @vtello
SciCrunch record: RRID:IMSR_CRL:028
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-
www.sciencedirect.com www.sciencedirect.com
-
Flybase_FBst0462081
DOI: 10.1016/j.cub.2024.07.060
Resource: Flybase_FBst0462081
Curator: @vtello
SciCrunch record: RRID:Flybase_FBst0462081
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-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
Bloomington Stock #28417
DOI: 10.1371/journal.pgen.1011181
Resource: RRID:BDSC_28417
Curator: @anisehay
SciCrunch record: RRID:BDSC_28417
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-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
UAS-mito-GCaMP6
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
R58E02-GAL4
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
PINK1B9
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
UAS-TDP-43-FLAG
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
UAS-TBPH-FLAG
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
TBPHG2
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
hCHCHD2 T61I
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
hCHCHD2 WT
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
dCHCHD2 null
DOI: 10.1093/pnasnexus/pgae319
Resource: SCR_00645
Curator: @anisehay
SciCrunch record: RRID:SCR_00645
-
-
onlinelibrary.wiley.com onlinelibrary.wiley.com
-
RRID: CVCL_0062
DOI: 10.1002/ijc.33832
Resource: (RRID:CVCL_0062)
Curator: @bandrow
SciCrunch record: RRID:CVCL_0062
-
-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
BL 80580
DOI: 10.3390/ijms25158429
Resource: RRID:BDSC_80580
Curator: @anisehay
SciCrunch record: RRID:BDSC_80580
-
BL 43632
DOI: 10.3390/ijms25158429
Resource: RRID:BDSC_43632
Curator: @anisehay
SciCrunch record: RRID:BDSC_43632
-
BL 27730
DOI: 10.3390/ijms25158429
Resource: RRID:BDSC_27730
Curator: @anisehay
SciCrunch record: RRID:BDSC_27730
-
BL 50783
DOI: 10.3390/ijms25158429
Resource: RRID:BDSC_50783
Curator: @anisehay
SciCrunch record: RRID:BDSC_50783
-
BL 18052
DOI: 10.3390/ijms25158429
Resource: RRID:BDSC_18052
Curator: @anisehay
SciCrunch record: RRID:BDSC_18052
-
BL 8765
DOI: 10.3390/ijms25158429
Resource: RRID:BDSC_8765
Curator: @anisehay
SciCrunch record: RRID:BDSC_8765
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-
www.ncbi.nlm.nih.gov www.ncbi.nlm.nih.gov
-
63147
DOI: 10.1242/bio.060491
Resource: RRID:BDSC_63147
Curator: @anisehay
SciCrunch record: RRID:BDSC_63147
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-
52.53.155.43 52.53.155.43
-
BDSC:60078
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60078
Curator: @scibot
SciCrunch record: RRID:BDSC_60078
-
BDSC:35156
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_35156
Curator: @scibot
SciCrunch record: RRID:BDSC_35156
-
BDSC:62869
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_62869
Curator: @scibot
SciCrunch record: RRID:BDSC_62869
-
BDSC:60078
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60078
Curator: @scibot
SciCrunch record: RRID:BDSC_60078
-
BDSC:60102
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60102
Curator: @scibot
SciCrunch record: RRID:BDSC_60102
-
BDSC:53025
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_53025
Curator: @scibot
SciCrunch record: RRID:BDSC_53025
-
BDSC:62869
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_62869
Curator: @scibot
SciCrunch record: RRID:BDSC_62869
-
BDSC:60102
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60102
Curator: @scibot
SciCrunch record: RRID:BDSC_60102
-
BDSC:53025
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_53025
Curator: @scibot
SciCrunch record: RRID:BDSC_53025
-
BDSC:41711
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_41711
Curator: @scibot
SciCrunch record: RRID:BDSC_41711
-
BDSC:41711
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_41711
Curator: @scibot
SciCrunch record: RRID:BDSC_41711
-
BDSC:55967
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_55967
Curator: @scibot
SciCrunch record: RRID:BDSC_55967
-
BDSC:53321
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_53321
Curator: @scibot
SciCrunch record: RRID:BDSC_53321
-
BDSC:55967
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_55967
Curator: @scibot
SciCrunch record: RRID:BDSC_55967
-
BDSC:53321
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_53321
Curator: @scibot
SciCrunch record: RRID:BDSC_53321
-
BDSC:38976
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_38976
Curator: @scibot
SciCrunch record: RRID:BDSC_38976
-
BDSC:38976
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_38976
Curator: @scibot
SciCrunch record: RRID:BDSC_38976
-
BDSC:55660
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_55660
Curator: @scibot
SciCrunch record: RRID:BDSC_55660
-
BDSC:55660
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_55660
Curator: @scibot
SciCrunch record: RRID:BDSC_55660
-
BDSC:60370
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60370
Curator: @scibot
SciCrunch record: RRID:BDSC_60370
-
BDSC:54823
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_54823
Curator: @scibot
SciCrunch record: RRID:BDSC_54823
-
BDSC:60370
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60370
Curator: @scibot
SciCrunch record: RRID:BDSC_60370
-
BDSC:54823
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_54823
Curator: @scibot
SciCrunch record: RRID:BDSC_54823
-
BDSC:57512
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_57512
Curator: @scibot
SciCrunch record: RRID:BDSC_57512
-
BDSC:60369
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_60369
Curator: @scibot
SciCrunch record: RRID:BDSC_60369
-
BDSC:57512
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_57512
Curator: @scibot
SciCrunch record: RRID:BDSC_57512
-
BDSC:55989
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_55989
Curator: @scibot
SciCrunch record: RRID:BDSC_55989
-
BDSC:61268
DOI: 10.1534/g3.119.400581
Resource: RRID:BDSC_61268
Curator: @scibot
SciCrunch record: RRID:BDSC_61268
-