Non-traditional data signals from social media and employment platforms for EXCC stock analysis

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

The Cyclistic Bike Share Case Study is a capstone project for the Google Data Analytics Professional Certificate on Coursera. In this project, I will follow the data analysis process which I learned from the course: ask, prepare, process, analyze, share and act to analyze the data.

Cyclistic is a bike-share company based in Chicago that launched a successful bike-sharing program in 2016. Throughout the years, the program has expanded significantly to a fleet of 5,824 bicycles and a network of 692 geotracked stations sprawled across the city. With the large number of bicycles across numerous stations, customers can rent bikes from one station and return them to any other station within the network at their convenience. This encourages people to opt for cycling as a mode of transportation, therefore contributing to the success of Cyclistic's bike-sharing program.

Cyclistic's marketing strategy has so far focused on building general awareness and appealing to broad consumer segments. The company offers flexibile pricing plans that cater to diverse needs of users including single-ride passes, full-day passes, and annual memberships. Besides, it provides reclining bikes, hand tricycles, and cargo bikes, effectively welcoming individuals with disabilities and those who can't ride on the standard two-wheeled bicycles. Based on the company database, Cyclistic users are more likely to ride for leisure, but about 30% use them to commute to work each day. While traditional bikes remain as the popular option, around 8% of users opt for the assistive alternatives.

The company's marketing director believes that the company’s future success depends on maximizing the number of annual memberships. Therefore, as a junior data analyst, my team and I have to understand how casual riders and annual members use Cyclistic bikes differently. From these insights, we will design a new marketing strategy to convert casual riders into annual members.

Abstract

The documentation covers Enterprise Survey panel datasets that were collected in Slovenia in 2009, 2013 and 2019.

The Slovenia ES 2009 was conducted between 2008 and 2009. The Slovenia ES 2013 was conducted between March 2013 and September 2013. Finally, the Slovenia ES 2019 was conducted between December 2018 and November 2019. The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector.

As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms' experiences and enterprises' perception of the environment in which they operate.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must take its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

As it is standard for the ES, the Slovenia ES was based on the following size stratification: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Slovenia ES 2009, 2013, 2019 were selected using stratified random sampling, following the methodology explained in the Sampling Manual for Slovenia 2009 ES and for Slovenia 2013 ES, and in the Sampling Note for 2019 Slovenia ES.

Three levels of stratification were used in this country: industry, establishment size, and oblast (region). The original sample designs with specific information of the industries and regions chosen are included in the attached Excel file (Sampling Report.xls.) for Slovenia 2009 ES. For Slovenia 2013 and 2019 ES, specific information of the industries and regions chosen is described in the "The Slovenia 2013 Enterprise Surveys Data Set" and "The Slovenia 2019 Enterprise Surveys Data Set" reports respectively, Appendix E.

For the Slovenia 2009 ES, industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries, services industries, and one residual (core) sector as defined in the sampling manual. Each industry had a target of 90 interviews. For the manufacturing industries sample sizes were inflated by about 17% to account for potential non-response cases when requesting sensitive financial data and also because of likely attrition in future surveys that would affect the construction of a panel. For the other industries (residuals) sample sizes were inflated by about 12% to account for under sampling in firms in service industries.

For Slovenia 2013 ES, industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Finally, for Slovenia 2019 ES, three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Slovenia 2019 Enterprise Surveys Data Set" report, Appendix C. Industry stratification was done as follows: Manufacturing – combining all the relevant activities (ISIC Rev. 4.0 codes 10-33), Retail (ISIC 47), and Other Services (ISIC 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For Slovenia 2009 and 2013 ES, size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

For Slovenia 2009 ES, regional stratification was defined in 2 regions. These regions are Vzhodna Slovenija and Zahodna Slovenija. The Slovenia sample contains panel data. The wave 1 panel “Investment Climate Private Enterprise Survey implemented in Slovenia” consisted of 223 establishments interviewed in 2005. A total of 57 establishments have been re-interviewed in the 2008 Business Environment and Enterprise Performance Survey.

For Slovenia 2013 ES, regional stratification was defined in 2 regions (city and the surrounding business area) throughout Slovenia.

Finally, for Slovenia 2019 ES, regional stratification was done across two regions: Eastern Slovenia (NUTS code SI03) and Western Slovenia (SI04).

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as (-8). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response.

