This folder contains the materials for my analyses of a publicly available video from The Daily Moth, including a link to the original video, the clip that I selected, and the .eaf file containing my annotations. To access the annotation file, please use ELAN, available for free download here: https://archive.mpi.nl/tla/elan/download My calculations are available here in three formats: PDF.csv files - one file for each sheetLink to a view-only version of my Google Sheets file

Dozens of missing epochs in the monthly gravity product of the satellite mission Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission greatly inhibit the complete analysis and full utilization of the data. Despite previous attempts to handle this problem, a general all-purpose gap-filling solution is still lacking. Here we propose a non-parametric, data-adaptive and easy-to-implement approach - composed of the Singular Spectrum Analysis (SSA) gap-filling technique, cross-validation, and spectral testing for significant components - to produce reasonable gap-filling results in the form of spherical harmonic coefficients (SHCs). We demonstrate that this approach is adept at inferring missing data from long-term and oscillatory changes extracted from available observations. A comparison in the spectral domain reveals that the gap-filling result resembles the product of GRACE missions below spherical harmonic degree 30 very well. As the degree increases above 30, the amplitude per degree of the gap-filling result decreases more rapidly than that of GRACE/GRACE-FO SHCs, showing effective suppression of noise. As a result, our approach can reduce noise in the oceans without sacrificing resolutions on land. The gap filling dataset is stored in the “SSA_filing/" folder. Each file represents a monthly result in the form of spherical harmonics. The data format follows the convention of the site ftp://isdcftp.gfz-potsdam.de/grace/. Low degree corrections (degree-1, C20, C30) have been made. The code to generate the dataset is located in the “code_share/“ folder, with an example for C30. The model-based Greenland mass balance result for data validation (results given in the paper) is provided in the "Greenland_SMB-D.txt” file.

Dataset description: The General Regionally Annotated Corpus of Ukrainian (GRAC, Shvedova et al. 2017-2024, uacorpus.org) was consulted to collect data for further analysis concerning the distribution of Singular vs. Plural verb forms in the target bahato construction. GRAC is a Sketch Engine corpus of over 1.8 billion words, representing texts from over 30,000 authors created between 1816 and 2023. This corpus is designed to serve as source material for linguistic research on Standard Ukrainian. Our data was collected during the month of February 2024. We extracted and annotated 28,491 examples of the bahato construction. An additional set of examples was collected from the Russian National Corpus (ruscorpora.ru) during the month of August 2024 to provide comparison with the Russian mnogo construction. For this purpose, 6,612 examples were extracted and annotated for word order and Singular vs. Plural verb agreement. Both the Ukrainian and the Russian data are included in this dataset, along with the R scripts used to analyze this data. Article abstract: We reveal an ongoing language change in Ukrainian involving a construction with a subject comprised of the indefinite quantifier багато ‘many’ modifying a noun phrase in the Genitive Plural. Number agreement on the verb varies, allowing both Singular (in 69.1% of attestations) and Plural (in 30.9% of attestations). Based on statistical analysis of corpus data, we investigate the influence of the factors of year of creation, word order of subject and verb, and animacy of the subject on the choice of verb number. We find that, while all combinations of word order and animacy are robustly attested, VS word order and inanimate subjects tend to prefer Singular, whereas SV word order and animate subjects tend to prefer Plural. Since about the 1950s, the proportion of Plural has been increasing, overtaking Singular in the current decade. We propose that this Singular vs. Plural variation is motivated by the human embodied experience of construing a group of items as either a homogeneous mass (and therefore Singular) or a multiplicity of individuals (and therefore Plural). This proposal is supported by the identification of micro-constructions that prefer Singular and show reduced individuation of human beings.

