dataesr/scientific-paragraphs-categorization huggingface dataset:
For each in vitro test method, classifications were compared to in vivo classifications using a bottom-up and top-down approach  (Scott et al. , 2010). Specifically, for the top-down approach, the ability of an in vitro test method to identify serious eye damage (i. e. , in vitro and in vivo methods independently predicted a formulation should be classified as Category 1) was evaluated (Category 1 vs Categories 2 and Not Classified). Likewise, for the bottom-up approach, the ability of an in vitro test method to identify formulations that do not require eye irritation labels (i. e. , in vitro and in vivo methods independently predicted a formulation should considered not classified (NC)) was evaluated (Categories 1 and 2 vs Not Classified). Data were also evaluated based on formulation type to determine if there were any specific types associated with particularly high or low concordance.
GHS concentration threshold (CT) approach To determine if the eye irritation potential of an agrochemical formulation could be predicted based on the collective toxicity of its individual hazardous ingredients, a standardized worksheet was developed. Each company performed the CT approach as described in the GHS for classification of mixtures and detailed in Table  S3. Each company obtained the eye and skin irritation classifications of the individual hazardous ingredients and assessed the pH for each of their respective formulations. None of the formulations had extreme pH (13 < pH < 2. 5) or skin corrosion (generally rare for agrochemical formulations) which was deemed useful in prediction of the in vivo rabbit outcomes  (Kolle et al. , 2017b; Corvaro et al. , 2017). In most cases, these data were obtained from external sources (i.
, 2014; Barroso et al. , 2017). In fact, in the present dataset all the 21 formulations assigned Category 1 (11%  [21/192] ) were based on the presence of a persistent effect on day 21 in at least one tested animal (Table  S8 ). Therefore, and in line with previous reports it is not fully unexpected that the BCOP and ICE applied in a top-down approach resulted in 100% false negative rates. However, when evaluating agrochemical formulations, BCOP and ICE test methods can be applied similar to the RhCE test method for the identification of non-irritant formulations; other results ("not non-irritant") require additional testing to further differentiate the ocular irritant categories. Although binary concordance between in vitro and GHS CT approach and in vivo results using a top-down or bottom-up approach was moderate for all four test methods evaluated, the negative predictivity rate for the four methods was >85% (Table  2A  and B ).
The high negative predictivity rate indicates that a formulation identified as not requiring hazard classification based on an in vitro test method or GHS CT approach, is not likely to be an eye irritant based on in vivo classification. Therefore, based on the data presented in this study, and considering recognized variability of the in vivo test method  (Weil and Scala, 1971; Earl et al. , 1997; York and Steiling, 1998; Luechtefeld et al. , 2016) , the in vitro methods or GHS CT approach are considered to be sufficiently predictive for the absence of eye irritation and as useful animal test replacement for regulatory applications. The formulation type may have an impact on whether a specific formulation produces an in vitro result that is concordant with the in vivo response. To assess the impact of formulation type, individual and group formulations were evaluated for concordance.
It is also possible that more extreme temperatures lead to more discussions of global warming in the local media, which in turn influence residents' climate change beliefs. 12 An additional advantage of using temperature anomaly is that it is plausibly exogenous to the local economic conditions and thus facilitates causal inferences  (Dell et al. , 2014). We use the monthly temperature data provided by the National Oceanic and Atmospheric Association (NOAA) from more than 10,000 weather stations across the U. S. to construct a baseline climatic variable. Specifically, the temperature anomaly in a region is the difference between monthly temperature (in Fahrenheit degrees) and the historical average temperature  (from 1961 to 1990 ) in the region. We then take a 36-month moving average of this temperature anomaly as our main variable of interest.
Data We obtain data from several sources including: (1) National Oceanic and Atmospheric Administration (NOAA); (  2  First, we obtain temperature data from NOAA. The raw temperature data is based on 5 km gridded data from more than 10,000 land-based weather stations. NOAA aggregates the data and provides monthly temperature data at U. S. county-level for 48 contiguous states since the year 1895. Our primary climatic variable is Temperature anomaly, defined as the difference between monthly temperature (in Fahrenheit degrees) and the 30-year average temperature (from 1961 to 1990) in a county. We then take a 36-month moving average of the temperature anomaly as the main explanatory variable. A positive (negative) temperature anomaly means a recent temperature warmer (cooler) than the historical average.