For 2009 and 2013 Slovenia ES, the survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Up to 4 attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals. Further research is needed on survey non-response in the Enterprise Surveys regarding potential introduction of bias.

For 2009, the number of contacted establishments per realized interview was 6.18. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The relatively low ratio of contacted establishments per realized interview (6.18) suggests that the main source of error in estimates in the Slovenia may be selection bias and not frame inaccuracy.

For 2013, the number of realized interviews per contacted establishment was 25%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 44%.

Finally, for 2019, the number of interviews per contacted establishments was 9.7%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The share of rejections per contact was 75.2%.

General descriptionThis dataset contains some markers of Open Science in the publications of the Chemical Biology Consortium Sweden (CBCS) between 2010 and July 2023. The sample of CBCS publications during this period consists of 188 articles. Every publication was visited manually at its DOI URL to answer the following questions.1. Is the research article an Open Access publication?2. Does the research article have a Creative Common license or a similar license?3. Does the research article contain a data availability statement?4. Did the authors submit data of their study to a repository such as EMBL, Genbank, Protein Data Bank PDB, Cambridge Crystallographic Data Centre CCDC, Dryad or a similar repository?5. Does the research article contain supplementary data?6. Do the supplementary data have a persistent identifier that makes them citable as a defined research output?VariablesThe data were compiled in a Microsoft Excel 365 document that includes the following variables.1. DOI URL of research article2. Year of publication3. Research article published with Open Access4. License for research article5. Data availability statement in article6. Supplementary data added to article7. Persistent identifier for supplementary data8. Authors submitted data to NCBI or EMBL or PDB or Dryad or CCDCVisualizationParts of the data were visualized in two figures as bar diagrams using Microsoft Excel 365. The first figure displays the number of publications during a year, the number of publications that is published with open access and the number of publications that contain a data availability statement (Figure 1). The second figure shows the number of publication sper year and how many publications contain supplementary data. This figure also shows how many of the supplementary datasets have a persistent identifier (Figure 2).File formats and softwareThe file formats used in this dataset are:.csv (Text file).docx (Microsoft Word 365 file).jpg (JPEG image file).pdf/A (Portable Document Format for archiving).png (Portable Network Graphics image file).pptx (Microsoft Power Point 365 file).txt (Text file).xlsx (Microsoft Excel 365 file)All files can be opened with Microsoft Office 365 and work likely also with the older versions Office 2019 and 2016. MD5 checksumsHere is a list of all files of this dataset and of their MD5 checksums.1. Readme.txt (MD5: 795f171be340c13d78ba8608dafb3e76)2. Manifest.txt (MD5: 46787888019a87bb9d897effdf719b71)3. Materials_and_methods.docx (MD5: 0eedaebf5c88982896bd1e0fe57849c2),4. Materials_and_methods.pdf (MD5: d314bf2bdff866f827741d7a746f063b),5. Materials_and_methods.txt (MD5: 26e7319de89285fc5c1a503d0b01d08a),6. CBCS_publications_until_date_2023_07_05.xlsx (MD5: 532fec0bd177844ac0410b98de13ca7c),7. CBCS_publications_until_date_2023_07_05.csv (MD5: 2580410623f79959c488fdfefe8b4c7b),8. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.xlsx (MD5: 9c67dd84a6b56a45e1f50a28419930e5),9. Data_from_CBCS_publications_until_date_2023_07_05_obtained_by_manual_collection.csv (MD5: fb3ac69476bfc57a8adc734b4d48ea2b),10. Aggregated_data_from_CBCS_publications_until_2023_07_05.xlsx (MD5: 6b6cbf3b9617fa8960ff15834869f793),11. Aggregated_data_from_CBCS_publications_until_2023_07_05.csv (MD5: b2b8dd36ba86629ed455ae5ad2489d6e),12. Figure_1_CBCS_publications_until_2023_07_05_Open_Access_and_data_availablitiy_statement.xlsx (MD5: 9c0422cf1bbd63ac0709324cb128410e),13. Figure_1.pptx (MD5: 55a1d12b2a9a81dca4bb7f333002f7fe),14. Image_of_figure_1.jpg (MD5: 5179f69297fbbf2eaaf7b641784617d7),15. Image_of_figure_1.png (MD5: 8ec94efc07417d69115200529b359698),16. Figure_2_CBCS_publications_until_2023_07_05_supplementary_data_and_PID_for_supplementary_data.xlsx (MD5: f5f0d6e4218e390169c7409870227a0a),17. Figure_2.pptx (MD5: 0fd4c622dc0474549df88cf37d0e9d72),18. Image_of_figure_2.jpg (MD5: c6c68b63b7320597b239316a1c15e00d),19. Image_of_figure_2.png (MD5: 24413cc7d292f468bec0ac60cbaa7809)