This folder contains the files used in the English analyses of my study (i.e., my secondary analysis). As described in my dissertation, the original data is shared here with permission from Dr. James Pennebaker.Each file is shared in one or more of the formats listed below, as appropriate:PDF.csv files (one file for each sheet)Link to my Google Sheets file

Many researchers seem to think that construction grammar posits the existence of just wholly idiosyncratic constructions or form-meaning pairings. However, this idea demonstrates a deep misunderstanding of the approach, since constructions rarely emerge sui generis. Rather, construction grammar aims to balance the fact that some linguistic uses cannot be fully predicted from other well-established uses, with the fact that extensions of a construction, while not predictable, are motivated by other senses in the constructional network. This study illustrates this tenet of constructional approaches to language by providing an analysis of the Spanish completive reflexive marker se. In order to identify the different senses of the completive se-construction I used data from the Spanish corpus CREA (Corpus de Referencia del Español Actual, http://corpus.rae.es/creanet.html). Given the large size of the corpus (200 million words), the frequency search—which is merely indicative—was arbitrarily limited to constructions in which the verb appeared in 3rd person singular and was directly followed by a direct object headed by the determined articles el ‘the’ (masculine) or la ‘the’ (feminine) in singular. The data set includes all the instances of the completive reflexive found in the sample described above.

This record contains all the raw data in the paper Nature Communications volume 12, Article number: 3994 (2021). Constrained by the Nielsen-Ninomiya no-go theorem, in all so-far experimentally determined Weyl semimetals (WSMs) the Weyl points (WPs) always appear in pairs in the momentum space with no exception. As a consequence, Fermi arcs occur on surfaces which connect the projections of the WPs with opposite chiral charges. However, this situation can be circumvented in the case of unpaired WP, without relevant surface Fermi arc connecting its surface projection, appearing singularly, while its Berry curvature field is absorbed by nontrivial charged nodal walls. Here, combining angle-resolved photoemission spectroscopy with density functional theory calculations, we show experimentally that a singular Weyl point emerges in PtGa at the center of the Brillouin zone (BZ), which is surrounded by closed Weyl nodal walls located at the BZ boundaries and there is no Fermi arc connecting its surface projection. Our results reveal that nontrivial band crossings of different dimensionalities can emerge concomitantly in condensed matter, while their coexistence ensures the net topological charge of different dimensional topological objects to be zero. Our observation extends the applicable range of the original Nielsen-Ninomiya no-go theorem which was derived from zero dimensional paired WPs with opposite chirality. The ARPES data, calculated band structure, and related documents are attached in this record.

This dataset accompanies a manuscript submission to Water Resources Research. The data herein consists of all of the hourly meteorological forcing variables needed to execute the iSnobal physically based snow model over the Tuolumne River Basin in the Sierra Nevada of California for water years 2013 to 2016. The variables are presented as vectors and include (1) air temperature, (2) relative humidity, (3) computed dew point temperature, (4) computed vapor pressure, (5) precipitation mass, (6) wind speed, (7) wind direction, (8) incoming solar radiation, and (9) a computed cloud factor product. Each variable has been through rigorous quality assurance and quality control to ensure that the data is model-ready. That is, there are no temporal data gaps over the total model domain, yet measurements from singular meteorological sites can go offline from time to time. Resources in this dataset:Resource Title: Dataset: Direct insertion of NASA Airborne Snow Observatory-derived snow depth time-series into the iSnobal energy balance snow model (Zenodo). File Name: Web Page, url: https://zenodo.org/record/1228400#.W8YDzGhKiUk The file structure is organized as follows: ASO_50m_depth_surfaces - This folder contains the Airborne Snow Observatory lidar-derived snow depth products aggregated to 50m gridded spatial resolution. Each file is titled with a date such as ‘TBYYYYMMDD_SUPERsnow_depth.asc’. The coordinates are in UTM zone 11N and use the WGS84 coordinate system. static_grids Static grids are used in each of the subsequent folders and are not changed between years. init0000.ipw Initialization file to begin the model run. Contains the digital elevation model in band 1, surface roughness raster in band 2, and zeroed images of snow properties in bands 3-7. maxus.nc netCDF file of 72 separate images of maximum upwind slope for all upwind directions from 0 (north) to 355 degrees in 5-degree increments. Derived using Adam Winstral’s Sx algorithm. tuolx_dem_50m.ipw Digital elevation model from ASO snow-free acquisition aggregated to 50m gridded spatial resolution. Same information as band 1 in the init0000.ipw file. tuolx_hetchy_mask_50m.ipw Basin mask of the Tuolumne River Basin above Hetch Hetchy Reservoir. Out-of-basin cells denoted as 0, and in-basin cells denoted as 1. tuolx_vegheight_50m.ipw Vegetation height raster in meters. Derived from NLCD dataset of vegetation type.. tuolx_vegk_50m.ipw Emissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type. tuolx_vegnlcd_50m.ipw Vegetation type from the National Land Cover Database. tuolx_vegtau_50m.ipw Fractional transmissivity of the vegetation canopy. Derived from NLCD dataset of vegetation type. The directories for each water year contain the configuration file for that year along with the vector meteorological data from measurement sites and site metadata in .csv format. wy2013 wy2014 wy2015 wy2016 backup_config.ini air_temp.csv cloud_factor.csv metadata.csv precip.csv vapor_pressure.csv wind_direction.csv wind_speed.csv