Second, we obtain detailed mortgage applications from HMDA, which is collected annually by the Federal Financial Institutions Examination Council (FFIEC). The database covers all mortgage applications that have been reviewed by qualified financial institutions in the calendar year. A financial institution is required to complete a HMDA register if it has at least one branch office in any metropolitan statistical area and meets certain criteria (i. e. , asset size above a specific threshold). HMDA includes the vast majority of home mortgage applications and approved loans in the United States, and provides information such as lender identity, borrower characteristics (e. g. , income, loan-to-income, and race), loan characteristics (e. g. , loan amount, type, and purpose), property characteristics (e.
g. , type and geographic location), and the application outcome (e. g. , approved, denied, withdrawn, or closed for incompleteness). Third, we obtain the loan pricing and loan characteristics information from Fannie Mae Single-Family Loan Performance Dataset (Fannie Mae) and Freddie Mac Single-Family Loan-Level Dataset (Freddie Mac). Loans covered by these two datasets are known as conforming loans, which are loans that are equal to or less than the dollar amount established by the conforming-loan limit set by the Federal Housing Finance Agency (FHFA) and meets the funding criteria of Freddie Mac and Fannie Mae. The Fannie Mae and Freddie Mac (F&F) datasets provide origination and performance data for fully amortizing, full documentations, single-family, conforming fixed-rate mortgages (the predominant conforming contract type in the U.
S. ). The F&F datasets provide detailed information on a range of borrower, property, and loan characteristics at the time of origination, such as loan interest rate, property location (first 3digit zip code), borrower credit score, loan-to-income ratio, loan-to-value ratio, and loan term. Fourth, we obtain U. S. county-level macroeconomic variables from the United States Census Bureau and Bureau of Labor Statistics. We obtain the county-level House Price Index (HPI) data, which is a broad measure of the movement of single-family house prices, from the Federal Housing Finance Agency (FHFA). Fifth, we download from Google Trends the monthly Search Volume Index (SVI) of the topic "Global warming" in each Designated Market Area (DMA) of U. S. 2526 This is used to proxy for people's attention to global warming.
More information can be found at: https://www. nielsen. com/intlcampaigns/us/dma-maps. html States now/within 10 years. The data on climate change belief is available annually from 2014 to 2018. To construct our main sample, we begin with all HMDA mortgage applications during 1990-2016. We drop applications that were closed for incompleteness, withdrawn by the applicant before a decision was made, and loans sold by the institution. We additionally drop Fintech lenders using the list provided by  Buchak et al. (2018). 27  We aggregate the loan applications to county level and match the temperature data and the macroeconomic variables with the HMDA database. Our final sample contains 83,408 county-year observations for 3105 unique counties in the U. S. between 1990 and 2016. In addition, we construct the sample containing loan interest rate information following  Buchak et al.
Overall, the insignificant result on loan interest rates suggests that lenders subjective concerns about climate change mainly manifest through adjusting approval decision instead of loan pricing. This is consistent with prior literature that loan pricing is determined mainly by computerized bank algorithms that rely on hard information, such as the borrower's FICO score, loan-to-value ratio, and documentation level, with little input from the loan officer  (Rajan et al. , 2015). One caveat about the insignificant effect on loan interest rate is that our data for this test only includes conforming loans, which are loans that meet the funding criteria of Freddie Mac and Fannie Mae and are eligible to be sold to these two GSEs. As a result, lenders who originate conforming loans do not need to hold them on their balance sheets, and hence have less incentives to charge higher 30 Loan denial reasons include loan-to-income ratio, employment history, credit history, collateral, insufficient cash, unverifiable information, credit application incomplete, mortgage insurance denied, and other unclassified reasons.
Although the disclosure of denial reason is not mandatory, most loan denial reasons are reported. Please see  Cortes et al. (2016)  for more details. 31 Loan officer may report as many as three reasons. We identify the loan denial due to collateral reason if at least one reason mentioned is "collateral". 32 We first apply the same method as Loan denial for collateral reason to construct loan denial for loan-to-income ratio, employment history, credit history, insufficient cash, unverifiable information, credit application incomplete, mortgage insurance denied, and other unclassified reasons. The Loan denial for other reasons is the sum of all these reasons. 2022 ), who find lenders charge higher interest rates for mortgages on properties exposed to a greater risk of sea level rise (SLR). Their loan interest rate dataset includes both conforming and non-conforming loans, and they further show that the pricing of SLR is more pronounced when the loans are not eligible to be sold to GSEs (non-conforming loans).
To further rule out the credit demand channel, we conduct several additional tests. First, we use natural disasters hitting neighboring counties as an alternative shock to beliefs about climate change  (Alok et al. , 2020; Correa et al. , 2023). The idea is that lenders' concern about climate change could rise after observing climate change-related natural disasters in neighboring counties, but these disasters are unlikely to affect the county's economic conditions. Following  Correa et al. (2023) , we first classify climate change-related disasters as hurricanes, flooding, and wildfire. 33 The data is obtained from SHELDUS (Spatial Hazard Events and Losses Database for the United States). We then identify counties not affected by climate change-related disasters, but whose neighboring counties are affected by these disasters ("indirect disasters").