This is a dataset downloaded off excelbianalytics.com created off of random VBA logic. I recently performed an extensive exploratory data analysis on it and I included new columns to it, namely: Unit margin, Order year, Order month, Order weekday and Order_Ship_Days which I think can help with analysis on the data. I shared it because I thought it was a great dataset to practice analytical processes on for newbies like myself.

Spreadsheet Software Market Size And Forecast

Spreadsheet Software Market size was valued at USD 10.05 Billion in 2023 and is expected to reach USD 14.55 Billion by 2031, with a CAGR of 7.8% from 2024-2031.

Global Spreadsheet Software Market Drivers

The market drivers for the Spreadsheet Software Market can be influenced by various factors. These may include:

Increasing Data Volume: As organizations generate and collect more data, the need for efficient data analysis and management tools, such as spreadsheet software, grows. Rising Demand for Data Visualization: Users increasingly seek sophisticated tools to visualize data for better insights. Spreadsheet software can provide charts and graphs, making data interpretation easier.

Global Spreadsheet Software Market Restraints

Several factors can act as restraints or challenges for the Spreadsheet Software Market, These may include:

Market Saturation: Many organizations already use established spreadsheet software such as Microsoft Excel or Google Sheets. The reliance on these platforms can make it difficult for new entrants or alternative solutions to capture market share. High Competition: The market is highly competitive, with numerous players offering similar features and functionalities. This can lead to price wars and reduced profit margins for software providers.

Empirical and simulated datasets used in Higuera et al. (2010): Higuera, P. E., Gavin, D. G., Bartlein, P. J. & Hallett, D. J. (2010) Peak detection in sediment-charcoal records: impacts of alternative data analysis methods on fire-history interpretations. International Journal of Wildland Fire, 19, 996-1014. The single .zip archive includes the following: (1) A single Excel file for each dataset analyzed in the paper, in the (older, single-file) input format used by the CharAnalysis program (https://sites.google.com/site/charanalysis/). There are two simulated datasets, "varConstant" and "varProportional" and each one was anlyzed using four different methods (as described in the paper); the parameters and results from each method are included in the appropriately named folder. Note that the input data for the four different methods do not vary. (2) A PDF of each figures included in the paper.

The attachment includes three folders:
The first folder, Data classification (testing and training), consists of two folders (crown_radius and height), the first crown_radius folder It contains excel data of three plant functional types (PFTs) - temperate needleleaf trees (MN), temperate broadleaf trees (MB) and tropical broadleaf trees (TB), these three excel data all contain 19 soil factors data, 22 climate factors data and information such as crown_radius_m, mask, stem_diameter_cm, etc. The information in the second height folder is similar, and it corresponds to Table 1.Data summary and Figure 3 for each PFT in the article;

The second folder, Feather importance, contains two excel spreadsheets (crown_radius-FI and height-FI), the first excel spreadsheet of crown_radius-FI Feather importance containing three plant functional types (PFTs) is temperate needleleaf trees (MN), temperate broadleaf trees (MB), and tropical broadleaf trees (TB); The excel table information of the second height-FI is similar, and its information corresponds to Figure 5 and Figure S3 in the article;

The third folder "program" contains two packages (make_model1 and make_model2) and a calling program "Source program". Among them, the make_model1 package is mainly used to obtain the best parameters for selecting the model; The make_model2 package is based on the selection of the make_model1 package to further analyze the specific FI values of the factors in the best model. The Source program is used to make specific calls to the package according to the requirements.

The uploaded Excel files contain raw data for the paper. Refer to the Methods section for detailed data analysis.

Content and data sourceThis dataset contains the results of a manual analysis of Open Science markers in the publications of the Swedish Metabolomics Centre (SMC) between 2016 and 2024. It contains similar variables as the data of the "Analysis of CBCS publications for Open Access, data availability statements and persistent identifiers for supplementary data" (Kieselbach, 2023).