In the wild, hosts often encounter and must respond to novel pathogens, that is pathogens that they have not encountered in recent evolutionary history, and therefore are not adapted to. How hosts respond to these novel pathogens and the outcome of such infections can be shaped by the host’s evolutionary history, especially by how well adapted the host is to its native pathogens, that is pathogens they have evolved with. Host adaptation to one pathogen can either increase its susceptibility to a novel pathogen, due to specialization of immune defenses and trade-offs between different arms of the immune system or can decrease susceptibility to novel pathogens by virtue of cross-resistance. Using laboratory Drosophila melanogaster populations, we explore if hosts experimentally adapted to survive infection challenges by a particular bacterial pathogen are also better at surviving infection challenges by novel bacterial pathogens. We found that such hosts can survive infection challenges by multiple novel pathogens, with the expanse of cross-resistance determined by the identity of the native pathogen and sex of the host. Therefore, we demonstrate that cross-resistance can evolve in host populations by virtue of adaptation to a single pathogen. This observation has important ecological consequences, especially in the modern era where spillover of novel pathogens is a common occurrence due to various factors, including climate change. Methods A. Details of fly populations

EPN selection regime: The EPN selection regime consists of three populations (E, P, and N), each having four blocks (1-4). E populations are subjected to infection with bacteria Enterococcus faecalis every generation; P populations are sham-infected controls; N populations are uninfected controls. Populations within each block (viz. E1, P1, and N1) share a common ancestor. Blocks serve as both evolutionary and experimental/statistical replicates.

IUS selection regime: The IUS selection regime consists of three populations (I, S, and U), each having four blocks (1-4). I populations are subjected to infection with bacteria Pseudomonas entomophila every generation; S populations are sham-infected controls; U populations are uninfected controls. Populations within each block (viz. I1, S1, and U1) share a common ancestor. Blocks serve as both evolutionary and experimental/statistical replicates.

B. Response to selection experiment

EPN selection regime: Flies from all 12 populations (3 populations X 4 blocks) were randomly distributed into 3 treatments: a. infected with bacteria Enterococcus faecalis (sample size = 200 males and 200 females), b. sham-infected (sample size = 100 males and 100 females), and c. uninfected (sample size = 100 males and 100 females). Post treatment flies were housed in cages (one cage per population per treatment per block), sexes being housed together. Mortality was recorded at regular intervals till 96 hours post treatment; flies alive at 96 hours were right censored. Experient was carried out after 35 generations of forward selection.

IUS selection regime: Flies from all 12 populations (3 populations X 4 blocks) were randomly distributed into 2 treatments:

a. infected with bacteria Pseudomonas entomophila (sample size = 50 males and 50 females), and b. sham-infected (sample size = 50 males and 50 females). Post treatment flies were housed in cages (one cage per population per treatment per block), sexes being housed together. Mortality was recorded at regular intervals till 96 hours post treatment; flies alive at 96 hours were right censored. Experiment was carried out after 160 generations of forward selection.

C. Cross-resistance experiment

EPN selection regime: Flies from E and P populations (all four blocks) were randomly distributed into 7 treatments: a. infected with bacteria Bacillus thuringiensis (Bt;sample size = 50 males and 50 females), b. infected with bacteria Micrococcus luteus (Ml; sample size = 50 males and 50 females), c. infected with bacteria Staphylococcus succinus (Ss; sample size = 50 males and 50 females), d. infected with bacteria Erwinia c. carotovora (Ecc; sample size = 50 males and 50 females), e. infected with bacteria Pseudomonas entomophila (Pe; sample size = 50 males and 50 females), f. infected with bacteria Providencia rettgeri (Pr; sample size = 50 males and 50 females), and g. sham-infected (sample size = 50 males and 50 females). Post treatment flies were housed in cages (one cage per population per treatment per block), sexes being housed together. Mortality was recorded at regular intervals till 96 hours post treatment; flies alive at 96 hours were right censored. Experient was carried out after 40 generations of forward selection.