(2019). The unit of analysis is at county-year level, and the sample period is from 2000 to 2016. Variable definitions are provided in Appendix A. Standard errors in parentheses are two-way clustered at county and year level. ***, **, and * correspond to statistical significance at the 1 %, 5 %, and 10 % levels, respectively. The nature logarithm of one plus the per capita damages due to natural hazards at county-year level. Natural hazards include floods, hurricane, tornado, storm and wildfire. The data is obtained from SHELDUS (Spatial Hazard Events and Losses Database for the United States). Other VariablesWorryThe fraction of population in a county who are somewhat/very worried about global warming from Yale Climate Opinions Maps. 42  TimingThe fraction of population in a county who think global warming is already harming people in the United States now/within 10 years from Yale Climate Opinions Maps.
Final data set The econometric analyses rely on a data set which combines information from various administrative sources. This includes administrative data for cloud adoption, which stem from the Survey on ICT Usage and E-Commerce in Enterprises administered by Eurostat. In addition, we use the German administrative business registry that contains additional information on firm characteristics. Finally, we rely on policy data, which provides specifics about the context and eligibility of the GRW grant scheme. The primary data source is administrative data on the use of cloud computing by firms. Under the administration of Eurostat, information on cloud computing and other ICT variables are collected by means of a business survey by each country annually through their office of national statistics, thus resulting in reasonable consistency in terms of questions asked and technologies covered across countries over time.
The German data set provided by the German Federal Statistical Office (destatis) is called ''Erhebung zur Nutzung von Informations-und Kommunikationstechnologien in Unternehmen'' (henceforth ICT survey). 8 Aside from  Schivardi and Schmitz (2020)  and  Duso and Schiersch (2022) , this paper is among the first to exploit this data set for firm-level analyses. Information on cloud adoption pertains to the years 2014 and 2016. In order to locate firms in municipalities, we match the administrative ICT survey with the German business registry (Unternehmensregister), which, in addition to regional identifiers, contains information on the firms' industry affiliation, sales, number of employees, and firm age. Data on the GRW programme has been acquired through the German Federal Office of Economic Affairs and Export Control (BAFA) as well as the German Federal Ministry for Economic Affairs and Energy (BMWi).
Discussion The econometric results presented in Section 5 suggest that the public funding scheme GRW discourages firms to adopt cloud computing services. Since cloud computing services are associated with various benefits at the firm and the total economy level, this is a prime example for an unintended policy effect. Due to the low number of firms in the data and the low overlap of firms in the two survey years, our estimation strategy is restricted to showing controlled correlations which should not be interpreted as causal. However, the hypothesis of the unintended policy effect of disincentivizing cloud usage is strengthened by the fact that we do not find significant correlations between the eligibility of the GRW funding and the employment of IT-staff (see Table  5 ).
On top, there is usually little bargaining about rents suggesting that asking rents likely reflect the market well (see  Waltl, 2018). We assess both price and volume changes causally linked to the rent freeze. Therefore, we make use of a comprehensive commercial data source: Empirica Systeme that pools several commonly used German online rental marketplaces. The coverage is convincingly representative for all publicly advertised rental objects. On top, we match individual advertisements to administrative data and use media data for supplemental analyses. We document a remarkable immediate aggregate drop of 7-11% in advertised rent prices, which we causally link to the rent freeze. While co-movements between sales and rent prices had been rather the norm, the two indices follow opposing trends ever since the rent freeze's enactment, potentially hinting towards a substitution effect between sectors.
National and International Resonance Within Germany, the rent freeze attained lots of public attention: Figure  2  plots the number of occurrences of the word Mietendeckel in German media between January 2018 and November 2020. The data are taken from the database GENIOS, 2  which includes about 2,200 high-quality German-speaking media with the total number of documents exceeding 500 million. For the first time, the Mietendeckel was mentioned a few times in 2013. However, it is only in early 2019 that the number of occurrences becomes non-negligible. The topic was most prominently discussed between announcement and enactment of the new policy. Moreover, the example of Berlin inspired people in other parts of Germany and even internationally to request similar regulations in their own cities or municipalities.
Data We assess the immediate impact of the policies on advertised asking rents. For this purpose, we use online sale and rent advertisements collected and processed by Empirica Systeme. 15 The platform gathers ample information on all types of apartments and houses on the market by pooling a rich set of real estate information providers. Also, it includes various dwelling characteristics and, importantly, precise information on location obtained by geo-coding exact addresses mentioned in the ads. 16 e exclude statistical outliers (properties older than 300 years) from our estimation sample as well as not yet build but already advertised units. In total, we exclude eight observations leaving 74,657 in the full estimation sample. These detailed breakdowns are reported as a part of comprehensive robustness and plausibility checks in section 6.