The sample of these publications was fetched from SciLifeLab on 5 May 2025 at the URL: https://publications.scilifelab.se/label/Swedish Metabolomics Centre (SMC)

It contains 285 articles that are the source data for the work to create this dataset. Every publication was manually visited at its DOI URL and checked for 23 variables.

Questions studiedSome of the questions that were addressed in the collection of the data are:

1. Does the article have an open license and what kind of license does it have?

2. Does the article contain research data that may have restricted access such as personal data and health data?

3. Does the article contain a data availability statement?

4. Does the article contain supplementary material that the authors added to it?

5. Does the supplementary material contain research data?

6. Does the supplementary material contain metabolomics data such as, for instance, summaries and visualizations?

7. Did the authors submit metabolomics data to MetaboLights at the EBI or to other repsoitories?

8. Did the authors submit other data to other repositories?

9. Is data available on request from the authors?

Visualization of dataThe data was compiled and visualized using Microsoft Excel 365. The visualization includes one table that gives a general overview of the dataset, and four figures that show some results of the analysis.

Figure 1. Percentage of publications between 2016 and 2024 with an Open Access License and with a data availability statement.

Figure 2. Submissions to repositories between 2016 and 2024.

Figure 3. Percentage of publications that contained supplementary material and if this supplementary material contained research data and metabolomics data.

Figure 4. Repositories used by the authors between 2016 and 2024.

List of variables1. Year of Publication (answer: year)

1. Date of Publication (answer: date)

2. DOI (answer: DOI)

3. DOI URL (answer: DOI URL)

4. Research article (answer: Yes or No)

5. Access to article without paywall (answer: Yes or No)

6. License for research article (answer: Name of the license or No)

7. Data with restricted access (answer: Yes or No)

8. Data availability statement in article (answer: Yes or No)

9. Supplementary material added to article (answer: Yes or No)

10. Access to supplementary material without paywall (answer: Yes or No)

11. Supplementary material contains research data (answer: Yes or No)

12. Supplementary data contains metabolomics data (answer: Yes or No)

13. Persistent identifier for supplementary data (answer: Yes or No)

14. Source data added to the article (answer: Yes or No)

15. Source data contain metabolomics data (answer: Yes or No)

16. Authors submitted metabolomics data to MetaboLights (answer: Yes or No)

17. Authors submitted metabolomics data to another repository (answer: name of the repository or No)

18. Authors submitted other data to a repository (answer: name of the repository or No)

19. Authors submitted other data to a second repository (answer: name of the repository or No)

20. Authors submitted other data to a third repository (answer: name of the repository or No)

21. Authors submitted code to a repository (answer: name of the repository or No)

22. Data available on request from the authors (answer: Yes or No)

Variables that are available in the source data1. Title of article

1. Authors

2. Journal

3. Year

4. (Date) Published

5. (Date) E-published

6. Volume

7. Issue

8. Pages

9. DOI

10. PMID

11. Labels

12. Qualifiers

13. IUID

14. URL

15. DOI URL of research article

16. PubMed URL of research article

File formats and softwareThe file formats used in this dataset are:

.csv (Text file)

.jpg (JPEG image file)

.pdf/A (Portable Document Format for archiving)

.txt (Text file)

.xlsx (Microsoft Excel 365 file)

All files can be opened with Microsoft Office 365.

ReferenceKieselbach, Theresa (2023). Analysis of CBCS publications for Open Access, data availability statements and persistent identifiers for supplementary data. Umeå University. Dataset. https://doi.org/10.17044/scilifelab.23641749.v1

AbbreviationsCC BY 4.0: Creative Commons Attribution 4.0 International Public License

CC BY-NC 4.0: Creative Commons Attribution-NonCommercial 4.0 International Public License

CC BY-NC 3.0: Creative Commons Attribution-NonCommercial 3.0 International Public License

CC BY-NC-ND 4.0: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Public License

DOI: Digital Object Identifier

EBI: European Bioinformatics Institute

EBI-ArrayExpress: The ArrayExpress collection of functional genomics data at the EBI

EBI-ENA: European Nucleotide Archive at the EBI

EBI-Pride: Proteomics Identification Database at the EBI

e!DAL: electronic Data Archive Library at the Leibniz Institute for Plant Genetics and Crop Plant Research