IUS selection regime: Flies from I and S populations (all four blocks) were randomly distributed into 7 treatments:

a. infected with bacteria Bacillus thuringiensis (Bt;sample size = 50 males and 50 females), b. infected with bacteria Micrococcus luteus (Ml; sample size = 50 males and 50 females), c. infected with bacteria Staphylococcus succinus (Ss; sample size = 50 males and 50 females), d. infected with bacteria Enterococcus faecalis (Ef; sample size = 50 males and 50 females), e. infected with bacteria Erwinia c. carotovora (Ecc; sample size = 50 males and 50 females), f. infected with bacteria Providencia rettgeri (Pr; sample size = 50 males and 50 females), and g. sham-infected (sample size = 50 males and 50 females). Post treatment flies were housed in cages (one cage per population per treatment per block), sexes being housed together. Mortality was recorded at regular intervals till 96 hours post treatment; flies alive at 96 hours were right censored. Experient was carried out after 160 generations of forward selection.

This folder contains the files used in the ASL analyses of my study: All of the data and calculations for my primary analysis, my exploratory analyses (except the one using a video from The Daily Moth, which can be found in a separate folder), and the ASL portions of my secondary analysis. As described in my dissertation, I am not sharing the original video files in order to protect the privacy of those who participated in my study.Each file is shared in one or more of the formats listed below, as appropriate:PDF.csv files (one file for each sheet)Link to my Google Sheets file

This dataset contains WebMercator tiles which contain gray-scale shaded relief (hill shades), and nothing else. The tiles have a resolution of 256×256px, suitable for web mapping libraries such as Leaflet. The hill shades are generated from SRTM altitude data, which cover the land area between 60° northern and 58° southern latitude, and which lies in the public domain. Map material without political or infrastructural features can be desirable, for example, in use cases where historical data is visualized on a map. The concrete motivation for generating this map material was the Dhimmis & Muslims project (project page, home page, GitHub, DaRUS dataset), which analyzed peaceful coexistence of religious groups in the medieval Middle East. A particular goal with creating the dataset was to have map material available under a permissive license for screenshots and publications, instead of relying on proprietary mapping services such as Mapbox. Teaser image: The hillshades of Cyprus on zoom level 9. This image is hosted externally by GitHub, but is also present in the repository as teaser.png. Coverage. The dataset covers zoom level 0 (entire world in one tile) to 12 (entire world in 4096×4096 tiles). The total size of the dataset is 22,369,621 tiles. However, of those, 19,753,304 tiles (88.3%) are empty, either because the landscape there is fully flat (i.e., on water), or because they lie fully outside the latitude range covered by the SRTM altitude data. The empty tiles are not stored. Instead, a singular placeholder file is stored in the repository, alongside a list of the empty tiles. During extraction, the placeholder empty tile can be symbolically linked in the file system to all the places where it is needed. The total size of the non-empty tiles is about 103GB. Files. Besides the placeholder file and the list of empty tiles, the repository also contains a manifest file. This file lists all non-empty tiles by the ZIP file they are contained in. The tiles themselves are grouped into ZIP files by the following schema: All tiles from levels 0 to 5 are contained in one ZIP file. All tiles of level N, N≥6 are contained in a ZIP file which is named after the tile of level N-6 (block level) that contains the tile in question, named tiles_.zip. Hence, all tiles of level 6 are contained in a singular ZIP file named tiles_6_0_0_0.zip. The tiles of level 7 are split up into four group ZIP files named tiles_7_1_{0,1}_{0,1}.zip, the tiles of level 8 into 16 group ZIP files named tiles_8_2_{0..3}_{0..3}.zip, and so on. Both the manifest file and the commands to generate the distribution of tiles on ZIP files can be generated using the linked software repository. Usage. The tile ZIP files can be downloaded and extracted. By serving the extracted directory structure in a web server, a slippy map tile server can be created. The linked software repository also contains a command-line utility that generates the required shell commands to download the ZIP files, extract them, and softlink (ln -s) the empty tiles to the appropriate places. This command-line utility can also optionally read in a GeoJSON file of an area of interest. In this case, only tiles within that area are downloaded in a higher zoom level, whereas tiles completely outside the area are only downloaded to a lower zoom level; both zoom levels are also configurable. See the documentation in the repository and the command-line utility’s help (-h) output for more details.