For comparability across seasons, the trade volume for usual activity between 23 February and 30 June are included for the two preceding years 2018 and 2019. For the calculations, all ads fulfilling our selection procedure described in section 4 are considered. The data are visualized in Figure  7. Repeating the same exercise for sales advertisements, we also observe a lower number of advertisements in the POST period, yet changes are less pronounced and not significant. The volume can also vary across seasons. Hence, we additionally compare the volume in the 2020 POST-treatment period to the exactly same time span in 2018 and 2019. The transaction volume in the two preceding years is indistinguishable from that in the 2020 PRE and TRANS periods. Hence, the volume effect we document here is a unique feature unambiguously linked to the rent freeze.
Acknowledging these shortcomings, we compute deviations (in EUR) conservatively via In other words, we include a potential error margin of 20% as such kind of over-shooting is explicitly allowed. Overall, we hence are likely to identify the upper bound of the number of dwellings exceeding the maximum allowed rent. Also, the amount of mismatches in EUR is, per construction, an upper bound. We compute deviations separately for the three different periods.
The various steps are summarized below. Detailed information on the development process and results can be found in the GERONTE Zenodo community  [9, 10]. The process started with multiple scoping and systematic reviews. In the majority of cases, literature alone could not answer the questions and, therefore, additional input was sought from a healthcare professionals expert panel. The panel was composed for GERONTE and consisted of advanced practice nurses working in oncology, geriatricians, general practitioners, and cancer specialists with a special interest in older patients. Furthermore, patient and informal caregiver recommendations on the design of the pathway were gathered during interviews and a focus group. After receiving input, a digital live expert meeting was held with the expert panel to reach consensus on what the various components of the care pathway had to look like.
The GERONTE Care Pathway Fig. 1  describes the GERONTE care pathway, which is subdivided in four phases. It starts with a baseline patient evaluation, in which both oncologic and non-oncologic information is collected. A dedicated Advanced Practice Nurse (APN) is the first point of contact for the patient and caregiver, and acts as case-manager. The evaluation of patients includes a geriatric assessment (GA) by a geriatric expert, discussion of patient priorities regarding treatment outcomes, assessment of the severity and impact of comorbidities, and input from the general practitioner. All these data will be collected in an online healthcare professional's dashboard that was made to create a clear overview of the individual patient's health status and to summarize the oncologic information.
The latter then deploys "claim" and "challenge" smart contracts whenever an agent acts on this SPRIG instance. These contracts are used to lock the corresponding bounty/stake from the users' wallets. They also record all the information needed for subsequent settlements, making payments to the users' wallets according to the rules of the SPRIG protocol. Another family of smart contracts is needed to aggregate the verdicts of the machine-level proof oracles. • Oracles: these are computers which are on the lookout for machine level proof claims and use a formal verification program (e. g. , Isabel or Lean) in order to verify the proposed proof and submit their verdict. The decentralization of such oracles can be done using layer 2 blockchains or a protocol such as Truebit  [Teutsch and Reitwiessner 2019 ].
A descriptive look at staleness We employ a dataset whose constituents are the 250 most liquid (in terms of average transaction volume during the period considered) NYSE-listed stocks. We are focusing on large and liquid stocks, i. e. , the universe of stocks which should be affected by staleness the least. Because our goal is to illustrate the informational content of staleness, our emphasis on the most liquid NYSE-listed stocks leads to conservative implications and is, therefore, economically revealing about the relevance of staleness, in general. For each stock, we have trades from January 2006 to December 2014. Idiosyncratic and systematic staleness are computed, for each day, using 10-second returns constructed from transaction prices using previous tick interpolation. The number of 10-second returns 𝑛 per day is, therefore, 2,340 and the number of days 𝑁 𝐷 is 2,265.
acknowledges partial support through the grant  RBSI14DDNN  '' A new measure of liquidity '' financed within the program SIR of the  Italian Ministry of Education and Research. Davide Pirino  and  Roberto Renò  acknowledge support from the project  HiDEA  ( Advanced Econometric Methods for High-Frequency Data ) financed by the  Italian Ministry of Education, University and Research  under the program  PRIN 2017 ,  Prot. 2017RSMPZZ. Roberto Renò  is also grateful to the  Institut Europlace de Finance Louis Bachelier  for the 2013  EIF Grant  ''A  new measure of liquidity in financial markets ''. All errors are our own.
MCDM approaches could be beneficial and useful in assisting decision-makers, providing more objective decisions, especially when combined with machine learning algorithms. Historical research on football player selection and performance prediction oscillated between the traditional coaching experience and data-driven approaches. MCDM methods have gained traction in recent years because they provide a multi-criteria perspective for informed decision making  (Dadelo et al. , 2014). Simultaneously, machine learning based on large datasets may provide granular predictions of player performance using models, such as neural networks and random forests  (Azeman et al. , 2021). The potential fusion of MCDM for selection and machine learning for predictive insights remains underexplored area of research.