IUID: Item Unique identification

LUDC: Lund University Diabetes Centre

LUDC repository: data repository at the Lund University Diabetes Centre

NCBI: National Center for Biotechnology Information

NCBI-GEO: The Gene Expression Omnibus database repository at the NCBI

NCBI-SRA: The Sequence Read Archive at the NCBI

PMID: Pubmed Identifier

URL: Uniform Resource Locator

MD5 Checksums of the filesManifest.txt (2 KB): 89f32a728fb74ebecef0aef4633130b0

README.txt (6 KB): 34ea4ad9cb9bdea54755fa87f2d0b913

Analysis_SMC_publications_2016_2024_Open_Access_publication_and_access_to_data_status_2025_06_24.csv (46 KB): 9719df26381901bc6aabfd34fdbfab81

Analysis_SMC_publications_2016_2024_Open_Access_publication_and_access_to_data_status_2025_06_24.xlsx (49 KB): 1ec95dc29262645240e7d8714967bcfc

Table_1_Overview_SMC_publications_2016_2024_status_2025_06_11.csv (391 Bytes): 1fd723dc6f52f18251d41c0d343a4f0f

Table_1_Overview_SMC_publications_2016_2024_status_2025_06_11.xlsx (9 KB): 38622a9681c6f1057a6e1a4be56b0285

Figure_1_SMC_publications_2016_2024_open_access_license_and_data_availability_status_2025_06_11.csv (468 Bytes): 9f9156f8d52603ccdec968f626bc002a

Figure_1_SMC_publications_2016_2024_open_access_license_and_data_availability_status_2025_06_11.jpg (119 KB): dc9a4d7de4c789e8aea46ce66e007301

Figure_1_SMC_publications_2016_2024_open_access_license_and_data_availability_status_2025_06_11.xlsx (15 KB): 6527d1ebd0069ef3757bd1b049f0fc74

Figure_2_SMC_publications_2016_2024_metabolomics_data_and_other_data_to_repositories_status_2024_06_12.csv (300 Bytes): 5abc4a0fcf776f8dc4745f41deddacbc

Figure_2_SMC_publications_2016_2024_metabolomics_data_and_other_data_to_repositories_status_2024_06_12.jpg (126 KB): e03e5bf4ba2d942c3b022aebb0a59033

Figure_2_SMC_publications_2016_2024_metabolomics_data_and_other_data_to_repositories_status_2024_06_12.xlsx (15 KB): a80f977c051d4798db221b07733c694b

Figure_3_SMC_publications_2016_2024_overview_supplementary_data_status_2025_06_11.csv (670 Bytes): a694a3defa98aa52fcdec8ff9e9e3316

Figure_3_SMC_publications_2016_2024_overview_supplementary_data_status_2025_06_11.jpg(153 KB): 3928bdc1f046ca9b6f66bdbcdf936ca8

Figure_3_SMC_publications_2016_2024_overview_supplementary_data_status_2025_06_11.xlsx (15 KB): 46dfda56b116b571b4bf8e3674b44512

Figure_4_SMC_publications_2016_2024_submission_of_data_to_repositories_status_2025_06_12.csv (498 Bytes): 8963a412cc9e458ced2e80883bb93e1a

Figure_4_SMC_publications_2016_2024_submission_of_data_to_repositories_status_2025_06_12.jpg (137 KB): c9ba447225e99431f24732128a754b7e

Figure_4_SMC_publications_2016_2024_submission_of_data_to_repositories_status_2025_06_12.xlsx (16 KB): 1e2813d3ccb0ee14991b276947c21b8a

Materials_and_methods_SMC_publications_2016_2024.docx (19 KB): 71776ffc1e530e1b40255763403b2f40

Materials_and_methods_SMC_publications_2016_2024.txt (4 KB): 26c4b91b958b9e33d93d13dc52b25da9

Materials_and_methods_SMC_publications_2026_2024.pdf (172 KB): eee564f452ef4f3cf57bb81a6874fcd4

SMC_publications_2016_2024_status_2025_05_05.csv (143 KB): 5e61d09244ca90b1e5b057a7afdfe5e7

SMC_publications_2016_2024_status_2025_05_05.xlsx (106 KB): 6977fbcac21ff5a12763e40de90c0a91