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Analysis of ‘Delta Levees Centerlines 2007’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/aa82c2f4-f621-4732-8615-ae7747653972 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Detailed center lines of the Sacramento-San Joaquin levee systems, and broken into singular segments of consistent-attribute sets. This version created from mapping from the 2007 Delta LIDAR. The use of high-resolution LiDAR, and the products derived from it, allow for levee anatomy to be captured for the surveyed area. The resulting data allows for the levee crown sections to be isolated and collapsed to a centerline, detailing the route of the levee system. This data can further be used for levee maintenance and management, flood modeling and prediction, as well as levee inventories. The data are therefore mostly the structural center lines of the levees, with some minor modifications as warranted. In the Delta Anatomy Mapping Project, all levee anatomies were delineated using slope grids built from available 2007 Delta LIDAR data points. LIDAR data points were converted to digital elevation models and subsequently into slope grids. Thresholds were identified that capture the levee crown, levee landside, levee waterside, ramps and toe ditches. Visual interpretations of slope thresholds were used in conjunction with heads-up digitizing to maintain smooth boundaries at a scale of 1:550. The delineation thresholds were derived from a combination of mapping scale, slope grid resolution and slope thresholds used for each anatomy classification. All anatomy has gone through internal quality control processes to ensure a minimum accuracy of +/- 3 feet. Anatomy data was further reviewed and tested by DWR for compliance with an interpretive mapping standard of 80% accuracy.Once the levee anatomy was created and accepted, isolation and export of the levee crown was used in conjucture with ET Geowizards to collapse the crown to a singular centerline which details the levee route. This data depicts the levee anatomy at the time of the LiDAR survey (2007) and are only accurate for that time. Users should be aware that temporal changes may have occurred since this data set was created and some parts of this data may no longer represent actual surface conditions. Changes in some linework and attribution were performed by CA DWR Division of Engineering in September, 2018, and current version was posted to DWR GIS Atlas at that time. This data set was produced by joint effort of DWR and Chico State University. Data were originally developed and supplied by the Geographic Information Center at California State University at Chico, under contract to California Department of Water Resources. DWR subsequently modified the linework in a few places along with the attribution for various levee characteristics of interest. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.1, dated September 11, 2019. DWR makes no warranties or guarantees — either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be sent to GIS@water.ca.gov

--- Original source retains full ownership of the source dataset ---

This site contains the widefield imaging datasets from the publication Cortical State Fluctuations during Sensory Decision Making, by Jacobs et al in Current Biology.This data is from the behavioural tasks described in the publication, and is in a compressed SVD format (see Methods in the publication for more details). The companion code is designed to take the data in this format.The datasets provided here contain the top 500 singular values, which is how the data in the publication was analysed, as this was found to sufficiently capture the data. The data contaning up to 2000 singular values can be shared on request.The timestamps of the datasets here are not all aligned with the behavioural datasets; the companion code takes care of this.The data is organised by experimental subject; most subjects were recorded from on multiple days, which form subfolders within the subject folder. Within a day, there may have been several experiments, which again form subfolders within the day folder. The companion code expects this data organisation.The companion code is available at: https://github.com/eakjacobs/Jacobs_et_al_CurrentBiologyFor more information and links to the behavioural and pupil datasets, please follow this link: https://doi.org/10.6084/m9.figshare.13084805The research article can be found (freely available) at https://www.cell.com/current-biology/fulltext/S0960-9822(20)31437-8

Biodiversity monitoring has entered an era of ‘big data’, exemplified by a near-continuous collection of sounds, images, chemical and other signals from organisms in diverse ecosystems. Such data streams have the potential to help identify new threats, assess the effectiveness of conservation interventions, as well as generate new ecological insights. However, appropriate analytical methods are often still missing, particularly with respect to characterizing cyclical temporal patterns. Here, we present a framework for characterizing and analysing ecological responses that represent nonstationary, complex temporal patterns and demonstrate the value of using Fourier transforms to decorrelate continuous data points. In our example, we use a framework based on three approaches (spectral analysis, magnitude squared coherence, and principal component analysis) to characterize differences in tropical forest soundscapes within and across sites and seasons in Gabon. By reconstructing the underly..., We used acoustic data collected from eight sites in the Ogooué-Ivindo province of Gabon to demonstrate how time-series approaches can be leveraged to compare cyclical trends within and between groups of sites. All soundscape sampling occurred in closed, Gabonese rainforest with minimal habitat disturbance for at least twenty years. First, we sampled the soundscape in the rainy season at four sites within Ivindo National Park, between February 19th and March 2nd 2021 (referred to as the Ivindo sites). Second, we sampled the soundscape in the dry season at four sites near Massaha between July 17th and July 23rd 2021 (hereafter referred to as the Massaha sites, about 15km from the Ivindo sites). At the time of sampling, the Massaha sites were located within a logging concession but no logging activity had commenced and there was an ongoing petition for the area to be re-designated as a community conservation area. Additionally, we used one site from the Lope National Park. At each site, we..., , # Time series methods for the analysis of soundscapes and other cyclical ecological data

https://doi.org/10.5061/dryad.xpnvx0kn6

Example dataset used for demonstrating the methods in "Times Series Methods for the Analysis of Soundscapes and Other Cyclical Ecological Data" Methods in Ecology and Evolution. This data represents soundscape data for eight tropical forest sites in Gabon collected between February and July 2021. See methods for more information. The soundscape is characterized using the soundscape index Power Minus Noise (PMN) for 256 frequency bins between 0-11 kHz for each minute of the day. PMN is a proxy for acoustic activity and provides a relatively simple index to demonstrate our methodological approach.

Description of the data and file structure

Column names:

• Site: Site code (factor; 8 levels)
• DatetimeFinal: Date time information (datatime object)
• TotPMN: Values for characterizing the soundscape, Power minus noi...

Let me know who is your pension scheme administrator – is it outsourced? If so to whom? Question 2 Please tell me what data you use to monitor the performance of your retirement processing. In particular I expect to see end-to-end process lead times, error rates, staffing levels and rates for both incoming complaints and complaint resolution. If you set targets for these items, please send those too. Question 3 Please send me the results of these data items over the last 2 years in a format that allows trend analysis (i.e. to assess I there are trend improvements or deteriorations). Question 4 Your board minutes of August 23 mention a “Quality Assurance KPI” which was surprisingly exceeded through out the quarter. Please send me the last 2 yrs results for this KPI and the target against which it is assessed. It is also surprising that the KPI is referred to in the singular. Please send me the same data on any other KPIs which you use to assess process performance, either generally or in the retirement process in particular. Question 5 I suspect that the overall performance of your management is poor, and would like to see any data you have on process performance over the last 2 years (so I can see trends over time). This should include elapse times for the retirement process, error rates, staffing levels and numbers or rates of complaints. This is an official request under the Freedom of information act 2000. Question 6 I would like a copy of your management structure chart so I know who is responsible for what. In particular who do the Trustees hold accountable for the performance of your Retirement processes at a senior level. This is also an official request under the Freedom of information act 2000. Your request was received on 19 April 2023, and I am dealing with it under the terms of the Freedom of Information Act 2000. Questions 5 and 6 were from FOI-01728 which was merged with FOI-01749 on 22 February 2024. Clarification relating to questions 2 to 5 was received on 6 March, and I have summarised this below: Question 2 Both please [formal and informal complaints], shown separately. Also data on how many complaints end up with the Ombudsman would be useful. Again all results should be shown categorised by time (whether weekly or monthly) so I can see trends over time. This should not be too onerous as your management should be interested in this and monitoring it routinely. Question 3 As a minimum you can send me quarterly data. I'm just interested in the processing of initial retirement claims (i.e. following the submission of an AW8) not routine monthly payments. Question 4 I'm just interested in the processing of initial retirement claims (i.e. following the submission of an AW8) not routine monthly payments. Question 5 Specifically what I want to see (and what your management should monitor) is: a) Average elapse time from the receipt of a first AW8 to the successful closure of a retirement claim (i.e. after all errors have been reworked). The average could be collated daily, weekly or monthly but should be frequent enough to show time trends so your management can identify if things are getting better or worse. This is called an end-to-end process metric and will best reflect your customers' experience. I am not generally interested in things like initial response times to communications (e.g. holding letters) or compliance against service standards unless they reflect customer experience. b) Average rates of error in processing could be measured in a variety of ways depending on your MI systems. So for example you may measure time spent by staff on activities and categorise it into say "basic work" and "rework". Or you may have error rates, say on calculations i.e. "how often did we need to calculate retirement benefits until we got it right?" I'm conscious a lot of rework is also inflicted on you by errors in the contribution record from employers, so I'd be interested in any metrics you have on the errors you find in the contribution record and what the impact is on your workloads; again time trend data would be best to demonstrate if it's getting better or worse. c) Metrics on staffing levels should be easy - something to show if you have enough staff (e.g. unfulfilled vacancies over time) and if they are overworked (e.g. overtime records, staff sickness, staff satisfaction surveys)