The eastern black rail (Laterallus jamaicensis jamaicensis; hereafter rail) is a small, cryptic marshbird that was recently listed as threatened under the U.S. Endangered Species Act. We organized a rapid prototyping workshop to initiate development of an adaptive management for rails on the Atlantic Coast. The in-person workshop spanned 2.5 days and was held in Titusville, Florida in January 2020. Workshop participants, comprised of species experts and land managers of rail habitats, chose to focus the framework on testing habitat management techniques to maximize rail occupancy, in which uncertainties could be reduced through a combination of field management experiments and coordinated monitoring. We used the qualitative value of information to prioritize uncertainties (stated as alternative hypotheses developed by participants in habitat-based breakout groups) that could serve as the basis for experiments within the adaptive management framework. Qualitative value of information (QVoI) is a newly-developed decision analysis tool that scores uncertainties in three areas: (1) Magnitude of uncertainty which reflects the strength of theoretical foundation and empirical support of the hypothesized relationship; (2) Relevance to management decisions which indicates how likely the preferred management alternative is to change if the uncertainty were resolved; and (3) Reducibility which is the degree to which the uncertainty could be resolved through research and monitoring. Magnitude is scored on a scale of 0–4, whereas Relevance and Reducibility can vary from 0–3. These data are the anonymized workshop participant (n=26) scores for nine hypotheses focused on testing habitat management techniques, to determine which hypotheses should serve as the basis for management experiments in an adaptive management framework. The data are contained in a .csv file that can be opened using a spreadsheet program such as Microsoft Excel, or read into a statistical analysis program such as Program R.

Excel spreadsheet containing, in separate sheets, the underlying numerical data and statistical analysis for the figures in the main manuscript.

Description:

This dataset comprises a comprehensive set of files designed for the analysis and 2D correlation of spectral data, specifically focusing on ATR and NIR spectra. It includes MATLAB scripts and supporting functions necessary to replicate the analysis, as well as the raw datasets used in the study. Below is a detailed description of the included files:

1. Data Analysis:

   • File Name: Data_Analysis.mlx
   • Description: This MATLAB Live Script file contains the main script used for the classification analysis of the spectral data. It includes steps for preprocessing, analysis, and visualization of the ATR and NIR spectra.

2. 2D Correlation Data Analysis:

   • File Name: Data_Analysis_2Dcorr.mlx
   • Description: This MATLAB Live Script file is similar to the primary analysis script but is specifically tailored for performing 2D correlation analysis on the spectral data. It includes detailed steps and code for executing the 2D correlation.

3. Functions:

   • Folder Name: Functions
   • Description: This folder contains all the necessary MATLAB function files required to replicate the analyses presented in the scripts. These functions handle various preprocessing steps, calculations, and visualizations.

4. Datasets:

   • File Names: ATR_dataset.xlsx, NIR_dataset.xlsx, Reference_data.csv
   • Description: These Excel files contain the raw spectral data for ATR and NIR analyses, as well as reference datasets. Each file includes multiple sheets with detailed measurements and metadata.

Usage Notes:

• Software Requirements:
  • MATLAB is required to run the .mlx files and utilize the functions.
  • PLS_Toolbox: Necessary for certain preprocessing and analysis steps.
  • MIDAS 2010: Available at MIDAS 2010, required for the 2D correlation analysis.
• Replication: Users can replicate the analyses by running the Data_Analysis.mlx and Data_Analysis_2Dcorr.mlx scripts in MATLAB, ensuring that the Functions folder is in the MATLAB path.
• Data Handling: The datasets are provided in .xlsx format, which can be easily imported into MATLAB or other data analysis software.

The excel file contain the raw data in three sheets. THe first sheet contains the overall demographic data we gathered, used to analyse total population size; the second sheet contains the data to analyse srambler arrival day; the third sheet contains the weekly population growth rate. The r-script files import the raw data and contain code to run the analyses and create figures. The fourth sheet in the Excel file contains the metadata on the explanatory and response variables of the analyses.Per capita first day of scrambler expression.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on the first since the experiment started, expressed per total population size (so per capita first scrambler expression), that we first observed a scrambler in a population.First scrambler expression.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on the day since the experiment started that we first observed a scrambler in a population.Population growth.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on population growth quantified as the total population size at week t+1 divided over total population size at week t. .Proportion scramblers.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on the proportion of males that are scramblers.Total population size.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on total population size. Adult population size.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on total adult number. Fecundity.R imports the raw data from Raw data.xlsx to analyse treatment effects of the experiment on the number of eggs produced per female.Testing different time periods.R checks to see if testing for time effects using (1) Week as continuous factor, (2) total time period binned in 7 time intervals and (3), total time period binned in 4 time intervals had any qualitatively different result on scrambler expression. There were no qualitative differences.Number of fighters and scramblers plotted over time.R plots the mean number of fighters and scramblers over time for each treatment combination of environmental quality and founder population size.