In 2014 the Office for National Statistics (ONS) designed the London Business Survey (LBS), on behalf of the London Enterprise Panel and the GLA. In 2021 the GLA requested an alternative to the London Business Survey, to provide a snapshot of businesses in London without the use of a bespoke survey. As such the ONS - London team has compiled various published datasets from multiple sources into a singular workbook. It is important to note that all data in this workbook is already in the public realm and has been compiled into this format for the convenience of users. As part of this project some bespoke adhoc data requests were produced to best match the original questions from the 2014 survey questions (where possible). Data is provided at the London or local authority level where it is published, however there are some cases where data is limited to the national level. Data is provided for the most recent year available. This varies by dataset.

This dataset consists of template sentences associating first names ([NAME]) with third-person singular pronouns ([PRONOUN]), e.g., [NAME] asked , not sounding as if [PRONOUN] cared about the answer . after all , [NAME] was the same as [PRONOUN] 'd always been . there were moments when [NAME] was soft , when [PRONOUN] seemed more like the person [PRONOUN] had been .

Usage python genter = load_dataset('aieng-lab/genter', trust_remote_code=True, split=split) split can be either train, val, test, or all.

Dataset Details Dataset Description

This dataset is a filtered version of BookCorpus containing only sentences where a first name is followed by its correct third-person singular pronoun (he/she). Based on these sentences, template sentences (masked) are created including two template keys: [NAME] and [PRONOUN]. Thus, this dataset can be used to generate various sentences with varying names (e.g., from aieng-lab/namexact) and filling in the correct pronoun for this name.

This dataset is a filtered version of BookCorpus that includes only sentences where a first name appears alongside its correct third-person singular pronoun (he/she).

From these sentences, template-based sentences (masked) are created with two template keys: [NAME] and [PRONOUN]. This design allows the dataset to generate diverse sentences by varying the names (e.g., using names from aieng-lab/namexact) and inserting the appropriate pronoun for each name.

Dataset Sources

Repository: github.com/aieng-lab/gradiend Original Data: BookCorpus

NOTE: This dataset is derived from BookCorpus, for which we do not have publication rights. Therefore, this repository only provides indices, names and pronouns referring to GENTER entries within the BookCorpus dataset on Hugging Face. By using load_dataset('aieng-lab/genter', trust_remote_code=True, split='all'), both the indices and the full BookCorpus dataset are downloaded locally. The indices are then used to construct the GENEUTRAL dataset. The initial dataset generation takes a few minutes, but subsequent loads are cached for faster access.

Dataset Structure

text: the original entry of BookCorpus masked: the masked version of text, i.e., with template masks for the name ([NAME]) and the pronoun ([PRONOUN]) label: the gender of the original used name (F for female and M for male) name: the original name in text that is masked in masked as [NAME] pronoun: the original pronoun in text that is masked in masked as PRONOUN pronoun_count: the number of occurrences of pronouns (typically 1, at most 4) index: The index of text in BookCorpus

Examples: index | text | masked | label | name | pronoun | pronoun_count ------|------|--------|-------|------|---------|-------------- 71130173 | jessica asked , not sounding as if she cared about the answer . | [NAME] asked , not sounding as if [PRONOUN] cared about the answer . | M | jessica | she | 1 17316262 | jeremy looked around and there were many people at the campsite ; then he looked down at the small keg . | [NAME] looked around and there were many people at the campsite ; then [PRONOUN] looked down at the small keg . | F | jeremy | he | 1 41606581 | tabitha did n't seem to notice as she swayed to the loud , thrashing music . | [NAME] did n't seem to notice as [PRONOUN] swayed to the loud , thrashing music . | M | tabitha | she | 1 52926749 | gerald could come in now , have a look if he wanted . | [NAME] could come in now , have a look if [PRONOUN] wanted . | F | gerald | he | 1 47875293 | chapter six as time went by , matthew found that he was no longer certain that he cared for journalism . | chapter six as time went by , [NAME] found that [PRONOUN] was no longer certain that [PRONOUN] cared for journalism . | F | matthew | he | 2 73605732 | liam tried to keep a straight face , but he could n't hold back a smile . | [NAME] tried to keep a straight face , but [PRONOUN] could n't hold back a smile . | F | liam | he | 1 31376791 | after all , ella was the same as she 'd always been . | after all , [NAME] was the same as [PRONOUN] 'd always been . | M | ella | she | 1 61942082 | seth shrugs as he hops off the bed and lands on the floor with a thud . | [NAME] shrugs as [PRONOUN] hops off the bed and lands on the floor with a thud . | F | seth | he | 1 68696573 | graham 's eyes meet mine , but i 'm sure there 's no way he remembers what he promised me several hours ago until he stands , stretching . | [NAME] 's eyes meet mine , but i 'm sure there 's no way [PRONOUN] remembers what [PRONOUN] promised me several hours ago until [PRONOUN] stands , stretching . | F | graham | he | 3 28923447 | grief tore through me-the kind i had n't known would be possible to feel again , because i had felt this when i 'd held caleb as he died . | grief tore through me-the kind i had n't known would be possible to feel again , because i had felt this when i 'd held [NAME] as [PRONOUN] died . | F | caleb | he | 1

Dataset Creation Curation Rationale

For the training of a gender bias GRADIEND model, a diverse dataset associating first names with both, its factual and counterfactual pronoun associations, to assess gender-related gradient information.

Source Data

The dataset is derived from BookCorpus by filtering it and extracting the template structure.

We selected BookCorpus as foundational dataset due to its focus on fictional narratives where characters are often referred to by their first names. In contrast, the English Wikipedia, also commonly used for the training of transformer models, was less suitable for our purposes. For instance, sentences like [NAME] Jackson was a musician, [PRONOUN] was a great singer may be biased towards the name Michael.

Data Collection and Processing

We filter the entries of BookCorpus and include only sentences that meet the following criteria:

Each sentence contains at least 50 characters Exactly one name of aieng-lab/namexact is contained, ensuringa correct name match. No other names from a larger name dataset (aieng-lab/namextend) are included, ensuring that only a single name appears in the sentence. The correct name's gender-specific third-person pronoun (he or she) is included at least once. All occurrences of the pronoun appear after the name in the sentence. The counterfactual pronoun does not appear in the sentence. The sentence excludes gender-specific reflexive pronouns (himself, herself) and possesive pronouns (his, her, him, hers) Gendered nouns (e.g., actor, actress, ...) are excluded, based on a gemdered-word dataset with 2421 entries.

This approach generated a total of 83772 sentences. To further enhance data quality, we employed s imple BERT model (bert-base-uncased) as a judge model. This model must predict the correct pronoun for selected names with high certainty, otherwise, sentences may contain noise or ambiguous terms not caught by the initial filtering. Specifically, we used 50 female and 50 male names from the (aieng-lab/namextend) train split, and a correct prediction means the correct pronoun token is predicted as the token with the highest probability in the induced Masked Language Modeling (MLM) task. Only sentences for which the judge model correctly predicts the pronoun for every test case were retrained, resulting in a total of 27031 sentences.

The data is split into training (87.5%), validation (2.5%) and test (10%) subsets.

Bias, Risks, and Limitations

Due to BookCorpus, only lower-case sentences are contained.

(:unav)...........................................

The dataset contains the multifractal spectral dimensions and singularity indices of all sampling points, as well as the necessary data for plotting multifractal spectra and singular spectral functions.

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