The "Ultimate Data Science Interview Q&A Treasury" dataset is a meticulously curated collection designed to empower aspiring data scientists with the knowledge and insights needed to excel in the competitive field of data science. Whether you're a beginner seeking to ground your foundations or an experienced professional aiming to brush up on the latest trends, this treasury serves as an indispensable guide. Furthermore, you might want to work on the following exercises using this dataset :

1)Keyword Analysis for Trending Topics: Frequency Analysis: Identify the most common keywords or terms that appear in the questions to spot trending topics or skills. 2)Topic Modeling: Use algorithms like Latent Dirichlet Allocation (LDA) or Non-negative Matrix Factorization (NMF) to group questions into topics automatically. This can reveal the underlying themes or areas of focus in data science interviews. 3)Text Difficulty Level Analysis: Implement Natural Language Processing (NLP) techniques to evaluate the complexity of questions and answers. This could help in categorizing them into beginner, intermediate, and advanced levels. 4)Clustering for Unsupervised Learning: Apply clustering techniques to group similar questions or answers together. This could help identify unique question patterns or common answer structures. 5)Automated Question Generation: Train a model to generate new interview questions based on the patterns and topics discovered in the dataset. This could be a valuable tool for creating mock interviews or study guides.

Excel spreadsheet containing, in separate sheets, the underlying numerical data for Figs 1A–1D, 2A–2E, 3A, 3C–3F, 4A, 4C, 4E, 4F, 5A–5E, 5G, 6C–6E, 6G, 6H, 7B–7E, 8B, 8C and 8E–8G and S1A, S1B, S2, S3, S4, S5A–S5C, S6A–S6C, S7A, S7B, S8, S9A, S9C, S9D, S9F, S10B, S10C, S11A, S11C, S12D, S12E, S13 and S14A–S14D.

Data for meta-analysis derived from studies comparing timber harvest or tree size to nest site occupancy or productivity in northern goshawks (Accipiter gentilis)These are data derived from published literature for a meta-analysis assessing the degree to which timber harvest and tree size explain productivity and site occupancy in northern goshawks (Accipiter gentilis)

We present these data in two alternative formats. (A) An Excel spreadsheet with multiple tabs. This spread sheet includes many comments linked to individual cells explaining the derivation of individual values. (B) A series of .csv files, each corresponding to a different tab in the Excel file. These are the same data as in the Excel sheet, but without the comments linked to individual cells.

The Excel tabs / CSV files are as follows: (1) repro_raw These are the studies that compared productivity (mostly number of fledged young per pair or per nest) to either timber harvest or tree size. We report the individual effe...

The uploaded Excel files contain the raw data for the single-molecule tweezer experiments in the paper. The raw data represent those from individual molecules in each experimental condition. Refer to Method section of the paper for detailed data analysis.

The dataset includes one master Excel spreadsheet containing literatures searched, catalogued and summarised in the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology, which contains 770 selected texts. The aims of the data collected are to produce an integrated theory that builds on the findings of different disciplines (Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology) focused on the understanding of factors and processes (from the macro social level to the social and psychological level), within the different life contexts, that promote or hinder youth active citizenship in EU. It is possible that similar databases of literature around Europe, Young People and Active Citizenship across the fields of Cultural Studies, Education, History, Media and Communication, Philosophy, Political Science, Psychology and Sociology exist in other forms, perhaps collected for studies on one or more of the included disciplines, but we do not currently have access to a similar repository. With that said, it is highly unlikely that an exact dataset corresponding to the specifics of this study exist in any form elsewhere, thus justifying the creation of new data for this study in the absence of suitable existing data. Data collected here will bridge the gap between global aggregated literatures on youth and citizenship separated by discipline on the one hand, and a new dataset offering an integrated literature analysis of different fields of study. The data sources are available in bibliographic format and attached via Excel document. The dataset relies on the following information taken from the data sources: specific identifying information about the text itself (title/author/year/publisher); abstract or summarizing information either taken directly from the text or summarized by the researcher; and keywords either taken directly from the text or summarized by the researcher. Finally, the aggregated literature review spreadsheet constitutes raw data which can be reused by researchers who want to compare our data with similar data collected in different countries, or to perform textual analysis (content analysis and/or data mining) on our data.